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(hy345@exeter.ac.uk)"}}}} \ No newline at end of file diff --git a/examples-v2/aspect_opinion_sentiment_category_extraction/multitask_train.py b/examples-v2/aspect_opinion_sentiment_category_extraction/multitask_train.py index 6863a55c7..3d4810213 100644 --- a/examples-v2/aspect_opinion_sentiment_category_extraction/multitask_train.py +++ b/examples-v2/aspect_opinion_sentiment_category_extraction/multitask_train.py @@ -11,6 +11,7 @@ import findfile from pyabsa import ABSAInstruction as absa_instruction + warnings.filterwarnings("ignore") import pandas as pd @@ -35,8 +36,8 @@ # id_test_file_path = './integrated_datasets' # id_train_file_path = "./integrated_datasets/acos_datasets/" # id_test_file_path = "./integrated_datasets/acos_datasets" -id_train_file_path = './integrated_datasets/acos_datasets/501.Laptop14' -id_test_file_path = './integrated_datasets/acos_datasets/501.Laptop14' +id_train_file_path = "./integrated_datasets/acos_datasets/501.Laptop14" +id_test_file_path = "./integrated_datasets/acos_datasets/501.Laptop14" # id_train_file_path = './integrated_datasets/acos_datasets/504.Restaurant16' # id_test_file_path = './integrated_datasets/acos_datasets/504.Restaurant16' diff --git a/examples-v2/aspect_polarity_classification/ensemble_inference.py b/examples-v2/aspect_polarity_classification/ensemble_inference.py index 921820de7..96a3172e9 100644 --- a/examples-v2/aspect_polarity_classification/ensemble_inference.py +++ b/examples-v2/aspect_polarity_classification/ensemble_inference.py @@ -44,7 +44,7 @@ def ensemble_performance(dataset, print_result=False): APC.APCDatasetList.Restaurant14, APC.APCDatasetList.Restaurant15, APC.APCDatasetList.Restaurant16, - APC.APCDatasetList.MAMS + APC.APCDatasetList.MAMS, ]: # Training pass diff --git a/examples-v2/aspect_polarity_classification/train_apc.py b/examples-v2/aspect_polarity_classification/train_apc.py index 11b0b53ad..a4f1c803d 100644 --- a/examples-v2/aspect_polarity_classification/train_apc.py +++ b/examples-v2/aspect_polarity_classification/train_apc.py @@ -31,23 +31,25 @@ APC.APCDatasetList.MAMS, ]: for model in [ - # APC.APCModelList.FAST_LSA_T_V2, + APC.APCModelList.FAST_LSA_T_V2, # APC.APCModelList.FAST_LSA_S_V2, - APC.APCModelList.BERT_SPC_V2, + # APC.APCModelList.BERT_SPC_V2, # APC.APCModelList.BERT_SPC ]: for pretrained_bert in [ # "microsoft/deberta-v3-base", - "bert-base-uncased", + # "bert-base-uncased", # 'roberta-base', # 'microsoft/deberta-v3-large', + "microsoft/deberta-v2-xlarge", ]: config = APC.APCConfigManager.get_apc_config_english() config.model = model config.pretrained_bert = pretrained_bert # config.pretrained_bert = 'roberta-base' config.evaluate_begin = 0 - config.max_seq_len = 80 + config.batch_size = 8 + config.max_seq_len = 70 config.num_epoch = 30 # config.log_step = 5 config.log_step = -1 @@ -59,9 +61,9 @@ config.cache_dataset = False config.l2reg = 1e-8 config.learning_rate = 2e-5 - config.use_amp = False config.use_bert_spc = True config.lsa = True + # config.use_amp = True config.use_torch_compile = False config.seed = [random.randint(0, 10000) for _ in range(3)] diff --git a/examples-v2/aspect_term_extraction/checkpoints.json b/examples-v2/aspect_term_extraction/checkpoints.json new file mode 100644 index 000000000..81906865c --- /dev/null +++ b/examples-v2/aspect_term_extraction/checkpoints.json @@ -0,0 +1 @@ +{"2.3.0": {"APC": {"multilingual": {"id": "", "Training Model": "FAST-LCF-BERT-Deberta", "Training Dataset": "APCDatasetList.Multilingual", "Language": "Multilingual", "Description": "Trained on RTX3090", "Available Version": "2.3.0+", "Checkpoint File": "fast_lcf_bert_Multilingual_acc_87.28_f1_81.33.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "english": {"id": "", "Training Model": "FAST-LSA-T-V2-Deberta", "Training Dataset": "APCDatasetList.English", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "2.3.0+", "Checkpoint File": "fast_lcf_bert_English_acc_84.65_f1_82.39.zip", 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{"multilingual": {"id": "", "Training Model": "DeBERTa-v3-Base", "Training Dataset": "SemEval + Synthetic + Chinese_Zhang datasets", "Language": "Multilingual", "Description": "Trained on RTX3090", "Available Version": "2.1.8+", "Checkpoint File": "multilingual-acos.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}, "UPPERTASKCODE": {"promise": {"id": "", "Training Model": "CodeT5-small", "Training Dataset": "DatasetName", "Language": "", "Description": "Trained on RTX3090", "Available Version": "1.16.0+", "Checkpoint File": "lstm_degrad_acc_85.26_f1_84.62.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}}} \ No newline at end of file diff --git a/examples-v2/aspect_term_extraction/extract_aspects.py b/examples-v2/aspect_term_extraction/extract_aspects.py index 6464931e7..8c4f60a4d 100644 --- a/examples-v2/aspect_term_extraction/extract_aspects.py +++ b/examples-v2/aspect_term_extraction/extract_aspects.py @@ -23,14 +23,16 @@ # aspect_extractor = ATEPC.AspectExtractor('english', auto_device=DeviceTypeOption.AUTO) # aspect_extractor = ATEPC.AspectExtractor('chinese', auto_device=DeviceTypeOption.AUTO) -inference_source = ATEPC.ATEPCDatasetList.Multilingual +# inference_source = ATEPC.ATEPCDatasetList.Multilingual +inference_source = ATEPC.ATEPCDatasetList.Laptop14 atepc_result = aspect_extractor.batch_predict( inference_source, # save_result=False, print_result=True, # print the result - pred_sentiment=True, # Predict the sentiment of extracted aspect terms + # pred_sentiment=True, # Predict the sentiment of extracted aspect terms + pred_sentiment=False, # Predict the sentiment of extracted aspect terms eval_batch_size=32, ) - -while True: - aspect_extractor.predict(input("Please input a sentence: ")) +print(atepc_result) +# while True: +# aspect_extractor.predict(input("Please input a sentence: ")) diff --git a/examples-v2/aspect_term_extraction/train_atepc.py b/examples-v2/aspect_term_extraction/train_atepc.py index caa256482..8c62f00d8 100644 --- a/examples-v2/aspect_term_extraction/train_atepc.py +++ b/examples-v2/aspect_term_extraction/train_atepc.py @@ -17,7 +17,7 @@ config.evaluate_begin = 0 config.max_seq_len = 128 config.batch_size = 16 -config.pretrained_bert = 'yangheng/deberta-v3-base-absa' +config.pretrained_bert = "yangheng/deberta-v3-base-absa" # config.pretrained_bert = "microsoft/mdeberta-v3-base" config.log_step = -1 config.l2reg = 1e-8 @@ -28,12 +28,13 @@ config.cache_dataset = True config.cross_validate_fold = -1 -chinese_sets = ATEPC.ATEPCDatasetList.Chinese_Zhang +chinese_sets = ATEPC.ATEPCDatasetList.Laptop14 +# chinese_sets = ATEPC.ATEPCDatasetList.Chinese_Zhang # chinese_sets = ATEPC.ATEPCDatasetList.Multilingual aspect_extractor = ATEPC.ATEPCTrainer( config=config, - from_checkpoint="english", # not necessary for most situations + # from_checkpoint="english", # not necessary for most situations dataset=chinese_sets, checkpoint_save_mode=1, auto_device=True, diff --git a/examples-v2/universal_sentiment_analysis/checkpoints.json b/examples-v2/universal_sentiment_analysis/checkpoints.json new file mode 100644 index 000000000..81906865c --- /dev/null +++ b/examples-v2/universal_sentiment_analysis/checkpoints.json @@ -0,0 +1 @@ +{"2.3.0": {"APC": {"multilingual": {"id": "", "Training Model": "FAST-LCF-BERT-Deberta", "Training Dataset": "APCDatasetList.Multilingual", "Language": "Multilingual", "Description": "Trained on RTX3090", "Available Version": "2.3.0+", "Checkpoint File": "fast_lcf_bert_Multilingual_acc_87.28_f1_81.33.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "english": {"id": "", "Training Model": "FAST-LSA-T-V2-Deberta", "Training Dataset": "APCDatasetList.English", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "2.3.0+", "Checkpoint File": "fast_lcf_bert_English_acc_84.65_f1_82.39.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "chinese": {"id": "", "Training Model": "FAST-LSA-T-V2-Deberta", "Training 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RTX3090", "Available Version": "1.10.5+", "Checkpoint File": "fast_lcf_atepc_Chinese_cdw_apcacc_96.22_apcf1_95.32_atef1_78.73.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}, "RNAC": {"degrad_lstm": {"id": "", "Training Model": "LSTM", "Training Dataset": "ABSADatasets.Multilingual", "Language": "RNA", "Description": "Trained on RTX3090", "Available Version": "1.16.0+", "Checkpoint File": "lstm_degrad_acc_85.26_f1_84.62.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "degrad_bert": {"id": "", "Training Model": "MLP", "Training Dataset": "Degrad", "Language": "RNA", "Description": "Trained on RTX3090", "Available Version": "1.16.0+", "Checkpoint File": "bert_mlp_degrad_acc_87.44_f1_86.99.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}, "TAD": {"tad-sst2": {"id": "", "Training Model": "TAD", "Training Dataset": "SST2", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "1.15+", "Checkpoint File": "TAD-SST2.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "tad-agnews10k": {"id": "", "Training Model": "TAD", "Training Dataset": "AGNews", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "1.15+", "Checkpoint File": "TAD-AGNews10K.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "tad-amazon": {"id": "", "Training Model": "TAD", "Training Dataset": "AGNews", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "1.15+", "Checkpoint File": "TAD-Amazon.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}, "CDD": {"promise": {"id": "", "Training Model": "CodeT5-small", "Training Dataset": "Promise", "Language": "Code", "Description": "Trained on RTX3090", "Available Version": "1.16.0+", "Checkpoint File": "bert_mlp_all_cpdp_acc_75.33_f1_73.52.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}, "ASTE": {"english1": {"id": "", "Training Model": "DeBERTa-v3-Base", "Training Dataset": "SemEval", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "2.1.1+", "Checkpoint File": "EMCGCN_SemEval_f1_74.01.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "english": {"id": "", "Training Model": "DeBERTa-v3-Base", "Training Dataset": "SemEval", "Language": "English", "Description": "Trained on RTX3090", "Available Version": "2.1.1+", "Checkpoint File": "ASTE-EMCGCN_SemEval_f1_74.71.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}, "multilingual": {"id": "", "Training Model": "DeBERTa-v3-Base", "Training Dataset": "SemEval + Synthetic + Chinese_Zhang datasets", "Language": "Multilingual", "Description": "Trained on RTX3090", "Available Version": "2.1.1+", "Checkpoint File": "EMCGCN-Multilingual-f1_51.95.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}, "ACOS": {"multilingual": {"id": "", "Training Model": "DeBERTa-v3-Base", "Training Dataset": "SemEval + Synthetic + Chinese_Zhang datasets", "Language": "Multilingual", "Description": "Trained on RTX3090", "Available Version": "2.1.8+", "Checkpoint File": "multilingual-acos.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}, "UPPERTASKCODE": {"promise": {"id": "", "Training Model": "CodeT5-small", "Training Dataset": "DatasetName", "Language": "", "Description": "Trained on RTX3090", "Available Version": "1.16.0+", "Checkpoint File": "lstm_degrad_acc_85.26_f1_84.62.zip", "Author": "H, Yang (hy345@exeter.ac.uk)"}}}} \ No newline at end of file diff --git a/examples-v2/universal_sentiment_analysis/inference.py b/examples-v2/universal_sentiment_analysis/inference.py new file mode 100644 index 000000000..78b892ade --- /dev/null +++ b/examples-v2/universal_sentiment_analysis/inference.py @@ -0,0 +1,25 @@ +# -*- coding: utf-8 -*- +# file: inference.py +# time: 23:18 06/12/2023 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. + +from pyabsa import UniversalSentimentAnalysis as USA + +usa_predictor = USA.USAPredictor(checkpoint="checkpoints") + +examples = [ + '{"text": "keyboard key fragile .", "labels": [{"aspect": "keyboard", "opinion": "fragile", "polarity": "negative", "category": "KEYBOARD#QUALITY"}]}' +] + +inference_results = usa_predictor.predict(examples) + +print(inference_results) + +while True: + text = input("Please input your text: ") + inference_results = usa_predictor.predict(text) + print(inference_results) diff --git a/examples-v2/universal_sentiment_analysis/train.py b/examples-v2/universal_sentiment_analysis/train.py new file mode 100644 index 000000000..37916b66c --- /dev/null +++ b/examples-v2/universal_sentiment_analysis/train.py @@ -0,0 +1,44 @@ +# -*- coding: utf-8 -*- +# file: train.py +# time: 11:30 2023/3/13 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. +import os +import warnings + +from pyabsa.tasks import UniversalSentimentAnalysis as USA + +warnings.filterwarnings("ignore") + +config = USA.USAConfigManager.get_usa_config_chinese() +config.model = USA.USAModelList.GenerationModel +config.evaluate_begin = 0 +config.max_seq_len = 256 +config.batch_size = 8 +config.pretrained_bert = "google/flan-t5-base" +# config.pretrained_bert = 'allenai/tk-instruct-base-def-pos' +config.log_step = -1 +config.l2reg = 1e-8 +config.num_epoch = 20 +config.seed = 42 +config.use_bert_spc = True +config.use_amp = False +config.cache_dataset = False +config.cross_validate_fold = -1 + +# chinese_sets = USA.USADatasetList.Laptop14 +chinese_sets = USA.USADatasetList.Multilingual + +usa_model = USA.USATrainer( + config=config, + dataset=chinese_sets, + checkpoint_save_mode=1, + auto_device=True, + load_aug=False, +).load_trained_model() + +outputs = usa_model.model.evaluate() +print(outputs) diff --git a/examples-v2/universal_sentiment_analysis/usa_dataset.json b/examples-v2/universal_sentiment_analysis/usa_dataset.json new file mode 100644 index 000000000..89ac16b95 --- /dev/null +++ b/examples-v2/universal_sentiment_analysis/usa_dataset.json @@ -0,0 +1,31677 @@ +[ + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this originally a few months back , died within a week .\n->i bought this originally a few months back , died within a week .\n[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n->From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n[{'aspect': 'beginning appetizers', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'scallops', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chocolate souffle with rasberry mint sorbet', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'taste', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Judging from previous posts this used to be a good place , but not any longer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nJudging from previous posts this used to be a good place , but not any longer .\n->", + "output": "{\"text\": \"Judging from previous posts this used to be a good place , but not any longer .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While this can hardly be called a restaurant , it is possibly the best deal in Manhatten : $ 4 for a plate heaped with rice and 2-3 entrees .\n->While this can hardly be called a restaurant , it is possibly the best deal in Manhatten : $ 4 for a plate heaped with rice and 2-3 entrees .\n[{'aspect': 'rice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The appetizers are just OK and the main courses were decidedly subpar .\n->The appetizers are just OK and the main courses were decidedly subpar .\n[{'aspect': 'appetizers', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'main courses', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\ntext: We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->", + "output": "{\"text\": \"We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keys feel fantastic to type on .\n->the keys feel fantastic to type on .\n[{'aspect': 'keys', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: They might be all business at the counter when you give your order , but their food says I love you .\n->They might be all business at the counter when you give your order , but their food says I love you .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'counter', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The food was lousy - too sweet or too salty and the portions tiny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was lousy - too sweet or too salty and the portions tiny .\n->", + "output": "{\"text\": \"The food was lousy - too sweet or too salty and the portions tiny .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had the best ravioli ever .\n->I had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Grilled whole fish wonderful , great spicing .\n->Grilled whole fish wonderful , great spicing .\n[{'aspect': 'fish', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Avoid this place !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAvoid this place !\n->", + "output": "{\"text\": \"Avoid this place !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'Avoid', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re coming from a pc you ' ll love the battery .\n->if you ' re coming from a pc you ' ll love the battery .\n[{'aspect': 'battery', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I have eaten at Saul , many times , the food is always consistently , outrageously good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have eaten at Saul , many times , the food is always consistently , outrageously good .\n->", + "output": "{\"text\": \"I have eaten at Saul , many times , the food is always consistently , outrageously good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: within two days of receiving this item a line appeared on my screen ( while i was using it , previously fine ) and below it the image / screen flickered .\n->within two days of receiving this item a line appeared on my screen ( while i was using it , previously fine ) and below it the image / screen flickered .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n->I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n[{'aspect': 'pastrami on challah sandwich', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Saul is the best restaurant on Smith Street and in Brooklyn .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSaul is the best restaurant on Smith Street and in Brooklyn .\n->", + "output": "{\"text\": \"Saul is the best restaurant on Smith Street and in Brooklyn .\", \"labels\": \"[{'aspect': 'Saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n->extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n[{'aspect': 'seller', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: The duck confit is always amazing and the foie gras terrine with figs was out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe duck confit is always amazing and the foie gras terrine with figs was out of this world .\n->", + "output": "{\"text\": \"The duck confit is always amazing and the foie gras terrine with figs was out of this world .\", \"labels\": \"[{'aspect': 'foie gras terrine with figs', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck confit', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: went here last night - nice decor , good service , but the food was surprisingly excellent .\n->went here last night - nice decor , good service , but the food was surprisingly excellent .\n[{'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: everything we had was good or ok . . . . but definitely nothing great .\n->everything we had was good or ok . . . . but definitely nothing great .\n[{'aspect': 'NULL', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: The wine list is interesting and has many good values .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is interesting and has many good values .\n->", + "output": "{\"text\": \"The wine list is interesting and has many good values .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'good values', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: was surprisingly disappointed .\n->was surprisingly disappointed .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: delicious bagels , especially when right out of the oven .\n->delicious bagels , especially when right out of the oven .\n[{'aspect': 'bagels', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I was very disappointed with this restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI was very disappointed with this restaurant .\n->", + "output": "{\"text\": \"I was very disappointed with this restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n->i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the key board and mouse pad are not very sensitive .\n->the key board and mouse pad are not very sensitive .\n[{'aspect': 'key board', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mouse pad', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: Food was okay , nothing great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was okay , nothing great .\n->", + "output": "{\"text\": \"Food was okay , nothing great .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n->this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n[{'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'samsung chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n->this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n[{'aspect': 'chromebook', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'boot up', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nChow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n->", + "output": "{\"text\": \"Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\", \"labels\": \"[{'aspect': 'Chow fun', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pork shu mai', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are often crowded on the weekends but they are efficient and accurate with their service .\n->They are often crowded on the weekends but they are efficient and accurate with their service .\n[{'aspect': 'service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crowded', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: - love the trackpad .\n->- love the trackpad .\n[{'aspect': 'trackpad', 'opinion': 'trackpad', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: I/we will never go back to this place again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI/we will never go back to this place again .\n->", + "output": "{\"text\": \"I/we will never go back to this place again .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'never go back', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dancing , white river and millenium rolls are musts .\n->the dancing , white river and millenium rolls are musts .\n[{'aspect': 'dancing , white river and millenium rolls', 'opinion': 'musts', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: every app i have downloaded from the google app store has worked perfectly .\n->every app i have downloaded from the google app store has worked perfectly .\n[{'aspect': 'app', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->", + "output": "{\"text\": \"Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: processer is blazing fast ( competes with 7th gen i7 hq line - check cpu benchmark ) .\n->processer is blazing fast ( competes with 7th gen i7 hq line - check cpu benchmark ) .\n[{'aspect': 'processer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: The selection changes frequently but the basic dishes are always available .\n->The selection changes frequently but the basic dishes are always available .\n[{'aspect': 'selection', 'opinion': 'changes frequently', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'basic dishes', 'opinion': 'available', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEverything is always cooked to perfection , the service is excellent , the decor cool and understated .\n->", + "output": "{\"text\": \"Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'understated', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n->i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n[{'aspect': 'waiter', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n->The people that work there are always so friendly you forget you are in New York sometimes .\n[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I had the duck breast special on my last visit and it was incredible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had the duck breast special on my last visit and it was incredible .\n->", + "output": "{\"text\": \"I had the duck breast special on my last visit and it was incredible .\", \"labels\": \"[{'aspect': 'duck breast special', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a beautifully designed dreamy egyptian restaurant that gets sceney at night .\n->a beautifully designed dreamy egyptian restaurant that gets sceney at night .\n[{'aspect': 'egyptian restaurant', 'opinion': 'dreamy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'egyptian restaurant', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n->The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n[{'aspect': 'miso soup', 'opinion': 'lacked flavor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'unfortunately', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The only thing I moderately enjoyed was their Grilled Chicken special with Edamame Puree .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only thing I moderately enjoyed was their Grilled Chicken special with Edamame Puree .\n->", + "output": "{\"text\": \"The only thing I moderately enjoyed was their Grilled Chicken special with Edamame Puree .\", \"labels\": \"[{'aspect': 'Grilled Chicken special with Edamame Puree', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n->my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n[{'aspect': 'place', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: - port minimalism .\n->- port minimalism .\n[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#PORTABILITY'}]\ntext: I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n->", + "output": "{\"text\": \"I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\", \"labels\": \"[{'aspect': 'Edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is exactly what i needed in a laptop .\n->this is exactly what i needed in a laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n->however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n->", + "output": "{\"text\": \"Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\", \"labels\": \"[{'aspect': 'sake list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is extensive and impressive .\n->The wine list is extensive and impressive .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what ' s more , the price was perfect as a small investment into my small business .\n->what ' s more , the price was perfect as a small investment into my small business .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n->", + "output": "{\"text\": \"We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a very fast laptop .\n->it ' s a very fast laptop .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i don ' t understand this kind of design .\n->i don ' t understand this kind of design .\n[{'aspect': 'design', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Food awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood awesome .\n->", + "output": "{\"text\": \"Food awesome .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use this for work , school , and to just watch videos or read books !\n->i use this for work , school , and to just watch videos or read books !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Service friendly and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService friendly and attentive .\n->", + "output": "{\"text\": \"Service friendly and attentive .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the rice was poor quality and was cooked so badly it was hard .\n->the rice was poor quality and was cooked so badly it was hard .\n[{'aspect': 'rice', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rice', 'opinion': 'cooked so badly', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rice', 'opinion': 'hard', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: loved it\n->loved it\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the food is decent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is decent .\n->", + "output": "{\"text\": \"the food is decent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: despite these cons i think this was a great purchase .\n->despite these cons i think this was a great purchase .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'cons', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: This place has got to be the best japanese restaurant in the new york area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place has got to be the best japanese restaurant in the new york area .\n->", + "output": "{\"text\": \"This place has got to be the best japanese restaurant in the new york area .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ' ve tried before but it always packed and does n ' t take reservations .\n->we ' ve tried before but it always packed and does n ' t take reservations .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: its battery life is really good , and the led lights are nice .\n->its battery life is really good , and the led lights are nice .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'led lights', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: Food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is great .\n->", + "output": "{\"text\": \"Food is great .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: took my mom for mother ' s day , and the maitre d ' was pretty rude .\n->took my mom for mother ' s day , and the maitre d ' was pretty rude .\n[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: my biggest gripe with this that makes me incredibly frustrated is that google hangouts is unreliable on this .\n->my biggest gripe with this that makes me incredibly frustrated is that google hangouts is unreliable on this .\n[{'aspect': 'google hangouts', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google hangouts', 'opinion': 'frustrated', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Service is top notch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is top notch .\n->", + "output": "{\"text\": \"Service is top notch .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n->i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: this laptop is beautiful , and that ' s about it .\n->this laptop is beautiful , and that ' s about it .\n[{'aspect': 'laptop', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe took advanatage of the half price sushi deal on saturday so it was well worth it .\n->", + "output": "{\"text\": \"We took advanatage of the half price sushi deal on saturday so it was well worth it .\", \"labels\": \"[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I found the food , service and value exceptional everytime I have been there .\n->I found the food , service and value exceptional everytime I have been there .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n->Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n[{'aspect': 'Thai food', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: In the evening , this place attracted a well dressed , with it , NY crowd .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn the evening , this place attracted a well dressed , with it , NY crowd .\n->", + "output": "{\"text\": \"In the evening , this place attracted a well dressed , with it , NY crowd .\", \"labels\": \"[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the people who want great food plus great service , Roxy is a place to AVOID !\n->For the people who want great food plus great service , Roxy is a place to AVOID !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n->It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n[{'aspect': 'food', 'opinion': 'surprisingly fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n$ 6 and there is much tasty food , all of it fresh and continually refilled .\n->", + "output": "{\"text\": \"$ 6 and there is much tasty food , all of it fresh and continually refilled .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop ' s construction is cheap and flimsy , the battery is not removable and the back case is nearly impossible to take off without damaging it .\n->this laptop ' s construction is cheap and flimsy , the battery is not removable and the back case is nearly impossible to take off without damaging it .\n[{'aspect': \"laptop ' s construction\", 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': \"laptop ' s construction\", 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'not removable', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}, {'aspect': 'back case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the broken mic was not a dealbreaker but annoying in a brand new device .\n->the broken mic was not a dealbreaker but annoying in a brand new device .\n[{'aspect': 'mic', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: I am not a vegetarian but , almost all the dishes were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI am not a vegetarian but , almost all the dishes were great .\n->", + "output": "{\"text\": \"I am not a vegetarian but , almost all the dishes were great .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wifi was pretty bad .\n->wifi was pretty bad .\n[{'aspect': 'wifi was', 'opinion': 'bad', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\nExample:\ntext: great machine for all my needs .\n->great machine for all my needs .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The food here is rather good , but only if you like to wait for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food here is rather good , but only if you like to wait for it .\n->", + "output": "{\"text\": \"The food here is rather good , but only if you like to wait for it .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had a great time at Jekyll and Hyde !\n->I had a great time at Jekyll and Hyde !\n[{'aspect': 'Jekyll and Hyde', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: laptop was working fine until just under 3 months of use when it bsod ' d and wouldn ' t turn back on .\n->laptop was working fine until just under 3 months of use when it bsod ' d and wouldn ' t turn back on .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\n->", + "output": "{\"text\": \"I like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\", \"labels\": \"[{'aspect': 'somosas', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chai', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chole', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dhosas', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dhal', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: guaranteed excellent customer service !\n->guaranteed excellent customer service !\n[{'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: battery lasts me all day , it ' s big screen is easy on the eyes .\n->battery lasts me all day , it ' s big screen is easy on the eyes .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: The service varys from day to day- sometimes they 're very nice , and sometimes not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service varys from day to day- sometimes they 're very nice , and sometimes not .\n->", + "output": "{\"text\": \"The service varys from day to day- sometimes they 're very nice , and sometimes not .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'varys', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My goodness , everything from the fish to the rice to the seaweed was absolutely amazing .\n->My goodness , everything from the fish to the rice to the seaweed was absolutely amazing .\n[{'aspect': 'fish', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seaweed', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after dinner the manager grabbed my boyfriend , asked him : where are you from . . . maybe you dont know how things work in america . . . and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .\n->after dinner the manager grabbed my boyfriend , asked him : where are you from . . . maybe you dont know how things work in america . . . and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .\n[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Also , specify if you like your food spicy- its rather bland if you do n't .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlso , specify if you like your food spicy- its rather bland if you do n't .\n->", + "output": "{\"text\": \"Also , specify if you like your food spicy- its rather bland if you do n't .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n->the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n[{'aspect': 'battery life', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'longevity', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the m3 processor is fast and fluid .\n->the m3 processor is fast and fluid .\n[{'aspect': 'm3 processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'm3 processor', 'opinion': 'fluid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->", + "output": "{\"text\": \"The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Delicious crab cakes too .\n->Delicious crab cakes too .\n[{'aspect': 'crab cakes', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i just received this product about an hour or so ago .\n->i just received this product about an hour or so ago .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: The lava cake dessert was incredible and I recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe lava cake dessert was incredible and I recommend it .\n->", + "output": "{\"text\": \"The lava cake dessert was incredible and I recommend it .\", \"labels\": \"[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was average or above including some surprising tasty dishes .\n->the food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n->today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOnce you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\n->", + "output": "{\"text\": \"Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\", \"labels\": \"[{'aspect': 'Cosette', 'opinion': 'off-the-beaten', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i never write on these sites but this restaurant is def worth commending !\n->i never write on these sites but this restaurant is def worth commending !\n[{'aspect': 'restaurant', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the speed at which this charges and reboots is amazing , and the battery life is long .\n->the speed at which this charges and reboots is amazing , and the battery life is long .\n[{'aspect': 'charges', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'reboots', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: This tiny restaurant is as cozy as it gets , with that certain Parisian flair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis tiny restaurant is as cozy as it gets , with that certain Parisian flair .\n->", + "output": "{\"text\": \"This tiny restaurant is as cozy as it gets , with that certain Parisian flair .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: that machine couldn ' t handle all the tabs i wanted to keep open , so after much research , i jumped into this acer .\n->that machine couldn ' t handle all the tabs i wanted to keep open , so after much research , i jumped into this acer .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: I have eaten at Saul , many times , the food is always consistently , outrageously good .\n->I have eaten at Saul , many times , the food is always consistently , outrageously good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The pizza was delivered cold and the cheese was n't even fully melted !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza was delivered cold and the cheese was n't even fully melted !\n->", + "output": "{\"text\": \"The pizza was delivered cold and the cheese was n't even fully melted !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': \"was n't even fully melted\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The server was really cool and served us our food and drinks with a smile .\n->The server was really cool and served us our food and drinks with a smile .\n[{'aspect': 'server', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food was good , the place was clean and affordable .\n->the food was good , the place was clean and affordable .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: The pizza is overpriced and soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza is overpriced and soggy .\n->", + "output": "{\"text\": \"The pizza is overpriced and soggy .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mizu is home to creative and unique rolls not to found anywhere else .\n->mizu is home to creative and unique rolls not to found anywhere else .\n[{'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: this is by far my favorite place in the neighborhood .\n->this is by far my favorite place in the neighborhood .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\n->", + "output": "{\"text\": \"Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are often crowded on the weekends but they are efficient and accurate with their service .\n->They are often crowded on the weekends but they are efficient and accurate with their service .\n[{'aspect': 'service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crowded', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: I had the Pad Thai and the noodles were sticky .\n->I had the Pad Thai and the noodles were sticky .\n[{'aspect': 'Pad Thai', 'opinion': 'sticky', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'noodles', 'opinion': 'sticky', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I think I 've had some the best meals of my life at minnow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI think I 've had some the best meals of my life at minnow .\n->", + "output": "{\"text\": \"I think I 've had some the best meals of my life at minnow .\", \"labels\": \"[{'aspect': 'meals', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n->the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n[{'aspect': '1080p screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': '1080p screen', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'hinge', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i still had some of those kinds of mishaps with the chromebook - - but not nearly as many .\n->i still had some of those kinds of mishaps with the chromebook - - but not nearly as many .\n[{'aspect': 'chromebook', 'opinion': 'mishaps', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n->", + "output": "{\"text\": \"The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'ever-changing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'great surprises', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got this for my mother in law and she could not be happier with how it works .\n->i got this for my mother in law and she could not be happier with how it works .\n[{'aspect': 'NULL', 'opinion': 'not be happier', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n->the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n[{'aspect': 'apps', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google docs / drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: The combination of super-fresh ingredients in the dishes are unusual but really delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe combination of super-fresh ingredients in the dishes are unusual but really delicious .\n->", + "output": "{\"text\": \"The combination of super-fresh ingredients in the dishes are unusual but really delicious .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'super-fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n->so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' ve called twice to try to connect it to printer , first call they told me i had to get a cloud ready printer after getting said printer it still doesn ' t connect .\n->i ' ve called twice to try to connect it to printer , first call they told me i had to get a cloud ready printer after getting said printer it still doesn ' t connect .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: Best Pastrami I ever had and great portion without being ridiculous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest Pastrami I ever had and great portion without being ridiculous .\n->", + "output": "{\"text\": \"Best Pastrami I ever had and great portion without being ridiculous .\", \"labels\": \"[{'aspect': 'Pastrami', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a capable chromebook !\n->this is a capable chromebook !\n[{'aspect': 'chromebook', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i am not fond of touchpads anyway , so probably not the best one to judge them .\n->i am not fond of touchpads anyway , so probably not the best one to judge them .\n[{'aspect': 'touchpads', 'opinion': 'not fond of', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: My wife had the fried shrimp which are huge and loved it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy wife had the fried shrimp which are huge and loved it .\n->", + "output": "{\"text\": \"My wife had the fried shrimp which are huge and loved it .\", \"labels\": \"[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great shabu shabu\n->great shabu shabu\n[{'aspect': 'shabu shabu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: power cord broke within the first two weeks of use .\n->power cord broke within the first two weeks of use .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n->", + "output": "{\"text\": \"As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its current price is not worth it .\n->its current price is not worth it .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: for the price , it ' s a solid laptop .\n->for the price , it ' s a solid laptop .\n[{'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The signs , the specials menus , food , and even all the waitstaff are ALL TOTALLY Japanese .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe signs , the specials menus , food , and even all the waitstaff are ALL TOTALLY Japanese .\n->", + "output": "{\"text\": \"The signs , the specials menus , food , and even all the waitstaff are ALL TOTALLY Japanese .\", \"labels\": \"[{'aspect': 'signs', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials menus', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was actually aweful .\n->the food was actually aweful .\n[{'aspect': 'food', 'opinion': 'aweful', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food is authentic Italian - delicious !\n->The food is authentic Italian - delicious !\n[{'aspect': 'food', 'opinion': 'authentic Italian', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place is worth an one-hour drive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is worth an one-hour drive .\n->", + "output": "{\"text\": \"This place is worth an one-hour drive .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had been a regular due to the consistently good food and ease of getting a table .\n->I had been a regular due to the consistently good food and ease of getting a table .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'ease', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: When we sat , we got great and fast service .\n->When we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My wife and I always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy wife and I always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n->", + "output": "{\"text\": \"My wife and I always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'young', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'not always well trained', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after all that , they complained to me about the small tip .\n->after all that , they complained to me about the small tip .\n[{'aspect': 'NULL', 'opinion': 'complained', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The food was delicious but do not come here on a empty stomach .\n->The food was delicious but do not come here on a empty stomach .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Decent wine at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDecent wine at reasonable prices .\n->", + "output": "{\"text\": \"Decent wine at reasonable prices .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n->Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'view of the new york city skiline', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s also worth noting my unit came with a floating trackpad , with initial play before the actually click .\n->it ' s also worth noting my unit came with a floating trackpad , with initial play before the actually click .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: This is by far my favorite place in the neighborhood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is by far my favorite place in the neighborhood .\n->", + "output": "{\"text\": \"This is by far my favorite place in the neighborhood .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n->The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the only real upgrade for the new one , before adding on options is faster memory .\n->the only real upgrade for the new one , before adding on options is faster memory .\n[{'aspect': 'memory', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->", + "output": "{\"text\": \"The service is excellent , the decor is great , and the food is delicious and comes in large portions .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i haven ' t had issues with the track pad as others have .\n->i haven ' t had issues with the track pad as others have .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: my wife upgraded from the 2 gigs of ram version to this , and it seems like there is more than twice the performance .\n->my wife upgraded from the 2 gigs of ram version to this , and it seems like there is more than twice the performance .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\ntext: I 'm partial to the Gnocchi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 'm partial to the Gnocchi .\n->", + "output": "{\"text\": \"I 'm partial to the Gnocchi .\", \"labels\": \"[{'aspect': 'Gnocchi', 'opinion': 'partial', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great hot dogs !\n->great hot dogs !\n[{'aspect': 'hot dogs', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The flavors are very fresh and pretty unobtrusive , nothing flashy .\n->The flavors are very fresh and pretty unobtrusive , nothing flashy .\n[{'aspect': 'flavors', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavors', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place is incredibly tiny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is incredibly tiny .\n->", + "output": "{\"text\": \"This place is incredibly tiny .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent food , although the interior could use some help .\n->excellent food , although the interior could use some help .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'interior', 'opinion': 'help', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: issue summary : frequent crashing\n->issue summary : frequent crashing\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The hostess is rude to the point of being offensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe hostess is rude to the point of being offensive .\n->", + "output": "{\"text\": \"The hostess is rude to the point of being offensive .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I LOVE their Thai\n->I LOVE their Thai\n[{'aspect': 'Thai', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: then it just rebooted without prompt .\n->then it just rebooted without prompt .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: We have been to this place many times , and always have great food , wine , and service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe have been to this place many times , and always have great food , wine , and service .\n->", + "output": "{\"text\": \"We have been to this place many times , and always have great food , wine , and service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was very prompt but slightly rushed .\n->Service was very prompt but slightly rushed .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'rushed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and how many times can you pick up the same perfectly aligned set of napkins , inspect them vapidly and plonk them down in exactly the same place instead of venturing a glance at people who are there to help you make the rent ?\n->and how many times can you pick up the same perfectly aligned set of napkins , inspect them vapidly and plonk them down in exactly the same place instead of venturing a glance at people who are there to help you make the rent ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: We were worried we would have trouble getting in , but somehow managed to have a short wait .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were worried we would have trouble getting in , but somehow managed to have a short wait .\n->", + "output": "{\"text\": \"We were worried we would have trouble getting in , but somehow managed to have a short wait .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food looked very appetizing and delicious since it came on a variety of fancy plates .\n->The food looked very appetizing and delicious since it came on a variety of fancy plates .\n[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'plates', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: then , to top things off , she dropped used silverware on my boyfriend ' s jacket and did not stop to apologize or clean the mess that was left on clothes .\n->then , to top things off , she dropped used silverware on my boyfriend ' s jacket and did not stop to apologize or clean the mess that was left on clothes .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: As always we had a great glass of wine while we waited .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs always we had a great glass of wine while we waited .\n->", + "output": "{\"text\": \"As always we had a great glass of wine while we waited .\", \"labels\": \"[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The cream cheeses are out of this world and I love that coffee ! !\n->The cream cheeses are out of this world and I love that coffee ! !\n[{'aspect': 'cream cheeses', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'coffee', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n->i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: When we sat , we got great and fast service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhen we sat , we got great and fast service .\n->", + "output": "{\"text\": \"When we sat , we got great and fast service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n->From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n[{'aspect': 'food', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: need to play with it a bit more .\n->need to play with it a bit more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe people that work there are always so friendly you forget you are in New York sometimes .\n->", + "output": "{\"text\": \"The people that work there are always so friendly you forget you are in New York sometimes .\", \"labels\": \"[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's simply the best meal in NYC .\n->It 's simply the best meal in NYC .\n[{'aspect': 'meal', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The pizza is delicious and the proprietor is one of the nicest in NYC .\n->The pizza is delicious and the proprietor is one of the nicest in NYC .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Make sure you try this place as often as you can .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMake sure you try this place as often as you can .\n->", + "output": "{\"text\": \"Make sure you try this place as often as you can .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If we were to move from the upper east side , we would genuinely miss this restaurant .\n->If we were to move from the upper east side , we would genuinely miss this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The waiters were not attentive except that the bill turned up on the table before we were finished .\n->The waiters were not attentive except that the bill turned up on the table before we were finished .\n[{'aspect': 'waiters', 'opinion': 'attentive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: This is a fun restaurant to go to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a fun restaurant to go to .\n->", + "output": "{\"text\": \"This is a fun restaurant to go to .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Thius is a must for anyone who loves Shabu-Shabu .\n->Thius is a must for anyone who loves Shabu-Shabu .\n[{'aspect': 'Shabu-Shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if i could give 0 stars i would do so for this place .\n->if i could give 0 stars i would do so for this place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: The pizza is yummy and I like the atmoshpere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza is yummy and I like the atmoshpere .\n->", + "output": "{\"text\": \"The pizza is yummy and I like the atmoshpere .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: over all it was a very nice romantic place .\n->over all it was a very nice romantic place .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n->Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n[{'aspect': 'salmon', 'opinion': 'wasnt impressed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servings for main entree', 'opinion': 'Small', 'polarity': 'negative', 'category': 'NULL'}]\ntext: But the pizza is way to expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the pizza is way to expensive .\n->", + "output": "{\"text\": \"But the pizza is way to expensive .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had the thai style fried sea bass . . . which was very good .\n->i had the thai style fried sea bass . . . which was very good .\n[{'aspect': 'thai style fried sea bass', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n->do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Sauce was watery and the food did n't have much flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSauce was watery and the food did n't have much flavor .\n->", + "output": "{\"text\": \"Sauce was watery and the food did n't have much flavor .\", \"labels\": \"[{'aspect': 'Sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: haru on park s is simply disgusting .\n->haru on park s is simply disgusting .\n[{'aspect': 'haru on park s', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it was nice when it was working .\n->it was nice when it was working .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: This place is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is great .\n->", + "output": "{\"text\": \"This place is great .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the new pro model is very light and compact , and can easily be carried around with you every day .\n->the new pro model is very light and compact , and can easily be carried around with you every day .\n[{'aspect': 'pro model', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'easily be carried', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: this is a great laptop .\n->this is a great laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The waitress was very patient with us and the food is phenomenal !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waitress was very patient with us and the food is phenomenal !\n->", + "output": "{\"text\": \"The waitress was very patient with us and the food is phenomenal !\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'patient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: from the spectacular caviar to the hospitable waitstaff , i felt like royalty and enjoyed every second of it .\n->from the spectacular caviar to the hospitable waitstaff , i felt like royalty and enjoyed every second of it .\n[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Food is excellent .\n->Food is excellent .\n[{'aspect': 'Food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service was prompt , friendly and great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was prompt , friendly and great .\n->", + "output": "{\"text\": \"Service was prompt , friendly and great .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - no audio in port .\n->- no audio in port .\n[{'aspect': 'audio in port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Great pizza and fantastic service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat pizza and fantastic service .\n->", + "output": "{\"text\": \"Great pizza and fantastic service .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the quality is terrible ( both versions ) for such a pricey product .\n->the quality is terrible ( both versions ) for such a pricey product .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'product', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i would recommend this machine to anyone who wants an inexpensive web - content device .\n->i would recommend this machine to anyone who wants an inexpensive web - content device .\n[{'aspect': 'machine', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: There was a small wait , but shorter than I expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere was a small wait , but shorter than I expected .\n->", + "output": "{\"text\": \"There was a small wait , but shorter than I expected .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n->The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n[{'aspect': 'ingredients', 'opinion': 'fresher', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'crispier', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'slice', 'opinion': 'less oily', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n->The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'NULL'}]\ntext: This is the best sushi in new york city - hands down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the best sushi in new york city - hands down .\n->", + "output": "{\"text\": \"This is the best sushi in new york city - hands down .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I must warn the reader that the portions sizes are very small ( especially the appetizers ) , so if you plan to eat until you are full and do not intend to order the chef 's special tasting menu , prepare to order and pay for an appetizer ( 1 dish for each person because the portions are not for sharing ) , a main entree , and the cold udon at the end of the meal .\n->I must warn the reader that the portions sizes are very small ( especially the appetizers ) , so if you plan to eat until you are full and do not intend to order the chef 's special tasting menu , prepare to order and pay for an appetizer ( 1 dish for each person because the portions are not for sharing ) , a main entree , and the cold udon at the end of the meal .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'appetizers', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Simply some good tasting Chinese food at incredible prices ...\n->Simply some good tasting Chinese food at incredible prices ...\n[{'aspect': 'Chinese food', 'opinion': 'good tasting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPlanet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n->", + "output": "{\"text\": \"Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Planet Thailand', 'opinion': 'hit', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: everything was going good until we got our meals .\n->everything was going good until we got our meals .\n[{'aspect': 'meals', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: With the great variety on the menu , I eat here often and never get bored .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWith the great variety on the menu , I eat here often and never get bored .\n->", + "output": "{\"text\": \"With the great variety on the menu , I eat here often and never get bored .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'great variety', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n->i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n[{'aspect': 'mizu', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: this model has a defect .\n->this model has a defect .\n[{'aspect': 'model', 'opinion': 'defect', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The atmosphere is n't the greatest , but I suppose that 's how they keep the prices down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is n't the greatest , but I suppose that 's how they keep the prices down .\n->", + "output": "{\"text\": \"The atmosphere is n't the greatest , but I suppose that 's how they keep the prices down .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': \"is n't the greatest\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n->it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the screen on this looks great , the bezels aren ' t noticeable .\n->the screen on this looks great , the bezels aren ' t noticeable .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': \"' t noticeable\", 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Try the crunchy tuna , it is to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the crunchy tuna , it is to die for .\n->", + "output": "{\"text\": \"Try the crunchy tuna , it is to die for .\", \"labels\": \"[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: big wong gets big ups for a fine establishment .\n->big wong gets big ups for a fine establishment .\n[{'aspect': 'big wong', 'opinion': 'big ups', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'big wong', 'opinion': 'fine', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Warm , comfortable surroundings , nice appointments ( witness the etched glass and brickwork separating the dining rooms ) .\n->Warm , comfortable surroundings , nice appointments ( witness the etched glass and brickwork separating the dining rooms ) .\n[{'aspect': 'surroundings', 'opinion': 'Warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'surroundings', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining rooms', 'opinion': 'nice', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: First went here to enjoy their garden terrace .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFirst went here to enjoy their garden terrace .\n->", + "output": "{\"text\": \"First went here to enjoy their garden terrace .\", \"labels\": \"[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the filet mignon dish was superb !\n->the filet mignon dish was superb !\n[{'aspect': 'filet mignon dish', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it was well worth the wait .\n->it was well worth the wait .\n[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n->", + "output": "{\"text\": \"The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'not over-bearing or rushed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: , as it doesn ' t have large built in memory .\n->, as it doesn ' t have large built in memory .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: food is great and inexpensive .\n->food is great and inexpensive .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Steak Tartare is a great bet , they fix it for you at the table .\n->", + "output": "{\"text\": \"The Steak Tartare is a great bet , they fix it for you at the table .\", \"labels\": \"[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n->The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'not over-bearing or rushed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you ' re going to be doing a lot of heavy lifting , this might not be the chromebook for you .\n->if you ' re going to be doing a lot of heavy lifting , this might not be the chromebook for you .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .\n->", + "output": "{\"text\": \"It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'French food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Baluchi 's has solid food and a nice decor at reasonable prices .\n->Baluchi 's has solid food and a nice decor at reasonable prices .\n[{'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I 've also been amazed at all the new additions in the past few years : A new Jazz Bar , the most fantastic Dining Garden , the Best Thin Crust Pizzas , and now a Lasagna Menu which is to die for ( these are not your average lasagnas ) !\n->I 've also been amazed at all the new additions in the past few years : A new Jazz Bar , the most fantastic Dining Garden , the Best Thin Crust Pizzas , and now a Lasagna Menu which is to die for ( these are not your average lasagnas ) !\n[{'aspect': 'Dining Garden', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Jazz Bar', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thin Crust Pizzas', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Lasagna Menu', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->", + "output": "{\"text\": \"Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\", \"labels\": \"[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n->My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n[{'aspect': 'food', 'opinion': 'opposite', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: never have i had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b / c i live in a walkup ) who always seem disappointed with their tips .\n->never have i had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b / c i live in a walkup ) who always seem disappointed with their tips .\n[{'aspect': 'delivery guys', 'opinion': 'dramatic', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Not sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !\n->", + "output": "{\"text\": \"Not sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !\", \"labels\": \"[{'aspect': 'neighborhood', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service ok but unfriendly , filthy bathroom .\n->service ok but unfriendly , filthy bathroom .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'bathroom', 'opinion': 'filthy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: but overall i give it a 10\n->but overall i give it a 10\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n->", + "output": "{\"text\": \"I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\", \"labels\": \"[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n->while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n[{'aspect': 'stock aluminum case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: definitely a great spot for a nice occasion or date .\n->definitely a great spot for a nice occasion or date .\n[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: I 'm not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and I all enjoy Mizu very much ... and we 're repeat customers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 'm not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and I all enjoy Mizu very much ... and we 're repeat customers .\n->", + "output": "{\"text\": \"I 'm not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and I all enjoy Mizu very much ... and we 're repeat customers .\", \"labels\": \"[{'aspect': 'Mizu', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: quiet keyboard .\n->quiet keyboard .\n[{'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: overall , i ' m definitely satisfied , i bought it wanting a well built laptop on the larger end ( which is why i ultimately went with the 14 inch ) with a solid material / frame .\n->overall , i ' m definitely satisfied , i bought it wanting a well built laptop on the larger end ( which is why i ultimately went with the 14 inch ) with a solid material / frame .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Moules were excellent , lobster ravioli was VERY salty !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMoules were excellent , lobster ravioli was VERY salty !\n->", + "output": "{\"text\": \"Moules were excellent , lobster ravioli was VERY salty !\", \"labels\": \"[{'aspect': 'Moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also they were $ 15 each !\n->also they were $ 15 each !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: hdd , agree didn ' t sound good .\n->hdd , agree didn ' t sound good .\n[{'aspect': 'hdd', 'opinion': \"' t sound good\", 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\ntext: Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTook my mom for Mother 's Day , and the maitre d ' was pretty rude .\n->", + "output": "{\"text\": \"Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\", \"labels\": \"[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a nice place to relax and have conversation .\n->it ' s a nice place to relax and have conversation .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n->All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n[{'aspect': 'appetizers', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'watering', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Tiny dessert was $ 8.00 ... just plain overpriced for what it is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTiny dessert was $ 8.00 ... just plain overpriced for what it is .\n->", + "output": "{\"text\": \"Tiny dessert was $ 8.00 ... just plain overpriced for what it is .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'Tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I was in love with Pongsri on 48th , but compared to Suan it is slow in service and overpriced .\n->I was in love with Pongsri on 48th , but compared to Suan it is slow in service and overpriced .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the portions are large and the servers always surprise us with a different starter .\n->the portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The drinks are always well made and wine selection is fairly priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe drinks are always well made and wine selection is fairly priced .\n->", + "output": "{\"text\": \"The drinks are always well made and wine selection is fairly priced .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'well made', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine selection', 'opinion': 'fairly priced', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n->you must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n[{'aspect': 'crabmeat lasagna', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chocolate bread pudding', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I love when restaurants think using fancy expensive ingrediants makes the food fine cuisine , even with no idea how to use them .\n->I love when restaurants think using fancy expensive ingrediants makes the food fine cuisine , even with no idea how to use them .\n[{'aspect': 'ingrediants', 'opinion': 'expensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Try their chef 's specials -- they are to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry their chef 's specials -- they are to die for .\n->", + "output": "{\"text\": \"Try their chef 's specials -- they are to die for .\", \"labels\": \"[{'aspect': \"chef 's specials\", 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"chef 's specials\", 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s fast , connects quickly to wifi , and the screen is quite nice .\n->it ' s fast , connects quickly to wifi , and the screen is quite nice .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: it is very good to use in korea .\n->it is very good to use in korea .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Downstairs lounge is always a good attraction\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDownstairs lounge is always a good attraction\n->", + "output": "{\"text\": \"Downstairs lounge is always a good attraction\", \"labels\": \"[{'aspect': 'Downstairs lounge', 'opinion': 'good attraction', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the turkey burgers are scary !\n->the turkey burgers are scary !\n[{'aspect': 'turkey burgers', 'opinion': 'scary', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n->One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n[{'aspect': 'menu', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Raga 's is a romantic , cozy restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRaga 's is a romantic , cozy restaurant .\n->", + "output": "{\"text\": \"Raga 's is a romantic , cozy restaurant .\", \"labels\": \"[{'aspect': \"Raga 's\", 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Raga 's\", 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it 's the only place you can get yummy authentic japanese comfort food .\n->it 's the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food here is rather good , but only if you like to wait for it .\n->The food here is rather good , but only if you like to wait for it .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The exotic food is beautifully presented and is a delight in delicious combinations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe exotic food is beautifully presented and is a delight in delicious combinations .\n->", + "output": "{\"text\": \"The exotic food is beautifully presented and is a delight in delicious combinations .\", \"labels\": \"[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ' d go back again\n->we ' d go back again\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n->The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'not very attentive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The staff is incredibly helpful and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is incredibly helpful and attentive .\n->", + "output": "{\"text\": \"The staff is incredibly helpful and attentive .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is quite disappointing .\n->it is quite disappointing .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the pro is by far the best .\n->the pro is by far the best .\n[{'aspect': 'pro', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The bar is very well stocked with interesting beers and well priced wines .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bar is very well stocked with interesting beers and well priced wines .\n->", + "output": "{\"text\": \"The bar is very well stocked with interesting beers and well priced wines .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'well stocked', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beers', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wines', 'opinion': 'well priced', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the other one had the same issue after 9 months of use , wasn ' t used right away when purchased .\n->the other one had the same issue after 9 months of use , wasn ' t used right away when purchased .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The staff is incredibly helpful and attentive .\n->The staff is incredibly helpful and attentive .\n[{'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Rude service , medicore food ... there are tons of restaurants in NY ... stay away from this one\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRude service , medicore food ... there are tons of restaurants in NY ... stay away from this one\n->", + "output": "{\"text\": \"Rude service , medicore food ... there are tons of restaurants in NY ... stay away from this one\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'medicore', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if known ahead of time , we would have not purchased this machine .\n->if known ahead of time , we would have not purchased this machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the screen is crisp and clear , easy to set up .\n->the screen is crisp and clear , easy to set up .\n[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: I had a great time at Jekyll and Hyde !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had a great time at Jekyll and Hyde !\n->", + "output": "{\"text\": \"I had a great time at Jekyll and Hyde !\", \"labels\": \"[{'aspect': 'Jekyll and Hyde', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wonderful strawberry daiquiries as well !\n->wonderful strawberry daiquiries as well !\n[{'aspect': 'strawberry daiquiries', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: mmmmmmmmmmmmmmm so delicious\n->mmmmmmmmmmmmmmm so delicious\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: I loved everythig about it-especially the shows and actors .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI loved everythig about it-especially the shows and actors .\n->", + "output": "{\"text\": \"I loved everythig about it-especially the shows and actors .\", \"labels\": \"[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: its alright\n->its alright\n[{'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\ntext: Our server was very helpful and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur server was very helpful and friendly .\n->", + "output": "{\"text\": \"Our server was very helpful and friendly .\", \"labels\": \"[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Much more reasonably priced too !\n->Much more reasonably priced too !\n[{'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: super yummy pizza !\n->super yummy pizza !\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The food was good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was good too .\n->", + "output": "{\"text\": \"The food was good too .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if i could give 0 stars i would do so for this place .\n->if i could give 0 stars i would do so for this place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it ' s fine as a computer , but the lack of a real guest account made it not workable as a family room media machine .\n->it ' s fine as a computer , but the lack of a real guest account made it not workable as a family room media machine .\n[{'aspect': 'guest account', 'opinion': 'not workable', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n->", + "output": "{\"text\": \"The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\", \"labels\": \"[{'aspect': 'outdoor atmosphere', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n->the pizza was delivered cold and the cheese was n ' t even fully melted !\n[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: service was efficient courteous .\n->service was efficient courteous .\n[{'aspect': 'service', 'opinion': 'efficient courteous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great service , great food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat service , great food .\n->", + "output": "{\"text\": \"Great service , great food .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is no way it justifies the accolades it receives , the attitude of the staff or the wait for a table .\n->there is no way it justifies the accolades it receives , the attitude of the staff or the wait for a table .\n[{'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n->Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n[{'aspect': 'meal', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTwo complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->", + "output": "{\"text\": \"Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\", \"labels\": \"[{'aspect': 'appetizer selection', 'opinion': 'complaints', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the manager was rude and handled the situation extremely poorly .\n->the manager was rude and handled the situation extremely poorly .\n[{'aspect': 'manager', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'manager', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the keys and mouse pad are responsive and comfortable to use .\n->the keys and mouse pad are responsive and comfortable to use .\n[{'aspect': 'keys', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keys', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWait staff is blantently unappreciative of your business but its the best pie on the UWS !\n->", + "output": "{\"text\": \"Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\", \"labels\": \"[{'aspect': 'Wait staff', 'opinion': 'unappreciative', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we wo n ' t go to this place again for a good meal .\n->we wo n ' t go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n->I must say it 's a little pricey for the food because it was not as spectacular as the view .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: By far the best salad I have had in a fast food restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBy far the best salad I have had in a fast food restaurant .\n->", + "output": "{\"text\": \"By far the best salad I have had in a fast food restaurant .\", \"labels\": \"[{'aspect': 'salad', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Cafe Noir', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery life is superb\n->battery life is superb\n[{'aspect': 'battery life', 'opinion': 'superb', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: fine dining restaurant quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfine dining restaurant quality .\n->", + "output": "{\"text\": \"fine dining restaurant quality .\", \"labels\": \"[{'aspect': 'dining', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was easy to set up .\n->it was easy to set up .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n->i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n[{'aspect': 'NULL', 'opinion': 'heaviness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOn a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n->", + "output": "{\"text\": \"On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Because of the delicate thin crust , take-out pies get soggy in their boxes .\n->Because of the delicate thin crust , take-out pies get soggy in their boxes .\n[{'aspect': 'take-out pies', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'delicate', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The chicken and steak were seasoned and cooked to perfection , and the lamb sandwhich is great for heartier appetites .\n->The chicken and steak were seasoned and cooked to perfection , and the lamb sandwhich is great for heartier appetites .\n[{'aspect': 'chicken', 'opinion': 'seasoned', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'seasoned', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb sandwhich', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->", + "output": "{\"text\": \"The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\", \"labels\": \"[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a pleasant surprise .\n->a pleasant surprise .\n[{'aspect': 'NULL', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the owner and staff are all japanese as well and that adds to the entire ambiance .\n->the owner and staff are all japanese as well and that adds to the entire ambiance .\n[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'ambiance', 'opinion': 'adds', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The staff is no nonsense .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is no nonsense .\n->", + "output": "{\"text\": \"The staff is no nonsense .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'no nonsense', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this restaurant was recommended by a local . . . as a matter of fact , he made the reservation for us and said we would not be disappointed !\n->this restaurant was recommended by a local . . . as a matter of fact , he made the reservation for us and said we would not be disappointed !\n[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i have been coming here for years and have nothing but good things to say about the service and the great staff at la lanterna .\n->i have been coming here for years and have nothing but good things to say about the service and the great staff at la lanterna .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: When I lived upstate for a while I would buy freeze the bagels and they would still be better than any else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhen I lived upstate for a while I would buy freeze the bagels and they would still be better than any else .\n->", + "output": "{\"text\": \"When I lived upstate for a while I would buy freeze the bagels and they would still be better than any else .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n->this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n[{'aspect': 'chromebook', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'boot up', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n->my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Worth visiting the 1st Ave spot because it is the original store .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWorth visiting the 1st Ave spot because it is the original store .\n->", + "output": "{\"text\": \"Worth visiting the 1st Ave spot because it is the original store .\", \"labels\": \"[{'aspect': '1st Ave spot', 'opinion': 'Worth visiting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is prepared quickly and efficiently .\n->the food is prepared quickly and efficiently .\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n->My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: He served me an Uni Hand roll , which I never had before , and let me tell you ... IT WAS HEAVEN !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHe served me an Uni Hand roll , which I never had before , and let me tell you ... IT WAS HEAVEN !\n->", + "output": "{\"text\": \"He served me an Uni Hand roll , which I never had before , and let me tell you ... IT WAS HEAVEN !\", \"labels\": \"[{'aspect': 'Uni Hand roll', 'opinion': 'HEAVEN', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Be sure to try the seasonal , and always delicious , specials .\n->Be sure to try the seasonal , and always delicious , specials .\n[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n->Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n[{'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The sake menu should not be overlooked !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sake menu should not be overlooked !\n->", + "output": "{\"text\": \"The sake menu should not be overlooked !\", \"labels\": \"[{'aspect': 'sake menu', 'opinion': 'overlooked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love pizza 33 . . .\n->love pizza 33 . . .\n[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: not happy with this one .\n->not happy with this one .\n[{'aspect': 'NULL', 'opinion': 'not happy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Service was very good - prompt , attentive and non-intrusive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was very good - prompt , attentive and non-intrusive .\n->", + "output": "{\"text\": \"Service was very good - prompt , attentive and non-intrusive .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n->the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n[{'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: this machine , through her minimum time of using it , has already been so much faster for her to use .\n->this machine , through her minimum time of using it , has already been so much faster for her to use .\n[{'aspect': 'machine', 'opinion': 'faster', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n->", + "output": "{\"text\": \"Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n->the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n->Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'understated', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTraditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n->", + "output": "{\"text\": \"Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\", \"labels\": \"[{'aspect': 'Traditional French decour', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hall', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m seriously considering returning it !\n->i ' m seriously considering returning it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard is very nice for me .\n->the keyboard is very nice for me .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: I 've been to at Cafe Spice probably 5-8 times , it is probably still the best Indian restaurant around Union Square .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've been to at Cafe Spice probably 5-8 times , it is probably still the best Indian restaurant around Union Square .\n->", + "output": "{\"text\": \"I 've been to at Cafe Spice probably 5-8 times , it is probably still the best Indian restaurant around Union Square .\", \"labels\": \"[{'aspect': 'Cafe Spice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The atmosphere is much better than Sripraphai ( more modern and sleek ) .\n->The atmosphere is much better than Sripraphai ( more modern and sleek ) .\n[{'aspect': 'atmosphere', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: nice view of river and nyc .\n->nice view of river and nyc .\n[{'aspect': 'view of river and nyc', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: To sum it up : Service varies from good to mediorce , depending on which waiter you get ; generally it is just average Ok .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTo sum it up : Service varies from good to mediorce , depending on which waiter you get ; generally it is just average Ok .\n->", + "output": "{\"text\": \"To sum it up : Service varies from good to mediorce , depending on which waiter you get ; generally it is just average Ok .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'varies', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is expensive but well worth the money .\n->it is expensive but well worth the money .\n[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: cons : the speakers make a loud muffled white noise while playing music on occasion .\n->cons : the speakers make a loud muffled white noise while playing music on occasion .\n[{'aspect': 'speakers', 'opinion': 'cons', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: Seating is always prompt , though the restaurant does fill up in the evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSeating is always prompt , though the restaurant does fill up in the evening .\n->", + "output": "{\"text\": \"Seating is always prompt , though the restaurant does fill up in the evening .\", \"labels\": \"[{'aspect': 'Seating', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n->and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n[{'aspect': 'backlit keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n->the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n->", + "output": "{\"text\": \"Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'raw vegatables in side orders', 'opinion': 'wondered about freshmess', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n->also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n[{'aspect': 'service', 'opinion': 'place', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'the', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'just', 'opinion': \"' re\", 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n->the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n[{'aspect': 'servers', 'opinion': 'perfected', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n->", + "output": "{\"text\": \"The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'semi-private boths', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'semi-private boths', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n->I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n[{'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we ordered this chromebook for my son to use for school .\n->we ordered this chromebook for my son to use for school .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: It 's simply the best meal in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's simply the best meal in NYC .\n->", + "output": "{\"text\": \"It 's simply the best meal in NYC .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have to say that the keyboard is my favorite feature .\n->i have to say that the keyboard is my favorite feature .\n[{'aspect': 'keyboard', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: The takeout is great too since they give high quality tupperware as well .\n->The takeout is great too since they give high quality tupperware as well .\n[{'aspect': 'takeout', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: If we were to move from the upper east side , we would genuinely miss this restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf we were to move from the upper east side , we would genuinely miss this restaurant .\n->", + "output": "{\"text\": \"If we were to move from the upper east side , we would genuinely miss this restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my review is based on the fact that i got a pretty good value on this when it was selling for $ 200 less than normal .\n->my review is based on the fact that i got a pretty good value on this when it was selling for $ 200 less than normal .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Avoid this place !\n->Avoid this place !\n[{'aspect': 'place', 'opinion': 'Avoid', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The restaurant is cute but not upscale .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe restaurant is cute but not upscale .\n->", + "output": "{\"text\": \"The restaurant is cute but not upscale .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'cute', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'not upscale', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this to save me money and now i wasted $ 700 to still have to go out and buy a new one .\n->i bought this to save me money and now i wasted $ 700 to still have to go out and buy a new one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n->so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->", + "output": "{\"text\": \"The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n->i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n[{'aspect': 'battery', 'opinion': 'better', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: personally , i would steer clear of this chromebook .\n->personally , i would steer clear of this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: We had a great time at the Jekyll and hyde Pub last night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had a great time at the Jekyll and hyde Pub last night .\n->", + "output": "{\"text\": \"We had a great time at the Jekyll and hyde Pub last night .\", \"labels\": \"[{'aspect': 'Jekyll and hyde Pub', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mousepad is functional but really doesnt get in the way .\n->the mousepad is functional but really doesnt get in the way .\n[{'aspect': 'mousepad', 'opinion': 'functional', 'polarity': 'neutral', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n->The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n[{'aspect': 'plain slice', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: After really enjoying ourselves at the bar we sat down at a table and had dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAfter really enjoying ourselves at the bar we sat down at a table and had dinner .\n->", + "output": "{\"text\": \"After really enjoying ourselves at the bar we sat down at a table and had dinner .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard does seem a little off size as i seem to often be one key offset when get in typing position .\n->keyboard does seem a little off size as i seem to often be one key offset when get in typing position .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: not sure it is related , but when i open facebook , aaaallllll the messages open simultaneously .\n->not sure it is related , but when i open facebook , aaaallllll the messages open simultaneously .\n[{'aspect': 'facebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: The server was really cool and served us our food and drinks with a smile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe server was really cool and served us our food and drinks with a smile .\n->", + "output": "{\"text\": \"The server was really cool and served us our food and drinks with a smile .\", \"labels\": \"[{'aspect': 'server', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for me the extra storage , back light keyboard ( you ' ll love it ) , 2 in 1 factor , and great build quality made it a no - brainer .\n->for me the extra storage , back light keyboard ( you ' ll love it ) , 2 in 1 factor , and great build quality made it a no - brainer .\n[{'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#DESIGN_FEATURES'}, {'aspect': 'back light keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i will be going back and heartily recommend it !\n->i will be going back and heartily recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The place 's decor and hidden bathrooms made for a good laugh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place 's decor and hidden bathrooms made for a good laugh .\n->", + "output": "{\"text\": \"The place 's decor and hidden bathrooms made for a good laugh .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hidden bathrooms', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We 've been to Grocery three times and not once has an item on the menu disappointed .\n->We 've been to Grocery three times and not once has an item on the menu disappointed .\n[{'aspect': 'menu', 'opinion': 'disappointed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I highly recommend visiting this restaurant and having dinner and drinks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend visiting this restaurant and having dinner and drinks !\n->", + "output": "{\"text\": \"I highly recommend visiting this restaurant and having dinner and drinks !\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so easy to use and is not that slow like some say works just fine for casual use .\n->so easy to use and is not that slow like some say works just fine for casual use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'not that slow', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n->i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n[{'aspect': 'look', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'speed', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n->", + "output": "{\"text\": \"If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .\n->the waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i had previouslu bought an msi mobo which refused to boot unless windows 10 was the os , but i had worked around all of those problems .\n->i had previouslu bought an msi mobo which refused to boot unless windows 10 was the os , but i had worked around all of those problems .\n[{'aspect': 'msi mobo', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n->", + "output": "{\"text\": \"The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\", \"labels\": \"[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what you are paying for is the environment and the name .\n->what you are paying for is the environment and the name .\n[{'aspect': 'environment', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n->have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n[{'aspect': 'machine', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The entertainment was great they have shows that go on through out the dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe entertainment was great they have shows that go on through out the dinner .\n->", + "output": "{\"text\": \"The entertainment was great they have shows that go on through out the dinner .\", \"labels\": \"[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: about 4 hours of battery\n->about 4 hours of battery\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n->As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The bagel was huge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bagel was huge .\n->", + "output": "{\"text\": \"The bagel was huge .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n->Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n[{'aspect': 'seating in the garden', 'opinion': 'lie', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'seats', 'opinion': 'not available', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i really think it ' s junk .\n->i really think it ' s junk .\n[{'aspect': 'NULL', 'opinion': 'junk', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n->", + "output": "{\"text\": \"This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: be prepared to wait a good five hours give or take until the system runs smoother .\n->be prepared to wait a good five hours give or take until the system runs smoother .\n[{'aspect': 'system', 'opinion': 'good', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'system', 'opinion': 'smoother', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n->stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n[{'aspect': 'stylus', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'stylus', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOther guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->", + "output": "{\"text\": \"Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My husband and I enjoy Sangria .\n->My husband and I enjoy Sangria .\n[{'aspect': 'Sangria', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n->i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n[{'aspect': 'NULL', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n->", + "output": "{\"text\": \"I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 1080p display is very sharp .\n->the 1080p display is very sharp .\n[{'aspect': '1080p display', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n->received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\ntext: All the staff is absolutely professional ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll the staff is absolutely professional ! !\n->", + "output": "{\"text\": \"All the staff is absolutely professional ! !\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: : p ) , and the machine is definitely zippy .\n->: p ) , and the machine is definitely zippy .\n[{'aspect': 'machine', 'opinion': 'zippy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: also battery life is max 8 hours , not 10 .\n->also battery life is max 8 hours , not 10 .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: This restaurant was way overhyped .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis restaurant was way overhyped .\n->", + "output": "{\"text\": \"This restaurant was way overhyped .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'overhyped', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve only found 2 other recent reviews of this product that had the same problem ( go ahead and search for them in the reviews ) .\n->i ' ve only found 2 other recent reviews of this product that had the same problem ( go ahead and search for them in the reviews ) .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: my review is based on the fact that i got a pretty good value on this when it was selling for $ 200 less than normal .\n->my review is based on the fact that i got a pretty good value on this when it was selling for $ 200 less than normal .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: My chow fun and chow see was really bland and oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy chow fun and chow see was really bland and oily .\n->", + "output": "{\"text\": \"My chow fun and chow see was really bland and oily .\", \"labels\": \"[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they do a ton of online homework and this is perfect for them .\n->they do a ton of online homework and this is perfect for them .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i still find the one piece touch pads unreliable to use even after all the tweaking .\n->i still find the one piece touch pads unreliable to use even after all the tweaking .\n[{'aspect': 'touch pads', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: The service was the only thing good about this restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was the only thing good about this restaurant .\n->", + "output": "{\"text\": \"The service was the only thing good about this restaurant .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is surprisingly poor .\n->the screen is surprisingly poor .\n[{'aspect': 'screen', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: highly recommended to all !\n->highly recommended to all !\n[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: It 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor .\n->", + "output": "{\"text\": \"It 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tuna', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen not aligned perfectly .\n->screen not aligned perfectly .\n[{'aspect': 'screen', 'opinion': 'not aligned perfectly', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: our teenage kids love it , too .\n->our teenage kids love it , too .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Their pad penang is delicious and everything else is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir pad penang is delicious and everything else is fantastic .\n->", + "output": "{\"text\": \"Their pad penang is delicious and everything else is fantastic .\", \"labels\": \"[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: honestly the worst sushi my husband and i had in our entire lives .\n->honestly the worst sushi my husband and i had in our entire lives .\n[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: in the evening , this place attracted a well dressed , with it , ny crowd .\n->in the evening , this place attracted a well dressed , with it , ny crowd .\n[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: The price is reasonable although the service is poor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe price is reasonable although the service is poor .\n->", + "output": "{\"text\": \"The price is reasonable although the service is poor .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with the recent apple upgrade to macbook pros ( the touch of genius bar or whatever ) , the price has jumped .\n->with the recent apple upgrade to macbook pros ( the touch of genius bar or whatever ) , the price has jumped .\n[{'aspect': 'macbook pros', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: From the terrible service , to the bland food , not to mention the unaccommodating managers , the overall experience was horrible .\n->From the terrible service , to the bland food , not to mention the unaccommodating managers , the overall experience was horrible .\n[{'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'managers', 'opinion': 'unaccommodating', 'polarity': 'negative', 'category': 'NULL'}]\ntext: fresh restaurant was amazing ... ... .. food was delicious and of course fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfresh restaurant was amazing ... ... .. food was delicious and of course fresh .\n->", + "output": "{\"text\": \"fresh restaurant was amazing ... ... .. food was delicious and of course fresh .\", \"labels\": \"[{'aspect': 'fresh restaurant', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh restaurant', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then it would not boot up .\n->then it would not boot up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: A cool place to hang with your friends for a couple of healthy drinks and desserts .\n->A cool place to hang with your friends for a couple of healthy drinks and desserts .\n[{'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Hats off to the chef .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHats off to the chef .\n->", + "output": "{\"text\": \"Hats off to the chef .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'Hats off', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n->it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: food was very good , but not what i would consider out of this world .\n->food was very good , but not what i would consider out of this world .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: the salads are delicious , both refreshing and very spicy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe salads are delicious , both refreshing and very spicy .\n->", + "output": "{\"text\": \"the salads are delicious , both refreshing and very spicy .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Despite the fact that the space is large , they 've overcrowded the floor with tables .\n->Despite the fact that the space is large , they 've overcrowded the floor with tables .\n[{'aspect': 'space', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'overcrowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: and the chromebook does not go to sleep or otherwise shut off .\n->and the chromebook does not go to sleep or otherwise shut off .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: We had Pam 's special fried fish and it was amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had Pam 's special fried fish and it was amazing .\n->", + "output": "{\"text\": \"We had Pam 's special fried fish and it was amazing .\", \"labels\": \"[{'aspect': \"Pam 's special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the vivobook f510ua is a great laptop with fantastic specs .\n->the vivobook f510ua is a great laptop with fantastic specs .\n[{'aspect': 'vivobook f510ua', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'specs', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: We both opted for a pasta dish and they were served timely and fresh .\n->We both opted for a pasta dish and they were served timely and fresh .\n[{'aspect': 'pasta dish', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great vibe , lots of people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat vibe , lots of people .\n->", + "output": "{\"text\": \"Great vibe , lots of people .\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n->the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n[{'aspect': 'hot dogs', 'opinion': 'juicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dogs', 'opinion': 'tender', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the overall durability is a bit suspect but with the price tag , it is essentially a 1 - 2 year investment for school .\n->the overall durability is a bit suspect but with the price tag , it is essentially a 1 - 2 year investment for school .\n[{'aspect': 'NULL', 'opinion': 'suspect', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: I did n't complain , I liked the atmosphere so much .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI did n't complain , I liked the atmosphere so much .\n->", + "output": "{\"text\": \"I did n't complain , I liked the atmosphere so much .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speakers overall are not very good .\n->the speakers overall are not very good .\n[{'aspect': 'speakers', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n->the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n[{'aspect': 'lobster knuckles', 'opinion': 'ok', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'lobster knuckles', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmbience is so cute and quaint , good for business although we were there on vacation .\n->", + "output": "{\"text\": \"Ambience is so cute and quaint , good for business although we were there on vacation .\", \"labels\": \"[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: next , is that the track pad is insanely wobbly .\n->next , is that the track pad is insanely wobbly .\n[{'aspect': 'track pad', 'opinion': 'wobbly', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: acer makes a good unit too .\n->acer makes a good unit too .\n[{'aspect': 'acer', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: Although we were looking for regular lettuce and some walnuts the salads we got were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlthough we were looking for regular lettuce and some walnuts the salads we got were great .\n->", + "output": "{\"text\": \"Although we were looking for regular lettuce and some walnuts the salads we got were great .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unfortunately , the downfall for me are the speakers .\n->unfortunately , the downfall for me are the speakers .\n[{'aspect': 'speakers', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Ingredients are organic which is a real plus for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIngredients are organic which is a real plus for me .\n->", + "output": "{\"text\": \"Ingredients are organic which is a real plus for me .\", \"labels\": \"[{'aspect': 'Ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is the best of both worlds\n->this laptop is the best of both worlds\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This is some really good , inexpensive sushi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is some really good , inexpensive sushi .\n->", + "output": "{\"text\": \"This is some really good , inexpensive sushi .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so , i switch with my boyfriend again to see if maybe i could stomach the meat and spinach again , but the spinach was so undercooked that i just could not bite through it .\n->so , i switch with my boyfriend again to see if maybe i could stomach the meat and spinach again , but the spinach was so undercooked that i just could not bite through it .\n[{'aspect': 'spinach', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n->it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The spicy Tuna roll is huge and probably the best that I 've had at this price range .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe spicy Tuna roll is huge and probably the best that I 've had at this price range .\n->", + "output": "{\"text\": \"The spicy Tuna roll is huge and probably the best that I 've had at this price range .\", \"labels\": \"[{'aspect': 'spicy Tuna roll', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spicy Tuna roll', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place was quiet and delightful .\n->The place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: nice piece but battery holds only 3h - not 10 .\n->nice piece but battery holds only 3h - not 10 .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: The Yellowtail was particularly good as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Yellowtail was particularly good as well .\n->", + "output": "{\"text\": \"The Yellowtail was particularly good as well .\", \"labels\": \"[{'aspect': 'Yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the c302 is a great machine .\n->the c302 is a great machine .\n[{'aspect': 'c302', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: beautiful display and runs fast !\n->beautiful display and runs fast !\n[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n->", + "output": "{\"text\": \"I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\", \"labels\": \"[{'aspect': 'all you can eat deal', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at all and it took several minutes to boot up .\n->at all and it took several minutes to boot up .\n[{'aspect': 'boot up', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The sandwiches are dry , tasteless and way overpriced .\n->The sandwiches are dry , tasteless and way overpriced .\n[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Big Wong gets big Ups for a fine establishment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBig Wong gets big Ups for a fine establishment .\n->", + "output": "{\"text\": \"Big Wong gets big Ups for a fine establishment .\", \"labels\": \"[{'aspect': 'Big Wong', 'opinion': 'big Ups', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Big Wong', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we will be back .\n->we will be back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i will never return .\n->i will never return .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: They have it all -- great price , food , and service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey have it all -- great price , food , and service .\n->", + "output": "{\"text\": \"They have it all -- great price , food , and service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just straight up cheap , good food .\n->just straight up cheap , good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i ' m beyond satisfied with this chromebook , it is stunning in every way .\n->i ' m beyond satisfied with this chromebook , it is stunning in every way .\n[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->", + "output": "{\"text\": \"The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i needed a small chromebook for light web use while traveling and this tablet does ok but considering the price i would have expected a better processor and keyboard lighting .\n->i needed a small chromebook for light web use while traveling and this tablet does ok but considering the price i would have expected a better processor and keyboard lighting .\n[{'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#PRICE'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#PRICE'}]\nExample:\ntext: all in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n->all in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: This place is always very crowded and popular .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is always very crowded and popular .\n->", + "output": "{\"text\": \"This place is always very crowded and popular .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'crowded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'popular', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cons : no caps lock key ( still haven ' t found it , help ! )\n->cons : no caps lock key ( still haven ' t found it , help ! )\n[{'aspect': 'caps lock key', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n->i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n[{'aspect': 'the new upgrades', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: Enjoyed a very nice Caesar Salad while my wife had arugula and goat cheese ... .both very tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEnjoyed a very nice Caesar Salad while my wife had arugula and goat cheese ... .both very tasty .\n->", + "output": "{\"text\": \"Enjoyed a very nice Caesar Salad while my wife had arugula and goat cheese ... .both very tasty .\", \"labels\": \"[{'aspect': 'Caesar Salad', 'opinion': 'Enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Caesar Salad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'arugula and goat cheese', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n->I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the battery life on the laptop is disappointing and the webcam doesn ' t work it .\n->the battery life on the laptop is disappointing and the webcam doesn ' t work it .\n[{'aspect': 'battery life on the laptop', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'webcam', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: We both opted for a pasta dish and they were served timely and fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe both opted for a pasta dish and they were served timely and fresh .\n->", + "output": "{\"text\": \"We both opted for a pasta dish and they were served timely and fresh .\", \"labels\": \"[{'aspect': 'pasta dish', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is also really nice .\n->The wine list is also really nice .\n[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n->the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n[{'aspect': 'keyboard', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: We concluded with tiramisu chocolate cake , both were delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe concluded with tiramisu chocolate cake , both were delicious .\n->", + "output": "{\"text\": \"We concluded with tiramisu chocolate cake , both were delicious .\", \"labels\": \"[{'aspect': 'tiramisu chocolate cake', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n->Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n[{'aspect': 'raddichio', 'opinion': 'bitter', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: when i go to type something , it sticks and will not release .\n->when i go to type something , it sticks and will not release .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recently went to this restaurant with some co-workers for lunch and had an amazing time .\n->", + "output": "{\"text\": \"I recently went to this restaurant with some co-workers for lunch and had an amazing time .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'amazing time', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while the product worked decently for about a month , it went downhill soon after .\n->while the product worked decently for about a month , it went downhill soon after .\n[{'aspect': 'product', 'opinion': 'decently', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Our waitress was sweet and accomodating , not overbearing .\n->Our waitress was sweet and accomodating , not overbearing .\n[{'aspect': 'waitress', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}]\ntext: sometimes i get good food and ok service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes i get good food and ok service .\n->", + "output": "{\"text\": \"sometimes i get good food and ok service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i returned it twice .\n->i returned it twice .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i loved it and would highly recommend .\n->i loved it and would highly recommend .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes i get bad food and bad service , sometimes i get good good and bad service .\n->", + "output": "{\"text\": \"sometimes i get bad food and bad service , sometimes i get good good and bad service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice screen , nice feel .\n->nice screen , nice feel .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: it ' s super portable and sleek .\n->it ' s super portable and sleek .\n[{'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: And evaluated on those terms Pastis is simply wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd evaluated on those terms Pastis is simply wonderful .\n->", + "output": "{\"text\": \"And evaluated on those terms Pastis is simply wonderful .\", \"labels\": \"[{'aspect': 'Pastis', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is courteous and friendly .\n->The staff is courteous and friendly .\n[{'aspect': 'staff', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n->calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'room', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'clerks', 'opinion': 'unhelpful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n->", + "output": "{\"text\": \"Mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\", \"labels\": \"[{'aspect': 'raddichio', 'opinion': 'bitter', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is set far from the small street it ' s on , and there is no traffic noise .\n->it is set far from the small street it ' s on , and there is no traffic noise .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\nExample:\ntext: i love the feel of a lighter os and can do many tasks using google / web based apps .\n->i love the feel of a lighter os and can do many tasks using google / web based apps .\n[{'aspect': 'os', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: My friend got the mushroom pizza which tasted better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy friend got the mushroom pizza which tasted better .\n->", + "output": "{\"text\": \"My friend got the mushroom pizza which tasted better .\", \"labels\": \"[{'aspect': 'mushroom pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n->the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n[{'aspect': 'crust', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'light', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: again , overall i believe this is a great laptop with an amazing keyboard .\n->again , overall i believe this is a great laptop with an amazing keyboard .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The sangria was pretty tasty and good on a hot muggy day .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sangria was pretty tasty and good on a hot muggy day .\n->", + "output": "{\"text\": \"The sangria was pretty tasty and good on a hot muggy day .\", \"labels\": \"[{'aspect': 'sangria', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sangria', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really wanted to like this chromebook .\n->i really wanted to like this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'wanted to like', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i will be back !\n->i will be back !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nKind of a small place but I guess if they are not too busy might be able to fit a group or kids .\n->", + "output": "{\"text\": \"Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n->it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n[{'aspect': 'company', 'opinion': 'poor', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: this is my 3rd chromebook and it is , by far , the flakiest one i ' ve had .\n->this is my 3rd chromebook and it is , by far , the flakiest one i ' ve had .\n[{'aspect': 'chromebook', 'opinion': 'flakiest', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: I started out with a Bombay beer which was big enough for two .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI started out with a Bombay beer which was big enough for two .\n->", + "output": "{\"text\": \"I started out with a Bombay beer which was big enough for two .\", \"labels\": \"[{'aspect': 'Bombay beer', 'opinion': 'big', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the rig works better than advertised .\n->the rig works better than advertised .\n[{'aspect': 'rig', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service was slow , but the people were friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was slow , but the people were friendly .\n->", + "output": "{\"text\": \"Service was slow , but the people were friendly .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n->the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'staff', 'opinion': 'not seem knowledgeable', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Consistently good Japanese Tapas .\n->Consistently good Japanese Tapas .\n[{'aspect': 'Japanese Tapas', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is authentic Italian - delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is authentic Italian - delicious !\n->", + "output": "{\"text\": \"The food is authentic Italian - delicious !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'authentic Italian', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the chromebook does not go to sleep or otherwise shut off .\n->and the chromebook does not go to sleep or otherwise shut off .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: only issue is that the graphics aren ' t quite as good as i expected , but i didn ' t buy this to use as a gaming pc so i ' m not overly concerned about it .\n->only issue is that the graphics aren ' t quite as good as i expected , but i didn ' t buy this to use as a gaming pc so i ' m not overly concerned about it .\n[{'aspect': 'graphics', 'opinion': \"' t quite as good as\", 'polarity': 'neutral', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: Pizza is terrific , as is homemade pasta .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza is terrific , as is homemade pasta .\n->", + "output": "{\"text\": \"Pizza is terrific , as is homemade pasta .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just that it seems that the hard drive doesn ' t work properly .\n->just that it seems that the hard drive doesn ' t work properly .\n[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i will say this , if you ' re not doing video production and just need basic computering , these chromebooks are everything .\n->i will say this , if you ' re not doing video production and just need basic computering , these chromebooks are everything .\n[{'aspect': 'chromebooks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Ambience is delightful , service impeccable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmbience is delightful , service impeccable .\n->", + "output": "{\"text\": \"Ambience is delightful , service impeccable .\", \"labels\": \"[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n->i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n[{'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'processor', 'opinion': 'faster', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'google play store', 'opinion': 'compatibility', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: no caps lock on the keyboard .\n->no caps lock on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: I 'm still mad that i had to pay for lousy food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 'm still mad that i had to pay for lousy food .\n->", + "output": "{\"text\": \"I 'm still mad that i had to pay for lousy food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life has been very good .\n->battery life has been very good .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: the price is right too .\n->the price is right too .\n[{'aspect': 'NULL', 'opinion': 'right', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n->", + "output": "{\"text\": \"The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\", \"labels\": \"[{'aspect': 'hanger steak', 'opinion': 'rubber', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tuna', 'opinion': 'flavorless', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought it for a xmas gift , dead in less than 3 months .\n->bought it for a xmas gift , dead in less than 3 months .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n->to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'teodora', 'opinion': 'deficiencies', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: Service was also horrible and the ambience is not that great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was also horrible and the ambience is not that great .\n->", + "output": "{\"text\": \"Service was also horrible and the ambience is not that great .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only thing more wonderful than the food ( which is exceptional ) is the service .\n->The only thing more wonderful than the food ( which is exceptional ) is the service .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i must give it yon out of yon stars !\n->i must give it yon out of yon stars !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: It 's a small cute restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's a small cute restaurant .\n->", + "output": "{\"text\": \"It 's a small cute restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'small cute', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he has thoroughly enjoyed it\n->he has thoroughly enjoyed it\n[{'aspect': 'NULL', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the retina display is stunning .\n->the retina display is stunning .\n[{'aspect': 'retina display', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: I absolutely love this place ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI absolutely love this place ! ! !\n->", + "output": "{\"text\": \"I absolutely love this place ! ! !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n->have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n[{'aspect': 'machine', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: We wo n't go to this place again for a good meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe wo n't go to this place again for a good meal .\n->", + "output": "{\"text\": \"We wo n't go to this place again for a good meal .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was slow , but the people were friendly .\n->Service was slow , but the people were friendly .\n[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: have never had a problem with service save a missing rice once .\n->have never had a problem with service save a missing rice once .\n[{'aspect': 'service', 'opinion': 'problem', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: However , I think this place is a good hang out spot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , I think this place is a good hang out spot .\n->", + "output": "{\"text\": \"However , I think this place is a good hang out spot .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n->as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n[{'aspect': 'audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: - beautiful , bright ips screen with full 1080p resolution\n->- beautiful , bright ips screen with full 1080p resolution\n[{'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: THe Pizza and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTHe Pizza and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .\n->", + "output": "{\"text\": \"THe Pizza and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its cheap plastic and honestly , the keyboard its really bad .\n->its cheap plastic and honestly , the keyboard its really bad .\n[{'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: I liked the food at this quasi-thai restaurant .\n->I liked the food at this quasi-thai restaurant .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: It is one the nicest outdoor restaurants I have ever seen in NY -- I am from Italy and this place rivals the ones in my country .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is one the nicest outdoor restaurants I have ever seen in NY -- I am from Italy and this place rivals the ones in my country .\n->", + "output": "{\"text\": \"It is one the nicest outdoor restaurants I have ever seen in NY -- I am from Italy and this place rivals the ones in my country .\", \"labels\": \"[{'aspect': 'outdoor restaurants', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very good to use in korea .\n->it is very good to use in korea .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: my father is very satisfied with this laptop\n->my father is very satisfied with this laptop\n[{'aspect': 'laptop', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFirst of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->", + "output": "{\"text\": \"First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\", \"labels\": \"[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop has good hardware specs , but the screen has very poor color coverage : 59 % .\n->this laptop has good hardware specs , but the screen has very poor color coverage : 59 % .\n[{'aspect': 'screen', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: on a side note , there is a slight defect on my chromebook as there is some creaking and loose feeling when pressing on the bottom left side of my screen , this can be especially annoying in tablet mode .\n->on a side note , there is a slight defect on my chromebook as there is some creaking and loose feeling when pressing on the bottom left side of my screen , this can be especially annoying in tablet mode .\n[{'aspect': 'chromebook', 'opinion': 'defect', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n->", + "output": "{\"text\": \"The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\", \"labels\": \"[{'aspect': 'tables', 'opinion': 'crammed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'too close', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Decor is nice and minimalist , food simple yet very well presented and cooked , and the wine list matches the food very well .\n->Decor is nice and minimalist , food simple yet very well presented and cooked , and the wine list matches the food very well .\n[{'aspect': 'Decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Decor', 'opinion': 'minimalist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'simple', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'well presented and cooked', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service is average .\n->service is average .\n[{'aspect': 'service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSlightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n->", + "output": "{\"text\": \"Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\", \"labels\": \"[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend the sophia pizza .\n->i highly recommend the sophia pizza .\n[{'aspect': 'sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: internet runs well .\n->internet runs well .\n[{'aspect': 'internet', 'opinion': 'well', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: Service is not what one would expect from a joint in this price category .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is not what one would expect from a joint in this price category .\n->", + "output": "{\"text\": \"Service is not what one would expect from a joint in this price category .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never has it run out of power while on battery .\n->never has it run out of power while on battery .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food here does a great service to the name ( cantonese that is . . . ) .\n->the food here does a great service to the name ( cantonese that is . . . ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Somehow working the italian charm with constant mille grazie does not constitute proper service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSomehow working the italian charm with constant mille grazie does not constitute proper service .\n->", + "output": "{\"text\": \"Somehow working the italian charm with constant mille grazie does not constitute proper service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cozy romantic atomosphere with only around 15 tables at most .\n->cozy romantic atomosphere with only around 15 tables at most .\n[{'aspect': 'atomosphere', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atomosphere', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: we could have made a meal of the yummy dumplings from the dumpling menu .\n->we could have made a meal of the yummy dumplings from the dumpling menu .\n[{'aspect': 'dumplings', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .\n->", + "output": "{\"text\": \"Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .\", \"labels\": \"[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: went on a 3 day oyster binge , with fish bringing up the closing , and i am so glad this was the place it o trip ended , because it was so great !\n->went on a 3 day oyster binge , with fish bringing up the closing , and i am so glad this was the place it o trip ended , because it was so great !\n[{'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Fish was overdone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFish was overdone .\n->", + "output": "{\"text\": \"Fish was overdone .\", \"labels\": \"[{'aspect': 'Fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was warm and attentive , beef carpaachio was exellent ( huge portion ) and pasta was fresh and well-prepared .\n->Service was warm and attentive , beef carpaachio was exellent ( huge portion ) and pasta was fresh and well-prepared .\n[{'aspect': 'Service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beef carpaachio', 'opinion': 'exellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'well-prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i knocked off a star for build quality control .\n->i knocked off a star for build quality control .\n[{'aspect': 'build quality control', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: Cute place , nice wait staff but would never go there again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCute place , nice wait staff but would never go there again .\n->", + "output": "{\"text\": \"Cute place , nice wait staff but would never go there again .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ballato 's is consistently delicious authentic italian food .\n->Ballato 's is consistently delicious authentic italian food .\n[{'aspect': 'italian food', 'opinion': 'delicious authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Seriously , this is the best all you can eat in town- As everyone says , the Spicy Tuna hand rolls are the best- have 4 of these , and you 've broken even .\n->Seriously , this is the best all you can eat in town- As everyone says , the Spicy Tuna hand rolls are the best- have 4 of these , and you 've broken even .\n[{'aspect': 'Spicy Tuna hand rolls', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Someone else recommended the dessert - we also left that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSomeone else recommended the dessert - we also left that .\n->", + "output": "{\"text\": \"Someone else recommended the dessert - we also left that .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'recommended', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n->chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n[{'aspect': 'chrome', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n->i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'software', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\ntext: Skip this restaurant , it 's a big disappointment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSkip this restaurant , it 's a big disappointment .\n->", + "output": "{\"text\": \"Skip this restaurant , it 's a big disappointment .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'Skip', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is good , i ca n ' t lie .\n->the food is good , i ca n ' t lie .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food was just OK , at least for what food was available .\n->The food was just OK , at least for what food was available .\n[{'aspect': 'food', 'opinion': 'OK', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMyagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .\n->", + "output": "{\"text\": \"Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .\", \"labels\": \"[{'aspect': 'Myagi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: red dragon roll - my favorite thing to eat , of any food group - hands down\n->red dragon roll - my favorite thing to eat , of any food group - hands down\n[{'aspect': 'red dragon roll', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Best Pastrami I ever had and great portion without being ridiculous .\n->Best Pastrami I ever had and great portion without being ridiculous .\n[{'aspect': 'Pastrami', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I 've never had bad service and the fish is fresh and delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've never had bad service and the fish is fresh and delicious .\n->", + "output": "{\"text\": \"I 've never had bad service and the fish is fresh and delicious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n->The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n[{'aspect': 'in-house lady DJ', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you want something really different than try jekyll and hyde .\n->if you want something really different than try jekyll and hyde .\n[{'aspect': 'jekyll and hyde', 'opinion': 'different', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Their tuna tartar appetizer is to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir tuna tartar appetizer is to die for .\n->", + "output": "{\"text\": \"Their tuna tartar appetizer is to die for .\", \"labels\": \"[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have frequented ' ino for several years and the food remains excellent .\n->have frequented ' ino for several years and the food remains excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: screen maximum brightness is still not bright enough\n->screen maximum brightness is still not bright enough\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\n->", + "output": "{\"text\": \"I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'ashamed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: trackpad is nice and quiet and responsive .\n->trackpad is nice and quiet and responsive .\n[{'aspect': 'trackpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: excellent computer , better than expected .\n->excellent computer , better than expected .\n[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The place is so cool and the service is prompt and curtious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is so cool and the service is prompt and curtious .\n->", + "output": "{\"text\": \"The place is so cool and the service is prompt and curtious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the sea bass .\n->try the sea bass .\n[{'aspect': 'sea bass', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this is perfect for my needs .\n->this is perfect for my needs .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I highly recommend to anyone to give this place a try .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend to anyone to give this place a try .\n->", + "output": "{\"text\": \"I highly recommend to anyone to give this place a try .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good , fast service .\n->Good , fast service .\n[{'aspect': 'service', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was very good - prompt , attentive and non - intrusive .\n->service was very good - prompt , attentive and non - intrusive .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'non - intrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: A restaurant that does n't try to do anything except serve great food with great service in a pleasant atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA restaurant that does n't try to do anything except serve great food with great service in a pleasant atmosphere .\n->", + "output": "{\"text\": \"A restaurant that does n't try to do anything except serve great food with great service in a pleasant atmosphere .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nice atmosphere , the service was very pleasant and the desert was good .\n->Nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'Nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the concept of japanese tapas is newly created and clearly does n ' t work .\n->the concept of japanese tapas is newly created and clearly does n ' t work .\n[{'aspect': 'japanese tapas', 'opinion': \"does n ' t work\", 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The dining room is quietly elegant with no music to shout over -- how refreshing !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dining room is quietly elegant with no music to shout over -- how refreshing !\n->", + "output": "{\"text\": \"The dining room is quietly elegant with no music to shout over -- how refreshing !\", \"labels\": \"[{'aspect': 'dining room', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining room', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: constantly got the blue screen , already tryed everything to fix it .\n->constantly got the blue screen , already tryed everything to fix it .\n[{'aspect': 'blue screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n->You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Vanilla Shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n->", + "output": "{\"text\": \"The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n->we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'gulab jamun ( dessert )', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n->Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n[{'aspect': 'Pizza', 'opinion': 'Outstanding', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLooking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n->", + "output": "{\"text\": \"Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n->portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: october 12 , 2017 - - started having trouble maintaining connection to wifi ( spectrum service ) , but usually after several loops re - entering password , connection would be re - established .\n->october 12 , 2017 - - started having trouble maintaining connection to wifi ( spectrum service ) , but usually after several loops re - entering password , connection would be re - established .\n[{'aspect': 'wifi', 'opinion': 'trouble', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\ntext: The view is breathtaking the service is top notch ... the ambiance is wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe view is breathtaking the service is top notch ... the ambiance is wonderful .\n->", + "output": "{\"text\": \"The view is breathtaking the service is top notch ... the ambiance is wonderful .\", \"labels\": \"[{'aspect': 'view', 'opinion': 'breathtaking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For appetizers , I recommend the shrimp fritters and dumplings .\n->For appetizers , I recommend the shrimp fritters and dumplings .\n[{'aspect': 'shrimp fritters', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n->for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\ntext: The staff offers impeccable service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff offers impeccable service .\n->", + "output": "{\"text\": \"The staff offers impeccable service .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i found the food to be outstanding , particulary the salmon dish i had .\n->i found the food to be outstanding , particulary the salmon dish i had .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon dish', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: lo and behold , i hadn ' t read quite all of the 1 * reviews : the worst was a teacher who had ordered so many for her several classes and said ` ` i think the problem is with the motherboard .\n->lo and behold , i hadn ' t read quite all of the 1 * reviews : the worst was a teacher who had ordered so many for her several classes and said ` ` i think the problem is with the motherboard .\n[{'aspect': 'motherboard', 'opinion': 'worst', 'polarity': 'negative', 'category': 'MOTHERBOARD#QUALITY'}]\ntext: My boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\n->", + "output": "{\"text\": \"My boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\", \"labels\": \"[{'aspect': 'New England Chowder', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Lobster Bisque', 'opinion': 'award', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , the batterly life that is reported by industry affiliates is way off .\n->also , the batterly life that is reported by industry affiliates is way off .\n[{'aspect': 'batterly life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: The shrimp scampi was excellent and the antipasti were plentiful .\n->The shrimp scampi was excellent and the antipasti were plentiful .\n[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My boyfriend had Prime Rib it was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy boyfriend had Prime Rib it was good .\n->", + "output": "{\"text\": \"My boyfriend had Prime Rib it was good .\", \"labels\": \"[{'aspect': 'Prime Rib', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n->in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Staffs are not that friendly , but the taste covers all .\n->Staffs are not that friendly , but the taste covers all .\n[{'aspect': 'Staffs', 'opinion': 'not that friendly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'taste', 'opinion': 'covers all', 'polarity': 'positive', 'category': 'NULL'}]\ntext: If you like spicy food get the chicken vindaloo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you like spicy food get the chicken vindaloo .\n->", + "output": "{\"text\": \"If you like spicy food get the chicken vindaloo .\", \"labels\": \"[{'aspect': 'chicken vindaloo', 'opinion': 'get', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The entire dining experience was wonderful !\n->The entire dining experience was wonderful !\n[{'aspect': 'dining experience', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n->Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n[{'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Go to Volare for 1st class service and terrific food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGo to Volare for 1st class service and terrific food .\n->", + "output": "{\"text\": \"Go to Volare for 1st class service and terrific food .\", \"labels\": \"[{'aspect': 'service', 'opinion': '1st class', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it 's the only place you can get yummy authentic japanese comfort food .\n->it 's the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food is decent .\n->the food is decent .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The portions are large and the servers always surprise us with a different starter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portions are large and the servers always surprise us with a different starter .\n->", + "output": "{\"text\": \"The portions are large and the servers always surprise us with a different starter .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n->i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n[{'aspect': 'chrome os', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'simple', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: we ate at this thai place following the reviews but very unhappy with the foods .\n->we ate at this thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The wine list is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is excellent .\n->", + "output": "{\"text\": \"The wine list is excellent .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: always great service !\n->always great service !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - system settings could be more robust and better organized\n->- system settings could be more robust and better organized\n[{'aspect': 'system settings', 'opinion': 'robust', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'system settings', 'opinion': 'better', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}]\ntext: The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->", + "output": "{\"text\": \"The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food is great .\n->food is great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i ca n ' t wait to go back .\n->i ca n ' t wait to go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n->", + "output": "{\"text\": \"The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only thing i ' d change would be the hard drive .\n->only thing i ' d change would be the hard drive .\n[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: My husband and I enjoyed each of the 6 taste size portions and left completely full .\n->My husband and I enjoyed each of the 6 taste size portions and left completely full .\n[{'aspect': 'portions', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\n->", + "output": "{\"text\": \"The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'prixe fixe tasting menu', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when it does run it runs great .\n->when it does run it runs great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Every course was better than the next .\n->Every course was better than the next .\n[{'aspect': 'course', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWith the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->", + "output": "{\"text\": \"With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'lemon salad', 'opinion': 'exception', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Personal pans are the perfect size for those hungry nights .\n->Personal pans are the perfect size for those hungry nights .\n[{'aspect': 'Personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu is very limited - i think we counted 4 or 5 entrees .\n->", + "output": "{\"text\": \"The menu is very limited - i think we counted 4 or 5 entrees .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: what ' s not to like , it ' s an amazing machine .\n->what ' s not to like , it ' s an amazing machine .\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->", + "output": "{\"text\": \"We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\", \"labels\": \"[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery life is outstanding and the quality is great .\n->the battery life is outstanding and the quality is great .\n[{'aspect': 'battery life', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: it ' s fast at processing , fast for web browsing and has a quick startup .\n->it ' s fast at processing , fast for web browsing and has a quick startup .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n->", + "output": "{\"text\": \"The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'diner-ish', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: go to volare for 1st class service and terrific food .\n->go to volare for 1st class service and terrific food .\n[{'aspect': 'service', 'opinion': '1st class', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i love my laptop but the battery life is the worst i ' ve ever had on a laptop .\n->i love my laptop but the battery life is the worst i ' ve ever had on a laptop .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'worst', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: This place is so much fun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is so much fun .\n->", + "output": "{\"text\": \"This place is so much fun .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very disappointed and i have n ' t even had the product for 12 hours .\n->very disappointed and i have n ' t even had the product for 12 hours .\n[{'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Our family never expected such incredible entertainment in a restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur family never expected such incredible entertainment in a restaurant .\n->", + "output": "{\"text\": \"Our family never expected such incredible entertainment in a restaurant .\", \"labels\": \"[{'aspect': 'entertainment', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the key board and mouse pad are not very sensitive .\n->the key board and mouse pad are not very sensitive .\n[{'aspect': 'key board', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mouse pad', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n[{'aspect': 'appetizer selection', 'opinion': 'stinks', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The staff was the friendliest that have seen in New York .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff was the friendliest that have seen in New York .\n->", + "output": "{\"text\": \"The staff was the friendliest that have seen in New York .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer ' s customer service is by far the worst .\n->acer ' s customer service is by far the worst .\n[{'aspect': \"acer ' s customer service\", 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: however , it does feel like a sturdy hinge .\n->however , it does feel like a sturdy hinge .\n[{'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: If you want something really different than try Jekyll and Hyde .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you want something really different than try Jekyll and Hyde .\n->", + "output": "{\"text\": \"If you want something really different than try Jekyll and Hyde .\", \"labels\": \"[{'aspect': 'Jekyll and Hyde', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality on this laptop is awesome for the price .\n->the build quality on this laptop is awesome for the price .\n[{'aspect': 'build quality', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n->other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: The food was pretty tradional but it was hot and good with large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was pretty tradional but it was hot and good with large portions .\n->", + "output": "{\"text\": \"The food was pretty tradional but it was hot and good with large portions .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tradional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: superb quality , looks and feels like apple , keyboard is great , the touchpad is flawless and the screen is brilliant , battery life is great too .\n->superb quality , looks and feels like apple , keyboard is great , the touchpad is flawless and the screen is brilliant , battery life is great too .\n[{'aspect': 'quality', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'flawless', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: The place is a lot of fun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is a lot of fun .\n->", + "output": "{\"text\": \"The place is a lot of fun .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n->i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n[{'aspect': 'battery', 'opinion': 'better', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: when you add it all together , it just does n ' t seem worth it to me . . . especially considering the prices .\n->when you add it all together , it just does n ' t seem worth it to me . . . especially considering the prices .\n[{'aspect': 'NULL', 'opinion': \"does n ' t seem worth\", 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': \"does n ' t seem worth\", 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: The characters really make for an enjoyable experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe characters really make for an enjoyable experience .\n->", + "output": "{\"text\": \"The characters really make for an enjoyable experience .\", \"labels\": \"[{'aspect': 'characters', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is heavy , but that is to be expected with a laptop like this one .\n->it is heavy , but that is to be expected with a laptop like this one .\n[{'aspect': 'laptop', 'opinion': 'heavy', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the fans did not turn on loudly if at all .\n->the fans did not turn on loudly if at all .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: However , I think Jeckll and Hydes t is one of those places that is fun to do once .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , I think Jeckll and Hydes t is one of those places that is fun to do once .\n->", + "output": "{\"text\": \"However , I think Jeckll and Hydes t is one of those places that is fun to do once .\", \"labels\": \"[{'aspect': 'Jeckll and Hydes', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there ' s another girl who i ca n ' t describe , she is about 5 ' 6 ' ' with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you ' re * really * talking about .\n->there ' s another girl who i ca n ' t describe , she is about 5 ' 6 ' ' with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you ' re * really * talking about .\n[{'aspect': 'girl', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'place', 'opinion': 'exceeded my expectations', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Service was slow had to wait to order and get food although not crowded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was slow had to wait to order and get food although not crowded .\n->", + "output": "{\"text\": \"Service was slow had to wait to order and get food although not crowded .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n->We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n[{'aspect': 'scenery', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner reservations', 'opinion': 'early', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and even with it ' s pub atmosphere they were great to my kids too !\n->and even with it ' s pub atmosphere they were great to my kids too !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: Drinks way over priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDrinks way over priced .\n->", + "output": "{\"text\": \"Drinks way over priced .\", \"labels\": \"[{'aspect': 'Drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer has been running the programs such as matlab , mathematics , diamond , among others without problem .\n->the computer has been running the programs such as matlab , mathematics , diamond , among others without problem .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this place has totally weird decor , stairs going up with mirrored walls - i am surprised how no one yet broke their head or fall off the stairs - mirrored walls make you dizzy and delusional . . .\n->this place has totally weird decor , stairs going up with mirrored walls - i am surprised how no one yet broke their head or fall off the stairs - mirrored walls make you dizzy and delusional . . .\n[{'aspect': 'decor', 'opinion': 'weird', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'mirrored walls', 'opinion': 'dizzy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'mirrored walls', 'opinion': 'delusional', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: Food was good not great not worth the wait or another visit\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was good not great not worth the wait or another visit\n->", + "output": "{\"text\": \"Food was good not great not worth the wait or another visit\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good not great not worth the wait or another visit', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend the samsung chromebook for browsing the internet , and for note taking .\n->i highly recommend the samsung chromebook for browsing the internet , and for note taking .\n[{'aspect': 'samsung chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n->The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great pizza for lunch place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat pizza for lunch place .\n->", + "output": "{\"text\": \"Great pizza for lunch place .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n->as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n->at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Service was quick .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was quick .\n->", + "output": "{\"text\": \"Service was quick .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was okay , nothing great .\n->Food was okay , nothing great .\n[{'aspect': 'Food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i ' ve had the laptop for a full day now and i can say it is quite impressive .\n->i ' ve had the laptop for a full day now and i can say it is quite impressive .\n[{'aspect': 'laptop', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The pizza was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza was great .\n->", + "output": "{\"text\": \"The pizza was great .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: noodles with shrimp and chicken and coconut juice is the MUST !\n->noodles with shrimp and chicken and coconut juice is the MUST !\n[{'aspect': 'noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Bagels are ok , but be sure not to make any special requests !\n->Bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n->", + "output": "{\"text\": \"Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard is pretty damn good and the track pad is fair .\n->keyboard is pretty damn good and the track pad is fair .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'track pad', 'opinion': 'fair', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: it began to shut down and restart all on it ' s own - continuously .\n->it began to shut down and restart all on it ' s own - continuously .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Delivery is fast too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDelivery is fast too .\n->", + "output": "{\"text\": \"Delivery is fast too .\", \"labels\": \"[{'aspect': 'Delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n->my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n[{'aspect': 'place', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the battery life is really pretty comparable ( slightly more with the mbp , and i can obtain info on app energy usage a bit easier imo ) .\n->the battery life is really pretty comparable ( slightly more with the mbp , and i can obtain info on app energy usage a bit easier imo ) .\n[{'aspect': 'battery life', 'opinion': 'comparable', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->", + "output": "{\"text\": \"Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n->thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n->although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEss-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n->", + "output": "{\"text\": \"Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent product , feels like quality all the way around .\n->excellent product , feels like quality all the way around .\n[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n->everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n[{'aspect': 'zucchero pomodori', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->", + "output": "{\"text\": \"The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n->Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - retina display is nice but not mind blowing as other have suggested\n->- retina display is nice but not mind blowing as other have suggested\n[{'aspect': 'retina display', 'opinion': 'nice', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\ntext: They have a huge selection of different cream cheeses and all of their salads are great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey have a huge selection of different cream cheeses and all of their salads are great .\n->", + "output": "{\"text\": \"They have a huge selection of different cream cheeses and all of their salads are great .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n->I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n[{'aspect': 'lamb chop', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: she had no trouble learning how to use it .\n->she had no trouble learning how to use it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: The lox is always fresh too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe lox is always fresh too .\n->", + "output": "{\"text\": \"The lox is always fresh too .\", \"labels\": \"[{'aspect': 'lox', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the staff is also young , energeic and hot ! ! ! !\n->And the staff is also young , energeic and hot ! ! ! !\n[{'aspect': 'staff', 'opinion': 'young', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'energeic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery is okay .\n->battery is okay .\n[{'aspect': 'battery', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'BATTERY#GENERAL'}]\ntext: Not impressed with the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot impressed with the food .\n->", + "output": "{\"text\": \"Not impressed with the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there ' s nothing i do n ' t like about this device , so in order of importance , here ' s what i love about it .\n->there ' s nothing i do n ' t like about this device , so in order of importance , here ' s what i love about it .\n[{'aspect': 'device', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n->the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n[{'aspect': 'decor', 'opinion': 'diner - ish', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\ntext: Zero ambiance to boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nZero ambiance to boot .\n->", + "output": "{\"text\": \"Zero ambiance to boot .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'Zero', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the os doesn ' t leave menu bar at the top for copying in programs for studies .\n->the os doesn ' t leave menu bar at the top for copying in programs for studies .\n[{'aspect': 'os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: first it took us a long time to find the place .\n->first it took us a long time to find the place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: I thought this place was totally overrated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI thought this place was totally overrated .\n->", + "output": "{\"text\": \"I thought this place was totally overrated .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n->The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n[{'aspect': 'hanger steak', 'opinion': 'rubber', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tuna', 'opinion': 'flavorless', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the one star is for warranty support .\n->the one star is for warranty support .\n[{'aspect': 'warranty support', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: The ambience was nice , but service was n't so great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ambience was nice , but service was n't so great .\n->", + "output": "{\"text\": \"The ambience was nice , but service was n't so great .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': \"was n't so great\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n->One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n[{'aspect': 'waiter', 'opinion': 'snobby', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: have the iced tea .\n->have the iced tea .\n[{'aspect': 'iced tea', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: This is the BEST Shabu-Shabu Restaurant in the Try-State Area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the BEST Shabu-Shabu Restaurant in the Try-State Area .\n->", + "output": "{\"text\": \"This is the BEST Shabu-Shabu Restaurant in the Try-State Area .\", \"labels\": \"[{'aspect': 'Shabu-Shabu Restaurant', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazing !\n->amazing !\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i have yet to update the drives on it or try a game but so far its a good machine .\n->i have yet to update the drives on it or try a game but so far its a good machine .\n[{'aspect': 'machine', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->", + "output": "{\"text\": \"The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m no expert on screens but i personally think the panel looks very nice .\n->i ' m no expert on screens but i personally think the panel looks very nice .\n[{'aspect': 'panel', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: service was also very good .\n->service was also very good .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The owner and staff are all Japanese as well and that adds to the entire ambiance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe owner and staff are all Japanese as well and that adds to the entire ambiance .\n->", + "output": "{\"text\": \"The owner and staff are all Japanese as well and that adds to the entire ambiance .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'adds', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the notebook is very well built and it could easily pass as a high - end machine .\n->the notebook is very well built and it could easily pass as a high - end machine .\n[{'aspect': 'notebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'machine', 'opinion': 'high - end', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: very good laptop\n->very good laptop\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Taxan delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTaxan delicious !\n->", + "output": "{\"text\": \"Taxan delicious !\", \"labels\": \"[{'aspect': 'Taxan', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the large screen and integral numeric pad are exactly what i need to easily process documents .\n->the large screen and integral numeric pad are exactly what i need to easily process documents .\n[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'integral numeric pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: atmosphere is nice and relaxed too . . .\n->atmosphere is nice and relaxed too . . .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: Try green curry with vegetables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry green curry with vegetables .\n->", + "output": "{\"text\": \"Try green curry with vegetables .\", \"labels\": \"[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mazing interior .\n->mazing interior .\n[{'aspect': 'interior', 'opinion': 'mazing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the keyboard is nice .\n->the keyboard is nice .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The quantity is also very good , you will come out satisfied .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe quantity is also very good , you will come out satisfied .\n->", + "output": "{\"text\": \"The quantity is also very good , you will come out satisfied .\", \"labels\": \"[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen maximum brightness is still not bright enough\n->screen maximum brightness is still not bright enough\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: leon is an east village gem : casual but hip , with well prepared basic french bistro fare , good specials , a warm and lively atmosphere .\n->leon is an east village gem : casual but hip , with well prepared basic french bistro fare , good specials , a warm and lively atmosphere .\n[{'aspect': 'leon', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'leon', 'opinion': 'hip', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'lively', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'french bistro fare', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The service is ok , some of the people did n't get what they asked for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is ok , some of the people did n't get what they asked for .\n->", + "output": "{\"text\": \"The service is ok , some of the people did n't get what they asked for .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great atmosphere\n->great atmosphere\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: We had the lobster sandwich and it was FANTASTIC .\n->We had the lobster sandwich and it was FANTASTIC .\n[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I had the best ravioli ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had the best ravioli ever .\n->", + "output": "{\"text\": \"I had the best ravioli ever .\", \"labels\": \"[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place has great indian chinese food .\n->this place has great indian chinese food .\n[{'aspect': 'indian chinese food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i am actually offended to have spent so much money on such a bad experience .\n->i am actually offended to have spent so much money on such a bad experience .\n[{'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: The wine the service was very good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine the service was very good too .\n->", + "output": "{\"text\": \"The wine the service was very good too .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good sound quality\n->good sound quality\n[{'aspect': 'sound quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: Great atmoshere and worth every bit .\n->Great atmoshere and worth every bit .\n[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This quaint and romantic trattoria is at the top of my Manhattan restaurant list .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis quaint and romantic trattoria is at the top of my Manhattan restaurant list .\n->", + "output": "{\"text\": \"This quaint and romantic trattoria is at the top of my Manhattan restaurant list .\", \"labels\": \"[{'aspect': 'trattoria', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'trattoria', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n->but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n[{'aspect': 'windows 10', 'opinion': 'worst', 'polarity': 'negative', 'category': 'OS#GENERAL'}, {'aspect': 'windows 10', 'opinion': 'awful', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: that ' s how confident i am in the asus after 10 days .\n->that ' s how confident i am in the asus after 10 days .\n[{'aspect': 'asus', 'opinion': 'confident', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\n->", + "output": "{\"text\": \"The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'regular menu-fare', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'regular menu-fare', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will be returning this item .\n->i will be returning this item .\n[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlthough the tables may be closely situated , the candle-light , food quality and service overcompensate .\n->", + "output": "{\"text\": \"Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\", \"labels\": \"[{'aspect': 'candle-light', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'closely situated', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * the screen is more than adequate for me , although i have not used it outside much yet .\n->* the screen is more than adequate for me , although i have not used it outside much yet .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: All of the pizzas are terrific and the price is even better !\n->All of the pizzas are terrific and the price is even better !\n[{'aspect': 'pizzas', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: If you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .\n->", + "output": "{\"text\": \"If you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .\", \"labels\": \"[{'aspect': 'marinara/arrabiatta sauce', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'marinara/arrabiatta sauce', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mozzarella en Carozza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good drink .\n->Good drink .\n[{'aspect': 'drink', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The design and atmosphere is just as good .\n->The design and atmosphere is just as good .\n[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Check out the secret back room .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCheck out the secret back room .\n->", + "output": "{\"text\": \"Check out the secret back room .\", \"labels\": \"[{'aspect': 'back room', 'opinion': 'secret', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the perfect date spot for williamsburg couples .\n->this is the perfect date spot for williamsburg couples .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n->on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n[{'aspect': 'trackpad', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: The food was authentic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was authentic .\n->", + "output": "{\"text\": \"The food was authentic .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lucky strike is a great casual place to just grab a bite to eat .\n->lucky strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'lucky strike', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'lucky strike', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n->very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'sound', 'opinion': 'big', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'boot times', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Who has room for Cheesesticks with the best pizza in NYC !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWho has room for Cheesesticks with the best pizza in NYC !\n->", + "output": "{\"text\": \"Who has room for Cheesesticks with the best pizza in NYC !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n->if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n[{'aspect': 'bottle', 'opinion': 'love', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}, {'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: screen not aligned perfectly .\n->screen not aligned perfectly .\n[{'aspect': 'screen', 'opinion': 'not aligned perfectly', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: Always great service !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlways great service !\n->", + "output": "{\"text\": \"Always great service !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the computer is 1 pound ( approx .\n->- the computer is 1 pound ( approx .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: LOVE THIS PLACE .\n->LOVE THIS PLACE .\n[{'aspect': 'PLACE', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is good , I ca n't lie .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is good , I ca n't lie .\n->", + "output": "{\"text\": \"The food is good , I ca n't lie .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: we love the food , drinks , and atmosphere !\n->we love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The hostess and the waitress were incredibly rude and did everything they could to rush us out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe hostess and the waitress were incredibly rude and did everything they could to rush us out .\n->", + "output": "{\"text\": \"The hostess and the waitress were incredibly rude and did everything they could to rush us out .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no back light keyboard\n->no back light keyboard\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: This is some really good , inexpensive sushi .\n->This is some really good , inexpensive sushi .\n[{'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Amma is nothing special .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmma is nothing special .\n->", + "output": "{\"text\": \"Amma is nothing special .\", \"labels\": \"[{'aspect': 'Amma', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was just OK , at least for what food was available .\n->The food was just OK , at least for what food was available .\n[{'aspect': 'food', 'opinion': 'OK', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: - ips full hd screen .\n->- ips full hd screen .\n[{'aspect': 'hd screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\ntext: I ate here a week ago and found most dishes average at best and too expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ate here a week ago and found most dishes average at best and too expensive .\n->", + "output": "{\"text\": \"I ate here a week ago and found most dishes average at best and too expensive .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'too expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is outstanding and the service is quick , friendly and very professional .\n->The food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place is always packed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is always packed .\n->", + "output": "{\"text\": \"This place is always packed .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: would never go back\n->would never go back\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n->i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Most importantly , food is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMost importantly , food is excellent .\n->", + "output": "{\"text\": \"Most importantly , food is excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what a hassle !\n->what a hassle !\n[{'aspect': 'NULL', 'opinion': 'hassle', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the rig works better than advertised .\n->the rig works better than advertised .\n[{'aspect': 'rig', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand yes Dal Bukhara is so dam good and so are all the kababs .\n->", + "output": "{\"text\": \"and yes Dal Bukhara is so dam good and so are all the kababs .\", \"labels\": \"[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n->The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n[{'aspect': 'parathas', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kebabs', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The atmosphere is unheralded , the service impeccable , and the food magnificant .\n->The atmosphere is unheralded , the service impeccable , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Haru on Park S is simply disgusting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHaru on Park S is simply disgusting .\n->", + "output": "{\"text\": \"Haru on Park S is simply disgusting .\", \"labels\": \"[{'aspect': 'Haru on Park S', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n->but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n[{'aspect': 'battery', 'opinion': 'erroneous', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'way too sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'cover / lid', 'opinion': 'cheaply', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: when we sat , we got great and fast service .\n->when we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The fish was not fresh and the rice tasted old and stale .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fish was not fresh and the rice tasted old and stale .\n->", + "output": "{\"text\": \"The fish was not fresh and the rice tasted old and stale .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service - friendly and attentive .\n->service - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: fast shipping too !\n->fast shipping too !\n[{'aspect': 'shipping', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\ntext: Quite frankly , this is some of the worst sushi I have ever tried .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nQuite frankly , this is some of the worst sushi I have ever tried .\n->", + "output": "{\"text\": \"Quite frankly , this is some of the worst sushi I have ever tried .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have mixed feelings about this acer chromebook .\n->i have mixed feelings about this acer chromebook .\n[{'aspect': 'acer chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n->The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'not over-bearing or rushed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: honestly the worst sushi my husband and i had in our entire lives .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhonestly the worst sushi my husband and i had in our entire lives .\n->", + "output": "{\"text\": \"honestly the worst sushi my husband and i had in our entire lives .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the pad thai , it ' s fabulous and their prices are so cheap !\n->try the pad thai , it ' s fabulous and their prices are so cheap !\n[{'aspect': 'pad thai', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'so cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: loses wifi connection every hour .\n->loses wifi connection every hour .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: not sure why this restaurant would be rated that highly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot sure why this restaurant would be rated that highly .\n->", + "output": "{\"text\": \"not sure why this restaurant would be rated that highly .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'highly', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you do n ' t mind pre - sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n->if you do n ' t mind pre - sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n[{'aspect': 'fish', 'opinion': 'low quality', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'sushi chef', 'opinion': 'miserable', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n->update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n[{'aspect': 'keyboard cover', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: the all-u-can-eat sushi is definitely in very poor quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe all-u-can-eat sushi is definitely in very poor quality .\n->", + "output": "{\"text\": \"the all-u-can-eat sushi is definitely in very poor quality .\", \"labels\": \"[{'aspect': 'all-u-can-eat sushi', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the audio for this laptop was poorly planned out .\n->- the audio for this laptop was poorly planned out .\n[{'aspect': 'audio for this laptop', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: prices are in line .\n->prices are in line .\n[{'aspect': 'NULL', 'opinion': 'in line', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\ntext: the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n->", + "output": "{\"text\": \"the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\", \"labels\": \"[{'aspect': 'soy sauce', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'vinegar-soaked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: dessert : pure disaster .\n->dessert : pure disaster .\n[{'aspect': 'dessert', 'opinion': 'disaster', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my girlfriend works from home with it and has no problems at all to do online classes with it .\n->my girlfriend works from home with it and has no problems at all to do online classes with it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the waitstaffs are nice though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waitstaffs are nice though .\n->", + "output": "{\"text\": \"the waitstaffs are nice though .\", \"labels\": \"[{'aspect': 'waitstaffs', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the veal and the mushrooms were cooked perfectly .\n->the veal and the mushrooms were cooked perfectly .\n[{'aspect': 'veal', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mushrooms', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: buy it for school , buy it for home , buy on for the grandkids .\n->buy it for school , buy it for home , buy on for the grandkids .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I have been to Roth 's twice and both times were very disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have been to Roth 's twice and both times were very disappointing .\n->", + "output": "{\"text\": \"I have been to Roth 's twice and both times were very disappointing .\", \"labels\": \"[{'aspect': \"Roth 's\", 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I absolutely love this place ! ! !\n->I absolutely love this place ! ! !\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i bought this laptop for software development .\n->i bought this laptop for software development .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: Both times I was extremely dissappointed by the service , which was boarderline rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBoth times I was extremely dissappointed by the service , which was boarderline rude .\n->", + "output": "{\"text\": \"Both times I was extremely dissappointed by the service , which was boarderline rude .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'dissappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i call acer support and after an hour they can not help me .\n->i call acer support and after an hour they can not help me .\n[{'aspect': 'acer support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n->pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n[{'aspect': 'flip functions', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: The dinner was ok , nothing I would have again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dinner was ok , nothing I would have again .\n->", + "output": "{\"text\": \"The dinner was ok , nothing I would have again .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'ok', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tapping it on either end is hit or miss .\n->tapping it on either end is hit or miss .\n[{'aspect': 'tapping', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i don ' t really have anywhere to rest my hand when i use it because it ' s so large .\n->i don ' t really have anywhere to rest my hand when i use it because it ' s so large .\n[{'aspect': 'NULL', 'opinion': 'large', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: I had their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .\n->", + "output": "{\"text\": \"I had their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .\", \"labels\": \"[{'aspect': 'eggs benedict', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was okay , nothing great .\n->food was okay , nothing great .\n[{'aspect': 'food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this is a great machine in many ways - - and with crouton could run full ubuntu , which made it a great little machine to write code on and deploy to a server .\n->this is a great machine in many ways - - and with crouton could run full ubuntu , which made it a great little machine to write code on and deploy to a server .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The service was attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was attentive .\n->", + "output": "{\"text\": \"The service was attentive .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their pad penang is delicious and everything else is fantastic .\n->Their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but i ' m growing ever more disenchanted with the core m3 processing speed .\n->but i ' m growing ever more disenchanted with the core m3 processing speed .\n[{'aspect': 'core m3', 'opinion': 'disenchanted', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: Planet Thai is great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPlanet Thai is great !\n->", + "output": "{\"text\": \"Planet Thai is great !\", \"labels\": \"[{'aspect': 'Planet Thai', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: typing is responsive , the touchescreen is a joy and it ' s fast .\n->typing is responsive , the touchescreen is a joy and it ' s fast .\n[{'aspect': 'typing', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchescreen', 'opinion': 'joy', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this by far one of the best laptops i ' ve ever purchased .\n->this by far one of the best laptops i ' ve ever purchased .\n[{'aspect': 'laptops', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: We love the food , drinks , and atmosphere !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe love the food , drinks , and atmosphere !\n->", + "output": "{\"text\": \"We love the food , drinks , and atmosphere !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n->If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n[{'aspect': 'meal', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: everything was good for a few days after receiving the product .\n->everything was good for a few days after receiving the product .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->", + "output": "{\"text\": \"The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\", \"labels\": \"[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i received this laptop promptly .\n->i received this laptop promptly .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: the all-u-can-eat sushi is definitely in very poor quality .\n->the all-u-can-eat sushi is definitely in very poor quality .\n[{'aspect': 'all-u-can-eat sushi', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Try the Pad Thai , it 's fabulous and their prices are so cheap !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the Pad Thai , it 's fabulous and their prices are so cheap !\n->", + "output": "{\"text\": \"Try the Pad Thai , it 's fabulous and their prices are so cheap !\", \"labels\": \"[{'aspect': 'Pad Thai', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n->The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n[{'aspect': 'menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Chilean Sea Bass', 'opinion': 'except', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n->while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n[{'aspect': 'NULL', 'opinion': 'uncourteous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Just because it 's cheap does NOT mean the portions are small or the food is nasty , IT IS GREAT !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nJust because it 's cheap does NOT mean the portions are small or the food is nasty , IT IS GREAT !\n->", + "output": "{\"text\": \"Just because it 's cheap does NOT mean the portions are small or the food is nasty , IT IS GREAT !\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'nasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagels are also reasonably priced for NYC .\n->The bagels are also reasonably priced for NYC .\n[{'aspect': 'bagels', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I was very impressed by this low-key upper eastsider and their authentically thai cuisine ! ! !\n->I was very impressed by this low-key upper eastsider and their authentically thai cuisine ! ! !\n[{'aspect': 'thai cuisine', 'opinion': 'authentically', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Eating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\n->", + "output": "{\"text\": \"Eating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'saves', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s just average , just shredded , no seasoning on it .\n->it ' s just average , just shredded , no seasoning on it .\n[{'aspect': 'NULL', 'opinion': 'average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: more importantly i appreciate the uncanny speed to boot up or wake up .\n->more importantly i appreciate the uncanny speed to boot up or wake up .\n[{'aspect': 'speed', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Chennai Garden is my favorite Indian restaurant in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nChennai Garden is my favorite Indian restaurant in the city .\n->", + "output": "{\"text\": \"Chennai Garden is my favorite Indian restaurant in the city .\", \"labels\": \"[{'aspect': 'Chennai Garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They 've the best desserts and mixed drinks as well as snack foods .\n->They 've the best desserts and mixed drinks as well as snack foods .\n[{'aspect': 'desserts', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mixed drinks', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'snack foods', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: ( that is a must , but not every restaurant can do . . . )\n->( that is a must , but not every restaurant can do . . . )\n[{'aspect': 'NULL', 'opinion': 'must', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: They have authentic Indian at amazin prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey have authentic Indian at amazin prices .\n->", + "output": "{\"text\": \"They have authentic Indian at amazin prices .\", \"labels\": \"[{'aspect': 'Indian', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n->while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n[{'aspect': 'NULL', 'opinion': 'uncourteous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the good news is that the android features ( google play store apps ) work nearly across the board .\n->the good news is that the android features ( google play store apps ) work nearly across the board .\n[{'aspect': 'android features', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: It 's a rather cramped and busy restaurant and it closes early .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's a rather cramped and busy restaurant and it closes early .\n->", + "output": "{\"text\": \"It 's a rather cramped and busy restaurant and it closes early .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n->original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n[{'aspect': 'screen', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: Faan 's got a great concept but a little rough on the delivery .\n->Faan 's got a great concept but a little rough on the delivery .\n[{'aspect': 'delivery', 'opinion': 'rough', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Patroon features a nice cigar bar and has great staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPatroon features a nice cigar bar and has great staff .\n->", + "output": "{\"text\": \"Patroon features a nice cigar bar and has great staff .\", \"labels\": \"[{'aspect': 'cigar bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do not buy this machine if you ' re hoping to run android apps .\n->do not buy this machine if you ' re hoping to run android apps .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n->it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The food is tasty and portion sizes are appropriate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is tasty and portion sizes are appropriate .\n->", + "output": "{\"text\": \"The food is tasty and portion sizes are appropriate .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well macbook is no less than expected .\n->well macbook is no less than expected .\n[{'aspect': 'macbook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: a weakness is the chicken in the salads .\n->a weakness is the chicken in the salads .\n[{'aspect': 'chicken in the salads', 'opinion': 'weakness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: This is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\n->", + "output": "{\"text\": \"This is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a nice place to relax and have conversation .\n->it ' s a nice place to relax and have conversation .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: as for actual device it is completely gorgeous and ( now ) works flawlessly .\n->as for actual device it is completely gorgeous and ( now ) works flawlessly .\n[{'aspect': 'device', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Not a great place for family or general dining .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot a great place for family or general dining .\n->", + "output": "{\"text\": \"Not a great place for family or general dining .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'Not a great', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n->other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: LOVE THIS PLACE .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLOVE THIS PLACE .\n->", + "output": "{\"text\": \"LOVE THIS PLACE .\", \"labels\": \"[{'aspect': 'PLACE', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer can not repair whatever disc issues it has .\n->the computer can not repair whatever disc issues it has .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n->the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n[{'aspect': 'boths', 'opinion': 'not as small', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'boths', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: Food is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is excellent .\n->", + "output": "{\"text\": \"Food is excellent .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff offers impeccable service .\n->The staff offers impeccable service .\n[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: however , the bluetooth is a nightmare .\n->however , the bluetooth is a nightmare .\n[{'aspect': 'bluetooth', 'opinion': 'nightmare', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: Fish is so very fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFish is so very fresh .\n->", + "output": "{\"text\": \"Fish is so very fresh .\", \"labels\": \"[{'aspect': 'Fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is my only device with this issue in my home .\n->it is my only device with this issue in my home .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n->Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n[{'aspect': 'dim sum', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Love YUKA .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLove YUKA .\n->", + "output": "{\"text\": \"Love YUKA .\", \"labels\": \"[{'aspect': 'YUKA', 'opinion': 'Love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other than that it has been great so far !\n->other than that it has been great so far !\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: finally , there are the ports .\n->finally , there are the ports .\n[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: Mermaid Inn is an overall good restaurant with really good seafood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMermaid Inn is an overall good restaurant with really good seafood .\n->", + "output": "{\"text\": \"Mermaid Inn is an overall good restaurant with really good seafood .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Mermaid Inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is very attentive and we can almost always get a table .\n->The staff is very attentive and we can almost always get a table .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n->We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n[{'aspect': 'Gulab Jamun ( dessert )', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The menu is limited but almost all of the dishes are excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu is limited but almost all of the dishes are excellent .\n->", + "output": "{\"text\": \"The menu is limited but almost all of the dishes are excellent .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : nice size , clear screen , quick on start up , very functional and easy to use .\n->pros : nice size , clear screen , quick on start up , very functional and easy to use .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: We thought that this place is using too much of MSG cooking in the foods .\n->We thought that this place is using too much of MSG cooking in the foods .\n[{'aspect': 'MSG cooking', 'opinion': 'too much', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n->", + "output": "{\"text\": \"The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\", \"labels\": \"[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spaghetti with Scallops and Shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sandwiches are dry , tasteless and way overpriced .\n->The sandwiches are dry , tasteless and way overpriced .\n[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n->by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: The service is good and ambience is good for a date or group outing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is good and ambience is good for a date or group outing .\n->", + "output": "{\"text\": \"The service is good and ambience is good for a date or group outing .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is plenty big and the visual very nice .\n->the screen is plenty big and the visual very nice .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: it ' s one of the worst laptops i ' ve ever had .\n->it ' s one of the worst laptops i ' ve ever had .\n[{'aspect': 'laptops', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The only fallback on this restaurant is the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only fallback on this restaurant is the prices .\n->", + "output": "{\"text\": \"The only fallback on this restaurant is the prices .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve used this daily for nearly eight months and have been very happy with .\n->i ' ve used this daily for nearly eight months and have been very happy with .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it has non existent boot times and updates are easy .\n->it has non existent boot times and updates are easy .\n[{'aspect': 'boot times', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'updates', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: Even though its good seafood , the prices are too high .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven though its good seafood , the prices are too high .\n->", + "output": "{\"text\": \"Even though its good seafood , the prices are too high .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Also good for client lunch meetings , esp .\n->Also good for client lunch meetings , esp .\n[{'aspect': 'lunch meetings', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it ' s exactly as i wanted it\n->it ' s exactly as i wanted it\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Truly the mark of an attentive waiter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTruly the mark of an attentive waiter .\n->", + "output": "{\"text\": \"Truly the mark of an attentive waiter .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is a lot of variety even for people who eat vegetarian like me .\n->there is a lot of variety even for people who eat vegetarian like me .\n[{'aspect': 'NULL', 'opinion': 'a lot of variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: Haru on Park S is simply disgusting .\n->Haru on Park S is simply disgusting .\n[{'aspect': 'Haru on Park S', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I highly recommend the restaurant based on our experience last night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend the restaurant based on our experience last night .\n->", + "output": "{\"text\": \"I highly recommend the restaurant based on our experience last night .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so again , the battery is surprisingly great .\n->so again , the battery is surprisingly great .\n[{'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: the lava cake dessert was incredible and i recommend it .\n->the lava cake dessert was incredible and i recommend it .\n[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ate at this Thai place following the reviews but very unhappy with the foods .\n->", + "output": "{\"text\": \"We ate at this Thai place following the reviews but very unhappy with the foods .\", \"labels\": \"[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n->i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this place is a must visit !\n->this place is a must visit !\n[{'aspect': 'place', 'opinion': 'must visit', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n->", + "output": "{\"text\": \"I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\", \"labels\": \"[{'aspect': 'jelly fish', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drunken chicken', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soupy dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'stir fry blue crab', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everyone was smiling so that made me feel welcome .\n->everyone was smiling so that made me feel welcome .\n[{'aspect': 'NULL', 'opinion': 'welcome', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Service was very prompt but slightly rushed .\n->Service was very prompt but slightly rushed .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'rushed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is so cheap and the waiters are nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is so cheap and the waiters are nice .\n->", + "output": "{\"text\": \"The food is so cheap and the waiters are nice .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They do n't seem to place an emphasis on specials or fresh ingredients which to me is necessary for good thai .\n->They do n't seem to place an emphasis on specials or fresh ingredients which to me is necessary for good thai .\n[{'aspect': 'ingredients', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'thai', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i highly recommend the samsung chromebook for browsing the internet , and for note taking .\n->i highly recommend the samsung chromebook for browsing the internet , and for note taking .\n[{'aspect': 'samsung chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Have frequented 'ino for several years and the food remains excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHave frequented 'ino for several years and the food remains excellent .\n->", + "output": "{\"text\": \"Have frequented 'ino for several years and the food remains excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this originally a few months back , died within a week .\n->i bought this originally a few months back , died within a week .\n[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the machine is easy to use , snappy , and everything the reviewers say .\n->the machine is easy to use , snappy , and everything the reviewers say .\n[{'aspect': 'machine', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'machine', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Cheese plate is a varied delight and great bargain at $ 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCheese plate is a varied delight and great bargain at $ 10 .\n->", + "output": "{\"text\": \"Cheese plate is a varied delight and great bargain at $ 10 .\", \"labels\": \"[{'aspect': 'Cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n->i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n[{'aspect': 'NULL', 'opinion': 'pleasantly suprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it ' s all about the food ! !\n->it ' s all about the food ! !\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: ( The asparagus , truffle oil , parmesan bruschetta is a winner ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( The asparagus , truffle oil , parmesan bruschetta is a winner ! )\n->", + "output": "{\"text\": \"( The asparagus , truffle oil , parmesan bruschetta is a winner ! )\", \"labels\": \"[{'aspect': 'asparagus , truffle oil , parmesan bruschetta', 'opinion': 'winner', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You will pay a lot for the decore , but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY .\n->You will pay a lot for the decore , but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY .\n[{'aspect': 'decore', 'opinion': 'pay a lot', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'no better or worse', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Big Wong is a great place to eat and fill your stomach .\n->Big Wong is a great place to eat and fill your stomach .\n[{'aspect': 'Big Wong', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Wine list is extensive without being over-priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWine list is extensive without being over-priced .\n->", + "output": "{\"text\": \"Wine list is extensive without being over-priced .\", \"labels\": \"[{'aspect': 'Wine list', 'opinion': 'extensive without being over-priced', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n->it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Frites were delicious if a bit on the thick side .\n->Frites were delicious if a bit on the thick side .\n[{'aspect': 'Frites', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Be sure to try the seasonal , and always delicious , specials .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBe sure to try the seasonal , and always delicious , specials .\n->", + "output": "{\"text\": \"Be sure to try the seasonal , and always delicious , specials .\", \"labels\": \"[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A touch more jalapeno heat for contrast and it would have been very good indeed .\n->A touch more jalapeno heat for contrast and it would have been very good indeed .\n[{'aspect': 'jalapeno', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: linux worked fairly well , and i was pretty pleased with it overall .\n->linux worked fairly well , and i was pretty pleased with it overall .\n[{'aspect': 'linux', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I loved this place ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI loved this place ! !\n->", + "output": "{\"text\": \"I loved this place ! !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The white bean brushetta to start was incredible and the pasta was phenomenal .\n->The white bean brushetta to start was incredible and the pasta was phenomenal .\n[{'aspect': 'white bean brushetta', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service is a little scatty at times but all is forgiven when the food arrives .\n->The service is a little scatty at times but all is forgiven when the food arrives .\n[{'aspect': 'service', 'opinion': 'scatty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'forgiven', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was very good , a great deal , and the place its self was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was very good , a great deal , and the place its self was great .\n->", + "output": "{\"text\": \"The food was very good , a great deal , and the place its self was great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n->The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n[{'aspect': 'waitstaff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'polite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the power _ supply is awesome .\n->the power _ supply is awesome .\n[{'aspect': 'power _ supply is', 'opinion': '.', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n->", + "output": "{\"text\": \"The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'freindly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its just a fun place to go , not a five star restaraunt .\n->its just a fun place to go , not a five star restaraunt .\n[{'aspect': 'restaraunt', 'opinion': 'five star', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: best pastrami i ever had and great portion without being ridiculous .\n->best pastrami i ever had and great portion without being ridiculous .\n[{'aspect': 'pastrami', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->", + "output": "{\"text\": \"I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\", \"labels\": \"[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Cafe Noir', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i go and eat out at many different restaurants and this is one place you have go and try .\n->i go and eat out at many different restaurants and this is one place you have go and try .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The service was attentive , yet discreet .\n->The service was attentive , yet discreet .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n->", + "output": "{\"text\": \"The service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You can get an excellent meal at most of the many Indian restaurants on nearby Lexington Avenue for the cost of one the dainty dishes here .\n->You can get an excellent meal at most of the many Indian restaurants on nearby Lexington Avenue for the cost of one the dainty dishes here .\n[{'aspect': 'meal', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'dainty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you are looking to ditch apple i recommend it and touch screen works great and easy to read movie scripts to take notes .\n->if you are looking to ditch apple i recommend it and touch screen works great and easy to read movie scripts to take notes .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touch screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n->", + "output": "{\"text\": \"The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Cafe Noir', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: gross food \u2013 wow -\n->gross food \u2013 wow -\n[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: yes , it ' s absolute garbage .\n->yes , it ' s absolute garbage .\n[{'aspect': 'NULL', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: Pizza here is consistently good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza here is consistently good .\n->", + "output": "{\"text\": \"Pizza here is consistently good .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n->While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n[{'aspect': 'drinks', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a thoroughly disappointing machine .\n->a thoroughly disappointing machine .\n[{'aspect': 'machine', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Salads are a delicious way to begin the meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSalads are a delicious way to begin the meal .\n->", + "output": "{\"text\": \"Salads are a delicious way to begin the meal .\", \"labels\": \"[{'aspect': 'Salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am much more productive with this machine .\n->i am much more productive with this machine .\n[{'aspect': 'machine', 'opinion': 'productive', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: i picked up the stylus and it fell apart , no drops no damage .\n->i picked up the stylus and it fell apart , no drops no damage .\n[{'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: You should pass on the calamari .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou should pass on the calamari .\n->", + "output": "{\"text\": \"You should pass on the calamari .\", \"labels\": \"[{'aspect': 'calamari', 'opinion': 'pass', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n->Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n[{'aspect': 'candle-light', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'closely situated', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Decor is charming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDecor is charming .\n->", + "output": "{\"text\": \"Decor is charming .\", \"labels\": \"[{'aspect': 'Decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n->to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n[{'aspect': 'power icon', 'opinion': 'dismay', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'power icon', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n->i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n[{'aspect': 'apple support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: What a great place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhat a great place .\n->", + "output": "{\"text\": \"What a great place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there are 2 pretty significant flaws in the design however , one i think might only be on my laptop .\n->there are 2 pretty significant flaws in the design however , one i think might only be on my laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: I have to say I have never had a disapointing meal here .\n->I have to say I have never had a disapointing meal here .\n[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I would definitely recommend SEA if you like thai cuisine !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would definitely recommend SEA if you like thai cuisine !\n->", + "output": "{\"text\": \"I would definitely recommend SEA if you like thai cuisine !\", \"labels\": \"[{'aspect': 'thai cuisine', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was good and food is wonderful .\n->Service was good and food is wonderful .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Ask for Usha , the nicest bartender in manhattan .\n->Ask for Usha , the nicest bartender in manhattan .\n[{'aspect': 'Usha', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->", + "output": "{\"text\": \"I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my laptop is affected by the staingate issue and apple denied responsibility , saying that it ' s a cosmetic problem caused by improper cleaning , yet there are thousands of reports on this .\n->my laptop is affected by the staingate issue and apple denied responsibility , saying that it ' s a cosmetic problem caused by improper cleaning , yet there are thousands of reports on this .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: fantastic computer !\n->fantastic computer !\n[{'aspect': 'computer', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I absolutely Loved this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI absolutely Loved this place .\n->", + "output": "{\"text\": \"I absolutely Loved this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'Loved', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so be forwarded , if you buy this , you are jumping right into a machine that won ' t get updates anymore ( which is something most chromebook owners want and a feature that makes them better than android and it ' s fragmented market ) .\n->so be forwarded , if you buy this , you are jumping right into a machine that won ' t get updates anymore ( which is something most chromebook owners want and a feature that makes them better than android and it ' s fragmented market ) .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: one thing keeps it from getting a five - star rave from me .\n->one thing keeps it from getting a five - star rave from me .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nExcellent atmosphere , delicious dishes good and friendly service .\n->", + "output": "{\"text\": \"Excellent atmosphere , delicious dishes good and friendly service .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the issue is that i got a faulty laptop and that ' s why the negative review .\n->the issue is that i got a faulty laptop and that ' s why the negative review .\n[{'aspect': 'laptop', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'negative', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n->The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n[{'aspect': 'decor', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'low', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"food 's presentation\", 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The wine list is also really nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is also really nice .\n->", + "output": "{\"text\": \"The wine list is also really nice .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do n ' t game , so not idea how that would go , but it ' s probably not bad .\n->i do n ' t game , so not idea how that would go , but it ' s probably not bad .\n[{'aspect': 'NULL', 'opinion': 'not bad', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: for $ 499 the laptop is a bargain , but you should only buy if you you plan to add an ssd in the near future .\n->for $ 499 the laptop is a bargain , but you should only buy if you you plan to add an ssd in the near future .\n[{'aspect': 'laptop', 'opinion': 'bargain', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: Everything was wonderful ; food , drinks , staff , mileau .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEverything was wonderful ; food , drinks , staff , mileau .\n->", + "output": "{\"text\": \"Everything was wonderful ; food , drinks , staff , mileau .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mileau', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: no backlit keyboard is kinda a bummer but i digress .\n->no backlit keyboard is kinda a bummer but i digress .\n[{'aspect': 'backlit keyboard', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: I have been to Casimir over 5 times and I have always had a great time there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have been to Casimir over 5 times and I have always had a great time there .\n->", + "output": "{\"text\": \"I have been to Casimir over 5 times and I have always had a great time there .\", \"labels\": \"[{'aspect': 'Casimir', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best of all is the warm vibe , the owner is super friendly and service is fast .\n->best of all is the warm vibe , the owner is super friendly and service is fast .\n[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i ' ve gotten more use out of this thing than i first envisioned .\n->i ' ve gotten more use out of this thing than i first envisioned .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: The food is great and reasonably priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is great and reasonably priced .\n->", + "output": "{\"text\": \"The food is great and reasonably priced .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try sushimi cucumber roll .\n->try sushimi cucumber roll .\n[{'aspect': 'sushimi cucumber roll', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: volume was not working .\n->volume was not working .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->", + "output": "{\"text\": \"The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'stressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'unisex bathroom', 'opinion': 'stressed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n->Excellent atmosphere , delicious dishes good and friendly service .\n[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m seriously considering returning it !\n->i ' m seriously considering returning it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrom the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n->", + "output": "{\"text\": \"From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\", \"labels\": \"[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caviar', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had crawfish boiled and despite making a mess , it was a ton of fun and quite tasty as well .\n->We had crawfish boiled and despite making a mess , it was a ton of fun and quite tasty as well .\n[{'aspect': 'crawfish boiled', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crawfish boiled', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We ordered a tuna melt - it came with out cheese which just made it a tuna sandwich .\n->We ordered a tuna melt - it came with out cheese which just made it a tuna sandwich .\n[{'aspect': 'tuna melt', 'opinion': 'with out', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'with out', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'tuna sandwich', 'opinion': 'with out', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nConsidering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n->", + "output": "{\"text\": \"Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\", \"labels\": \"[{'aspect': 'waitstaff', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: prices are fair across the board for both food and bev .\n->prices are fair across the board for both food and bev .\n[{'aspect': 'food', 'opinion': 'fair', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bev', 'opinion': 'fair', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n->The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n[{'aspect': 'bruscetta', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mix of greens', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->", + "output": "{\"text\": \"I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\", \"labels\": \"[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: january 21 , 2016 update : i love this laptop even more than before .\n->january 21 , 2016 update : i love this laptop even more than before .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: feel totally comfortable with it , and will never go back to a pc .\n->feel totally comfortable with it , and will never go back to a pc .\n[{'aspect': 'NULL', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Friendly staff that actually lets you enjoy your meal and the company you 're with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFriendly staff that actually lets you enjoy your meal and the company you 're with .\n->", + "output": "{\"text\": \"Friendly staff that actually lets you enjoy your meal and the company you 're with .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: run do n ' t walk .\n->run do n ' t walk .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the build - quality is pretty good .\n->the build - quality is pretty good .\n[{'aspect': 'build - quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: I found the food to be outstanding , particulary the salmon dish I had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI found the food to be outstanding , particulary the salmon dish I had .\n->", + "output": "{\"text\": \"I found the food to be outstanding , particulary the salmon dish I had .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon dish', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touch screen is nice , and i like to use it for free handing things when i need to .\n->the touch screen is nice , and i like to use it for free handing things when i need to .\n[{'aspect': 'touch screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: Food is great and inexpensive .\n->Food is great and inexpensive .\n[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I also ordered the Change Mojito , which was out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI also ordered the Change Mojito , which was out of this world .\n->", + "output": "{\"text\": \"I also ordered the Change Mojito , which was out of this world .\", \"labels\": \"[{'aspect': 'Change Mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keep up the good work guys !\n->keep up the good work guys !\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: We had the pot-stickers which were great and a tempura dish that was great .\n->We had the pot-stickers which were great and a tempura dish that was great .\n[{'aspect': 'pot-stickers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tempura dish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n->", + "output": "{\"text\": \"We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\", \"labels\": \"[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s completely quiet , no heat whatsoever , and very fast !\n->it ' s completely quiet , no heat whatsoever , and very fast !\n[{'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' m giving this five stars considering the price .\n->i ' m giving this five stars considering the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: Overall , excellent restaurant !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOverall , excellent restaurant !\n->", + "output": "{\"text\": \"Overall , excellent restaurant !\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but so far this laptop has been up to its expectations .\n->but so far this laptop has been up to its expectations .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: got a date ? go here !\n->got a date ? go here !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The food was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was good .\n->", + "output": "{\"text\": \"The food was good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was mediocre , and the lack of air conditioning made for a less than comfortable meal .\n->The service was mediocre , and the lack of air conditioning made for a less than comfortable meal .\n[{'aspect': 'service', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'air conditioning', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'comfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the built - in speaker is below average .\n->the built - in speaker is below average .\n[{'aspect': 'built - in speaker', 'opinion': 'below average', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: The place was nice and calm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place was nice and calm .\n->", + "output": "{\"text\": \"The place was nice and calm .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'calm', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice chromebook .\n->nice chromebook .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: only three months in , and the laptop won ' t charge .\n->only three months in , and the laptop won ' t charge .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: but the service was a bit slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the service was a bit slow .\n->", + "output": "{\"text\": \"but the service was a bit slow .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n->product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: the screen is also crisp and the speakers are punchy for a laptop .\n->the screen is also crisp and the speakers are punchy for a laptop .\n[{'aspect': 'speakers', 'opinion': 'punchy', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: The buffet had a nice selection .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe buffet had a nice selection .\n->", + "output": "{\"text\": \"The buffet had a nice selection .\", \"labels\": \"[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n->The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n[{'aspect': 'eggplant parmesan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'baked ziti with meatsauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The food was average or above including some surprising tasty dishes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was average or above including some surprising tasty dishes .\n->", + "output": "{\"text\": \"The food was average or above including some surprising tasty dishes .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicate spices , onions , eggs and a kick - ass roti .\n->delicate spices , onions , eggs and a kick - ass roti .\n[{'aspect': 'spices', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'onions', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'eggs', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'roti', 'opinion': 'kick - ass', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n->the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Service was also very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was also very good .\n->", + "output": "{\"text\": \"Service was also very good .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: latterly the best laptop i ' ve ever had , fast , powerful , stunning display , little problems with pairing bluetooth\n->latterly the best laptop i ' ve ever had , fast , powerful , stunning display , little problems with pairing bluetooth\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'pairing bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i plugged it back in , let it fully charge as directed and have had no problems since .\n->i plugged it back in , let it fully charge as directed and have had no problems since .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I got an excellent piece of cheesecake and we had several other nice pastries .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI got an excellent piece of cheesecake and we had several other nice pastries .\n->", + "output": "{\"text\": \"I got an excellent piece of cheesecake and we had several other nice pastries .\", \"labels\": \"[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the unit is whisper quiet and hasn ' t gotten hot no matter how hard i push it .\n->the unit is whisper quiet and hasn ' t gotten hot no matter how hard i push it .\n[{'aspect': 'unit', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: I had the best ravioli ever .\n->I had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My son and his girlfriend both wanted cheeseburgers and they were huge !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy son and his girlfriend both wanted cheeseburgers and they were huge !\n->", + "output": "{\"text\": \"My son and his girlfriend both wanted cheeseburgers and they were huge !\", \"labels\": \"[{'aspect': 'cheeseburgers', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sushi experience was unbelievable with my fiance .\n->sushi experience was unbelievable with my fiance .\n[{'aspect': 'sushi', 'opinion': 'unbelievable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: screen looks good , it has a good battery life , keypad has some nice feedback , the works .\n->screen looks good , it has a good battery life , keypad has some nice feedback , the works .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nConsequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\n->", + "output": "{\"text\": \"Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\", \"labels\": \"[{'aspect': 'burgers', 'opinion': 'fell apart', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is nearly impossible to get a table , so if you ever have the chance to go here for dinner , do not pass it up .\n->it is nearly impossible to get a table , so if you ever have the chance to go here for dinner , do not pass it up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n->sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n[{'aspect': 'c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->", + "output": "{\"text\": \"This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'trendi', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n->chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n[{'aspect': 'chromeos', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chromeos', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The service was attentive and her suggestions of menu items was right on the mark .\n->The service was attentive and her suggestions of menu items was right on the mark .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu items', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n->", + "output": "{\"text\": \"The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far i absolutely love it .\n->so far i absolutely love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: after 3 weeks using this flip , i am quite happy with its performance , design .\n->after 3 weeks using this flip , i am quite happy with its performance , design .\n[{'aspect': 'flip', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'flip', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: And the Tom Kha soup was pathetic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd the Tom Kha soup was pathetic .\n->", + "output": "{\"text\": \"And the Tom Kha soup was pathetic .\", \"labels\": \"[{'aspect': 'Tom Kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - trackpad is too finicky and not my favorite\n->- trackpad is too finicky and not my favorite\n[{'aspect': 'trackpad', 'opinion': 'finicky', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: this is a great laptop .\n->this is a great laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: If you want good authentic Thai this place is not the place to go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you want good authentic Thai this place is not the place to go .\n->", + "output": "{\"text\": \"If you want good authentic Thai this place is not the place to go .\", \"labels\": \"[{'aspect': 'Thai', 'opinion': 'good authentic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the design of the space is good .\n->the design of the space is good .\n[{'aspect': 'space', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: with the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->with the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lemon salad', 'opinion': 'exception', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The pesto pizza was excellent , thin-crust pizza with a nice amount of spicy Italian cheese that I 'd never heard of before .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pesto pizza was excellent , thin-crust pizza with a nice amount of spicy Italian cheese that I 'd never heard of before .\n->", + "output": "{\"text\": \"The pesto pizza was excellent , thin-crust pizza with a nice amount of spicy Italian cheese that I 'd never heard of before .\", \"labels\": \"[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spicy Italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: atmosphere is nice and relaxed too . . .\n->atmosphere is nice and relaxed too . . .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: aside from that , laptop seems fine .\n->aside from that , laptop seems fine .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: THe back garden sitting area is very pleasant , where you can see their personal herb garden .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTHe back garden sitting area is very pleasant , where you can see their personal herb garden .\n->", + "output": "{\"text\": \"THe back garden sitting area is very pleasant , where you can see their personal herb garden .\", \"labels\": \"[{'aspect': 'back garden sitting area', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n->now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: you just have to deal with a low battery and that ' s all\n->you just have to deal with a low battery and that ' s all\n[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: We had the lobster sandwich and it was FANTASTIC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had the lobster sandwich and it was FANTASTIC .\n->", + "output": "{\"text\": \"We had the lobster sandwich and it was FANTASTIC .\", \"labels\": \"[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: as to my comment about the food , no apology or acknowledgment was made .\n->as to my comment about the food , no apology or acknowledgment was made .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n->", + "output": "{\"text\": \"My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\", \"labels\": \"[{'aspect': 'portion', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french fries', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The well mannered , pleasant staff that Tony has in his employ .\n->The well mannered , pleasant staff that Tony has in his employ .\n[{'aspect': 'staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and you ca n ' t beat the prices .\n->and you ca n ' t beat the prices .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: We waited at the bar and had martinis that were just right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe waited at the bar and had martinis that were just right .\n->", + "output": "{\"text\": \"We waited at the bar and had martinis that were just right .\", \"labels\": \"[{'aspect': 'martinis', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely recommend this chromebook , it ' s a beautiful machine .\n->definitely recommend this chromebook , it ' s a beautiful machine .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n->the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: love the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the food .\n->", + "output": "{\"text\": \"love the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is just right .\n->the screen is just right .\n[{'aspect': 'screen', 'opinion': 'right', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: it served my needs of web browsing and word processing for a number of years , but its battery life had dwindled and neither the screen nor the processor can match this asus .\n->it served my needs of web browsing and word processing for a number of years , but its battery life had dwindled and neither the screen nor the processor can match this asus .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\ntext: it 's the only place you can get yummy authentic japanese comfort food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit 's the only place you can get yummy authentic japanese comfort food .\n->", + "output": "{\"text\": \"it 's the only place you can get yummy authentic japanese comfort food .\", \"labels\": \"[{'aspect': 'japanese comfort food', 'opinion': 'yummy authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer put together a solid package .\n->acer put together a solid package .\n[{'aspect': 'acer', 'opinion': 'solid', 'polarity': 'positive', 'category': 'COMPANY#QUALITY'}]\nExample:\ntext: d ) a bluetooth mouse - while this unarguably has one of the best trackpads in chromebook land , there are still times you just need the precision of a mouse .\n->d ) a bluetooth mouse - while this unarguably has one of the best trackpads in chromebook land , there are still times you just need the precision of a mouse .\n[{'aspect': 'trackpads', 'opinion': 'best', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\ntext: Great food , good size menu , great service and an unpretensious setting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food , good size menu , great service and an unpretensious setting .\n->", + "output": "{\"text\": \"Great food , good size menu , great service and an unpretensious setting .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'good size', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'unpretensious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : streamlined and simple ; easy to use overall ; easy to find / delete pics & files ; good price ; looks nice from the outside , with the lid down ; good 14 ` ` screen size that ' s surprisingly hard to find\n->pros : streamlined and simple ; easy to use overall ; easy to find / delete pics & files ; good price ; looks nice from the outside , with the lid down ; good 14 ` ` screen size that ' s surprisingly hard to find\n[{'aspect': 'NULL', 'opinion': 'streamlined', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: we had fun eating in there , we were there like around 3 a . m . in the morning !\n->we had fun eating in there , we were there like around 3 a . m . in the morning !\n[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The freshest , best variety , and the fastest delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe freshest , best variety , and the fastest delivery .\n->", + "output": "{\"text\": \"The freshest , best variety , and the fastest delivery .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was average or above including some surprising tasty dishes .\n->The food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: overall , i ' m not very pleased with this computer .\n->overall , i ' m not very pleased with this computer .\n[{'aspect': 'computer', 'opinion': 'not very pleased', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->", + "output": "{\"text\": \"We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'two types of sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so close , but not good enough .\n->so close , but not good enough .\n[{'aspect': 'NULL', 'opinion': 'not good enough', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i love the feel of a lighter os and can do many tasks using google / web based apps .\n->i love the feel of a lighter os and can do many tasks using google / web based apps .\n[{'aspect': 'os', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Definitely a great spot for a nice occasion or date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDefinitely a great spot for a nice occasion or date .\n->", + "output": "{\"text\": \"Definitely a great spot for a nice occasion or date .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i definitely recommend this if you are looking for a good gaming laptop .\n->i definitely recommend this if you are looking for a good gaming laptop .\n[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is something to reconsidered if one to buy the chromebook for doing homework and a lot of typing .\n->this is something to reconsidered if one to buy the chromebook for doing homework and a lot of typing .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: Average to good Thai food , but terrible delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAverage to good Thai food , but terrible delivery .\n->", + "output": "{\"text\": \"Average to good Thai food , but terrible delivery .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: been there , done that , and new york , it ' s not that big a deal .\n->been there , done that , and new york , it ' s not that big a deal .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Acceptable prices .\n->Acceptable prices .\n[{'aspect': 'prices', 'opinion': 'Acceptable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This is a wonderful place on all stand points especially value ofr money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a wonderful place on all stand points especially value ofr money .\n->", + "output": "{\"text\": \"This is a wonderful place on all stand points especially value ofr money .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after the ssd upgrade , the computer is very fast .\n->after the ssd upgrade , the computer is very fast .\n[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: i can eat here every day of the week really lol love this place . . . )\n->i can eat here every day of the week really lol love this place . . . )\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: An excellent service\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAn excellent service\n->", + "output": "{\"text\": \"An excellent service\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with the great variety on the menu , i eat here often and never get bored .\n->with the great variety on the menu , i eat here often and never get bored .\n[{'aspect': 'menu', 'opinion': 'great variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i am sure amazon will exchange it again but it is not worth the time and hassle .\n->i am sure amazon will exchange it again but it is not worth the time and hassle .\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: We were greeted promptly by the waiter who was very nice and cordial .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were greeted promptly by the waiter who was very nice and cordial .\n->", + "output": "{\"text\": \"We were greeted promptly by the waiter who was very nice and cordial .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'cordial', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was well worth the wait .\n->it was well worth the wait .\n[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the counter service is bad .\n->the counter service is bad .\n[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nShe was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n->", + "output": "{\"text\": \"She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's great to go for a quick lunch either alone or with a friend .\n->It 's great to go for a quick lunch either alone or with a friend .\n[{'aspect': 'lunch', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The place is so cool and the service is prompt and curtious .\n->The place is so cool and the service is prompt and curtious .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n->", + "output": "{\"text\": \"We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\", \"labels\": \"[{'aspect': 'Gulab Jamun ( dessert )', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i called they would not accept the serial number .\n->when i called they would not accept the serial number .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: The lox is always fresh too .\n->The lox is always fresh too .\n[{'aspect': 'lox', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n->", + "output": "{\"text\": \"I thanked my friend who recommended me this restaurant and will certainly recommend it to others .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked beautifully and smoothly .\n->it worked beautifully and smoothly .\n[{'aspect': 'NULL', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: microphone is really low .\n->microphone is really low .\n[{'aspect': 'microphone', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: Service here was great , food was fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService here was great , food was fantastic .\n->", + "output": "{\"text\": \"Service here was great , food was fantastic .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Bartender was unable to tear himself away from friends at bar .\n->Bartender was unable to tear himself away from friends at bar .\n[{'aspect': 'Bartender', 'opinion': 'unable to tear', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: we were not dissappointed in the least bit by this little gem .\n->we were not dissappointed in the least bit by this little gem .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGuacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->", + "output": "{\"text\": \"Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\", \"labels\": \"[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus is a great computer company .\n->asus is a great computer company .\n[{'aspect': 'asus', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'computer company', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: this chromebook is amazing , i have had zero issues with it thus far and i ' ve used it quite extensively during the semester .\n->this chromebook is amazing , i have had zero issues with it thus far and i ' ve used it quite extensively during the semester .\n[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: You can not go wrong with this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou can not go wrong with this place .\n->", + "output": "{\"text\": \"You can not go wrong with this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n->i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n[{'aspect': 'performs', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: very fast product , but with this kind of technology it ' s not possible to use a program , that i work ( minitab ) .\n->very fast product , but with this kind of technology it ' s not possible to use a program , that i work ( minitab ) .\n[{'aspect': 'product', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The food is outstanding and the service is quick , friendly and very professional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is outstanding and the service is quick , friendly and very professional .\n->", + "output": "{\"text\": \"The food is outstanding and the service is quick , friendly and very professional .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this battery lasts for 5 + hours on some of the most taxing apps my phone can run .\n->this battery lasts for 5 + hours on some of the most taxing apps my phone can run .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n->the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n[{'aspect': 'tracking pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: Always a nice crowd , but never loud .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlways a nice crowd , but never loud .\n->", + "output": "{\"text\": \"Always a nice crowd , but never loud .\", \"labels\": \"[{'aspect': 'crowd', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crowd', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a great laptop !\n->a great laptop !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: however , after weeks of having this laptop , its outgoes a bunch of problems .\n->however , after weeks of having this laptop , its outgoes a bunch of problems .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I am reluctant to write because I would not want my jem of a pizza place to become overcrowded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI am reluctant to write because I would not want my jem of a pizza place to become overcrowded .\n->", + "output": "{\"text\": \"I am reluctant to write because I would not want my jem of a pizza place to become overcrowded .\", \"labels\": \"[{'aspect': 'pizza place', 'opinion': 'overcrowded', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really wanted to like this chromebook .\n->i really wanted to like this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'wanted to like', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i would highly recommend .\n->i would highly recommend .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The crust is thin , the ingredients are fresh and the staff is friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe crust is thin , the ingredients are fresh and the staff is friendly .\n->", + "output": "{\"text\": \"The crust is thin , the ingredients are fresh and the staff is friendly .\", \"labels\": \"[{'aspect': 'crust', 'opinion': 'thin', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n->i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n[{'aspect': 'battery', 'opinion': 'better', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: game performance was fantastic .\n->game performance was fantastic .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The fish was really , really fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fish was really , really fresh .\n->", + "output": "{\"text\": \"The fish was really , really fresh .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Have eaten at Ginger House several times , and it 's always good .\n->Have eaten at Ginger House several times , and it 's always good .\n[{'aspect': 'Ginger House', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: luckily we saved room for the bbq salmon , sea bass and crispy duck .\n->luckily we saved room for the bbq salmon , sea bass and crispy duck .\n[{'aspect': 'bbq salmon', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sea bass', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crispy duck', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: We all agreed that mare is one of the best seafood restaurants in New York .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe all agreed that mare is one of the best seafood restaurants in New York .\n->", + "output": "{\"text\": \"We all agreed that mare is one of the best seafood restaurants in New York .\", \"labels\": \"[{'aspect': 'mare', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this laptop !\n->i love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food is so cheap and the waiters are nice .\n->The food is so cheap and the waiters are nice .\n[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I stumbled upon this great pizzeria as I explored my new neighborhood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI stumbled upon this great pizzeria as I explored my new neighborhood .\n->", + "output": "{\"text\": \"I stumbled upon this great pizzeria as I explored my new neighborhood .\", \"labels\": \"[{'aspect': 'pizzeria', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is excellent .\n->The wine list is excellent .\n[{'aspect': 'wine list', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: system shutdown problems every month .\n->system shutdown problems every month .\n[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: All of the pizzas are terrific and the price is even better !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll of the pizzas are terrific and the price is even better !\n->", + "output": "{\"text\": \"All of the pizzas are terrific and the price is even better !\", \"labels\": \"[{'aspect': 'pizzas', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a thoroughly disappointing machine .\n->a thoroughly disappointing machine .\n[{'aspect': 'machine', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: aside from the larger capacity , it hasn ' t lived up to some of the other hardware i ' ve used .\n->aside from the larger capacity , it hasn ' t lived up to some of the other hardware i ' ve used .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: I highly recommend the Sophia pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend the Sophia pizza .\n->", + "output": "{\"text\": \"I highly recommend the Sophia pizza .\", \"labels\": \"[{'aspect': 'Sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i always was a huge fan of apple and i always thought the price on the macbook pros were too steep but i finally took the plunge and i ' m satisfied .\n->i always was a huge fan of apple and i always thought the price on the macbook pros were too steep but i finally took the plunge and i ' m satisfied .\n[{'aspect': 'macbook pros', 'opinion': 'satisfied', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n->battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: For the people who want great food plus great service , Roxy is a place to AVOID !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor the people who want great food plus great service , Roxy is a place to AVOID !\n->", + "output": "{\"text\": \"For the people who want great food plus great service , Roxy is a place to AVOID !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n->it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n->initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The first time the sushi was outstanding , the second time it was a little bland .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe first time the sushi was outstanding , the second time it was a little bland .\n->", + "output": "{\"text\": \"The first time the sushi was outstanding , the second time it was a little bland .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'outstanding', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only cons are the battery and the brightness of the screen .\n->the only cons are the battery and the brightness of the screen .\n[{'aspect': 'battery', 'opinion': 'cons', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'cons', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'lot', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The blond wood decor is very soothing , the premium sake is excellent and the service is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe blond wood decor is very soothing , the premium sake is excellent and the service is great .\n->", + "output": "{\"text\": \"The blond wood decor is very soothing , the premium sake is excellent and the service is great .\", \"labels\": \"[{'aspect': 'blond wood decor', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'premium sake', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when family came in he gave them apps to test their palets , and then ordered for them .\n->when family came in he gave them apps to test their palets , and then ordered for them .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - performance is solid for chromebook use .\n->- performance is solid for chromebook use .\n[{'aspect': 'chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Warning : You may find it difficult to dine at other Japanese restaurants after a visit to Mizu !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWarning : You may find it difficult to dine at other Japanese restaurants after a visit to Mizu !\n->", + "output": "{\"text\": \"Warning : You may find it difficult to dine at other Japanese restaurants after a visit to Mizu !\", \"labels\": \"[{'aspect': 'Mizu', 'opinion': 'difficult', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza here is consistently good .\n->Pizza here is consistently good .\n[{'aspect': 'Pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is also extremely well priced .\n->it is also extremely well priced .\n[{'aspect': 'NULL', 'opinion': 'extremely well', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n->", + "output": "{\"text\": \"Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\", \"labels\": \"[{'aspect': 'people', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"does n't quite match up\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Thalia', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it feels wonderful to finally say that about an acer display .\n->it feels wonderful to finally say that about an acer display .\n[{'aspect': 'acer display', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n->the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\ntext: I ordered the smoked salmon and roe appetizer and it was off flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ordered the smoked salmon and roe appetizer and it was off flavor .\n->", + "output": "{\"text\": \"I ordered the smoked salmon and roe appetizer and it was off flavor .\", \"labels\": \"[{'aspect': 'smoked salmon and roe appetizer', 'opinion': 'off flavor', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i go twice a month !\n->i go twice a month !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n->we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: I expected quite a bit more from such an expensive menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI expected quite a bit more from such an expensive menu .\n->", + "output": "{\"text\": \"I expected quite a bit more from such an expensive menu .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but today i noticed it ' s cracking ( ref : pics ) .\n->but today i noticed it ' s cracking ( ref : pics ) .\n[{'aspect': 'NULL', 'opinion': 'cracking', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Try the Pad Thai , or sample anything on the appetizer menu ... they 're all delicious .\n->Try the Pad Thai , or sample anything on the appetizer menu ... they 're all delicious .\n[{'aspect': 'Pad Thai', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizer menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The view is spectacular , and the food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe view is spectacular , and the food is great .\n->", + "output": "{\"text\": \"The view is spectacular , and the food is great .\", \"labels\": \"[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My fav was the sassy lassi ...\n->My fav was the sassy lassi ...\n[{'aspect': 'sassy lassi', 'opinion': 'fav', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a cool bar with great food , and tons of excellent beer .\n->a cool bar with great food , and tons of excellent beer .\n[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: delicious simple food in nice outdoor atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicious simple food in nice outdoor atmosphere .\n->", + "output": "{\"text\": \"delicious simple food in nice outdoor atmosphere .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is always packed .\n->this place is always packed .\n[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n->he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n[{'aspect': 'uni hand roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Kind , attentive wait staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nKind , attentive wait staff .\n->", + "output": "{\"text\": \"Kind , attentive wait staff .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'Kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great except came with a bad left hinge .\n->great except came with a bad left hinge .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hinge', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: If you 're looking for a great meal at a decent price , go to Del Frisco 's !\n->If you 're looking for a great meal at a decent price , go to Del Frisco 's !\n[{'aspect': 'meal', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n->", + "output": "{\"text\": \"I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\", \"labels\": \"[{'aspect': 'scallops', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mahi mahi ( on saffron risotto', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you want a casual neighborhood bistro that has great food and excellent service , this is the place .\n->If you want a casual neighborhood bistro that has great food and excellent service , this is the place .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne . . . got a little moody afterwards cause was stuffed . . . lol\n->the pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne . . . got a little moody afterwards cause was stuffed . . . lol\n[{'aspect': 'pasta penne', 'opinion': 'buttery', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pasta penne', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: Delicious crab cakes too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDelicious crab cakes too .\n->", + "output": "{\"text\": \"Delicious crab cakes too .\", \"labels\": \"[{'aspect': 'crab cakes', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works really well with my art programs and runs a lot better !\n->it works really well with my art programs and runs a lot better !\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: one of the best\n->one of the best\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Even if the food was n't this good , the garden is a great place to sit outside and relax .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven if the food was n't this good , the garden is a great place to sit outside and relax .\n->", + "output": "{\"text\": \"Even if the food was n't this good , the garden is a great place to sit outside and relax .\", \"labels\": \"[{'aspect': 'garden', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"was n't this good\", 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I love the atmorphere @ peep !\n->I love the atmorphere @ peep !\n[{'aspect': 'atmorphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: even if the food was n ' t this good , the garden is a great place to sit outside and relax .\n->even if the food was n ' t this good , the garden is a great place to sit outside and relax .\n[{'aspect': 'garden', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': \"n ' t this good\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Their calzones are horrific , bad , vomit-inducing , YUCK .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir calzones are horrific , bad , vomit-inducing , YUCK .\n->", + "output": "{\"text\": \"Their calzones are horrific , bad , vomit-inducing , YUCK .\", \"labels\": \"[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'vomit-inducing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'YUCK', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: go there to relax and feel like your somewhere else .\n->go there to relax and feel like your somewhere else .\n[{'aspect': 'NULL', 'opinion': 'relax', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'homemade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'herbs', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The counter service is bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe counter service is bad .\n->", + "output": "{\"text\": \"The counter service is bad .\", \"labels\": \"[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doubles as an android tablet and so far the experience with running android apps has been good .\n->it doubles as an android tablet and so far the experience with running android apps has been good .\n[{'aspect': 'android apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: super off balance with respect to screen .\n->super off balance with respect to screen .\n[{'aspect': 'screen', 'opinion': 'off balance', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\ntext: The sandwiches are dry , tasteless and way overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sandwiches are dry , tasteless and way overpriced .\n->", + "output": "{\"text\": \"The sandwiches are dry , tasteless and way overpriced .\", \"labels\": \"[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall a good buy .\n->overall a good buy .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i typically love acers ( i have a regular laptop ) but this machine has been a nightmare .\n->i typically love acers ( i have a regular laptop ) but this machine has been a nightmare .\n[{'aspect': 'machine', 'opinion': 'nightmare', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Calling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCalling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n->", + "output": "{\"text\": \"Calling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'room', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'clerks', 'opinion': 'unhelpful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n->the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n[{'aspect': 'cold appetizer dishes', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n->The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Seriously , this place kicks ass .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSeriously , this place kicks ass .\n->", + "output": "{\"text\": \"Seriously , this place kicks ass .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'kicks ass', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall it has been a wonder experience and quality product .\n->overall it has been a wonder experience and quality product .\n[{'aspect': 'product', 'opinion': 'wonder', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'product', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: very happy with my purchase , fast delivery , package well .\n->very happy with my purchase , fast delivery , package well .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'package', 'opinion': 'well', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The atmosphere is unheralded , the service impecible , and the food magnificant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is unheralded , the service impecible , and the food magnificant .\n->", + "output": "{\"text\": \"The atmosphere is unheralded , the service impecible , and the food magnificant .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: After really enjoying ourselves at the bar we sat down at a table and had dinner .\n->After really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'table', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: This is such a lovely , peaceful place to eat outside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is such a lovely , peaceful place to eat outside .\n->", + "output": "{\"text\": \"This is such a lovely , peaceful place to eat outside .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'peaceful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just received this product about an hour or so ago .\n->i just received this product about an hour or so ago .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the screen looks fantastic and movies look great .\n->the screen looks fantastic and movies look great .\n[{'aspect': 'screen', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: This is a great place to take out-of-towners , and perfect for watching the sunset .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a great place to take out-of-towners , and perfect for watching the sunset .\n->", + "output": "{\"text\": \"This is a great place to take out-of-towners , and perfect for watching the sunset .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hardware specs on this are very nice compared to other offerings : 4g ram , 32g storage , quad - core processor , etc .\n->the hardware specs on this are very nice compared to other offerings : 4g ram , 32g storage , quad - core processor , etc .\n[{'aspect': 'hardware specs', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: Have frequented 'ino for several years and the food remains excellent .\n->Have frequented 'ino for several years and the food remains excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great sushi experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat sushi experience .\n->", + "output": "{\"text\": \"Great sushi experience .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s easy to use , convenient .\n->it ' s easy to use , convenient .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: But the thing that my wife and I hated was it was so loud and it felt like ' bar ' or ' pub ' .\n->But the thing that my wife and I hated was it was so loud and it felt like ' bar ' or ' pub ' .\n[{'aspect': 'bar', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pub', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Unique apppetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nUnique apppetizers .\n->", + "output": "{\"text\": \"Unique apppetizers .\", \"labels\": \"[{'aspect': 'apppetizers', 'opinion': 'Unique', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In such a crappy part of town to find a good value for lunch , this place is great .\n->In such a crappy part of town to find a good value for lunch , this place is great .\n[{'aspect': 'value', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very junky\n->very junky\n[{'aspect': 'junky', 'opinion': 'junky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Try sushimi cucumber roll .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry sushimi cucumber roll .\n->", + "output": "{\"text\": \"Try sushimi cucumber roll .\", \"labels\": \"[{'aspect': 'sushimi cucumber roll', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n->Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this chrome book joins the group and is itself excellent and different .\n->this chrome book joins the group and is itself excellent and different .\n[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chrome book', 'opinion': 'different', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The service is awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is awful .\n->", + "output": "{\"text\": \"The service is awful .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do not buy this machine if you ' re hoping to run android apps .\n->do not buy this machine if you ' re hoping to run android apps .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n->android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n[{'aspect': 'android', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: This place is not worth the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is not worth the prices .\n->", + "output": "{\"text\": \"This place is not worth the prices .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'not worth the prices', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but overall i give it a 10\n->but overall i give it a 10\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: other than that it ' s everything i imagined and more .\n->other than that it ' s everything i imagined and more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Love Pizza 33 ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLove Pizza 33 ...\n->", + "output": "{\"text\": \"Love Pizza 33 ...\", \"labels\": \"[{'aspect': 'Pizza 33', 'opinion': 'Love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to me , this chromebook is a great value .\n->to me , this chromebook is a great value .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: our teenage kids love it , too .\n->our teenage kids love it , too .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .\n->", + "output": "{\"text\": \"I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'crave', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer has a great battery life and it is just like every other computer just smaller size and it is a great brand name .\n->this computer has a great battery life and it is just like every other computer just smaller size and it is a great brand name .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n->So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n[{'aspect': 'thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This tiny Williamsburg spot is always pleasantly surprising .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis tiny Williamsburg spot is always pleasantly surprising .\n->", + "output": "{\"text\": \"This tiny Williamsburg spot is always pleasantly surprising .\", \"labels\": \"[{'aspect': 'Williamsburg spot', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: how can a brand new computer not charge properly ?\n->how can a brand new computer not charge properly ?\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i can bring it anywhere because of how small it is : $\n->i can bring it anywhere because of how small it is : $\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: The pizza is delicious and the proprietor is one of the nicest in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza is delicious and the proprietor is one of the nicest in NYC .\n->", + "output": "{\"text\": \"The pizza is delicious and the proprietor is one of the nicest in NYC .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought this for my ten year old for school and creating .\n->bought this for my ten year old for school and creating .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: bought this for someone else , can ' t believe how good it is .\n->bought this for someone else , can ' t believe how good it is .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Bagels are ok , but be sure not to make any special requests !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBagels are ok , but be sure not to make any special requests !\n->", + "output": "{\"text\": \"Bagels are ok , but be sure not to make any special requests !\", \"labels\": \"[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The dinner menu is diverse and top-notch as well .\n->The dinner menu is diverse and top-notch as well .\n[{'aspect': 'dinner menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner menu', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: asus support is responsive but ineffective .\n->asus support is responsive but ineffective .\n[{'aspect': 'asus support', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus support', 'opinion': 'ineffective', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\ntext: the turkey burgers are scary !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe turkey burgers are scary !\n->", + "output": "{\"text\": \"the turkey burgers are scary !\", \"labels\": \"[{'aspect': 'turkey burgers', 'opinion': 'scary', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you buy this machine - be prepared for it to break .\n->if you buy this machine - be prepared for it to break .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: that said : if you can deal with the short battery life , you ' re not going to find a better machine at this price .\n->that said : if you can deal with the short battery life , you ' re not going to find a better machine at this price .\n[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The sushi was awful !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sushi was awful !\n->", + "output": "{\"text\": \"The sushi was awful !\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n->all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n[{'aspect': 'web browsing', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: The rice was poor quality and was cooked so badly it was hard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe rice was poor quality and was cooked so badly it was hard .\n->", + "output": "{\"text\": \"The rice was poor quality and was cooked so badly it was hard .\", \"labels\": \"[{'aspect': 'rice', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'cooked so badly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'hard', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 10\n->10\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The food was bland oily .\n->The food was bland oily .\n[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFurthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n->", + "output": "{\"text\": \"Furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m surprised people are settling for claiming ` ` it ' s good enough ` ` or ` ` gets the job done ` ` when every time i use it , i ' m constantly reminded of how i should have just sprang for the pixelbook when it was on sale for $ 699 .\n->i ' m surprised people are settling for claiming ` ` it ' s good enough ` ` or ` ` gets the job done ` ` when every time i use it , i ' m constantly reminded of how i should have just sprang for the pixelbook when it was on sale for $ 699 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: We 've been to Grocery three times and not once has an item on the menu disappointed .\n->We 've been to Grocery three times and not once has an item on the menu disappointed .\n[{'aspect': 'menu', 'opinion': 'disappointed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Good , fast service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood , fast service .\n->", + "output": "{\"text\": \"Good , fast service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you are someone who appreciates simplicity , elegance , and wonderfully presented and tasting seafood and vegetables regardless of portion size , Kai is your place .\n->If you are someone who appreciates simplicity , elegance , and wonderfully presented and tasting seafood and vegetables regardless of portion size , Kai is your place .\n[{'aspect': 'seafood', 'opinion': 'wonderfully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'tasting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'wonderfully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'tasting', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: to get the features like this good luck .\n->to get the features like this good luck .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Food is great and inexpensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is great and inexpensive .\n->", + "output": "{\"text\": \"Food is great and inexpensive .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n->i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n->The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n[{'aspect': 'wait staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The location is perfect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe location is perfect .\n->", + "output": "{\"text\": \"The location is perfect .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i literally just got back home after visiting casa la femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .\n->i literally just got back home after visiting casa la femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .\n[{'aspect': 'casa la femme', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'casa la femme', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n->Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n[{'aspect': 'raw vegatables', 'opinion': 'wondered', 'polarity': 'negative', 'category': 'NULL'}]\ntext: If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n->", + "output": "{\"text\": \"If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\", \"labels\": \"[{'aspect': 'bottle', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Also good for client lunch meetings , esp .\n->Also good for client lunch meetings , esp .\n[{'aspect': 'lunch meetings', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: - screen brightness is generally ` ` enough ` ` but maybe not enough for watching dark movies in bright lights .\n->- screen brightness is generally ` ` enough ` ` but maybe not enough for watching dark movies in bright lights .\n[{'aspect': 'screen brightness', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: Great atmoshere and worth every bit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat atmoshere and worth every bit .\n->", + "output": "{\"text\": \"Great atmoshere and worth every bit .\", \"labels\": \"[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza was a little soggy .\n->Pizza was a little soggy .\n[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Be sure to try the seasonal , and always delicious , specials .\n->Be sure to try the seasonal , and always delicious , specials .\n[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Winnie and her staff are the best crew you can find serving you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWinnie and her staff are the best crew you can find serving you .\n->", + "output": "{\"text\": \"Winnie and her staff are the best crew you can find serving you .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Winnie', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 2 stars taken for horrible sound quality\n->2 stars taken for horrible sound quality\n[{'aspect': 'sound quality', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: $ 20 gets you unlimited sushi of a very high quality -- I even took a friend here from Japan who said it was one of the best sushi places in the US that he has been to .\n->$ 20 gets you unlimited sushi of a very high quality -- I even took a friend here from Japan who said it was one of the best sushi places in the US that he has been to .\n[{'aspect': 'sushi', 'opinion': 'unlimited', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi places', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality', 'opinion': 'high', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is reliable and the price is moderate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is reliable and the price is moderate .\n->", + "output": "{\"text\": \"The food is reliable and the price is moderate .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: machine periodically crashed .\n->machine periodically crashed .\n[{'aspect': 'machine', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Best Pastrami I ever had and great portion without being ridiculous .\n->Best Pastrami I ever had and great portion without being ridiculous .\n[{'aspect': 'Pastrami', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: For authentic Thai food , look no further than Toons .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor authentic Thai food , look no further than Toons .\n->", + "output": "{\"text\": \"For authentic Thai food , look no further than Toons .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Lucky Strike is a great casual place to just grab a bite to eat .\n->Lucky Strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'place', 'opinion': 'great casual', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The menu changed , portions were even smaller than before , a lentil dish was salty beyond edibility , a basmati rice dish lacked flavor .\n->The menu changed , portions were even smaller than before , a lentil dish was salty beyond edibility , a basmati rice dish lacked flavor .\n[{'aspect': 'menu', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'lentil dish', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'basmati rice dish', 'opinion': 'lacked flavor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Try the Pad Thai , or sample anything on the appetizer menu ... they 're all delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the Pad Thai , or sample anything on the appetizer menu ... they 're all delicious .\n->", + "output": "{\"text\": \"Try the Pad Thai , or sample anything on the appetizer menu ... they 're all delicious .\", \"labels\": \"[{'aspect': 'Pad Thai', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizer menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: since i already do all of my work in google drive , this is perfect for me .\n->since i already do all of my work in google drive , this is perfect for me .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: very disappointed in this machine .\n->very disappointed in this machine .\n[{'aspect': 'machine', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The service was attentive , yet discreet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was attentive , yet discreet .\n->", + "output": "{\"text\": \"The service was attentive , yet discreet .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Make reservations but expect to be delayed 15-20 minutes as the hosting staff was having difficulty seating guests who arrived with a reservation because they probably had a lot of walk ins being so close to Time Square .\n->Make reservations but expect to be delayed 15-20 minutes as the hosting staff was having difficulty seating guests who arrived with a reservation because they probably had a lot of walk ins being so close to Time Square .\n[{'aspect': 'reservations', 'opinion': 'delayed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'difficulty', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->", + "output": "{\"text\": \"The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\", \"labels\": \"[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now , the headphone jack produces low volume at 10 percent capacity .\n->now , the headphone jack produces low volume at 10 percent capacity .\n[{'aspect': 'headphone jack', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: ( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n->( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n[{'aspect': 'it', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: The place was quiet and delightful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place was quiet and delightful .\n->", + "output": "{\"text\": \"The place was quiet and delightful .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the start up process was very simple and relatively quick .\n->the start up process was very simple and relatively quick .\n[{'aspect': 'start up', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n->it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n[{'aspect': 'customer service and support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: Service was good and food is wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was good and food is wonderful .\n->", + "output": "{\"text\": \"Service was good and food is wonderful .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can right - click by using two fingers to tap the track pad at the same time ( or 2 fingers on the touchpad , using one of them to press press down / click the touchpad ) .\n->you can right - click by using two fingers to tap the track pad at the same time ( or 2 fingers on the touchpad , using one of them to press press down / click the touchpad ) .\n[{'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: this place is great .\n->this place is great .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: I did not try the caviar but I tried their salmon and crab salad ( they are all good )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI did not try the caviar but I tried their salmon and crab salad ( they are all good )\n->", + "output": "{\"text\": \"I did not try the caviar but I tried their salmon and crab salad ( they are all good )\", \"labels\": \"[{'aspect': 'salmon', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab salad', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n->very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n[{'aspect': 'sound volume', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: It is definitely a good spot for snacks and chat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is definitely a good spot for snacks and chat .\n->", + "output": "{\"text\": \"It is definitely a good spot for snacks and chat .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n->We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n[{'aspect': 'dining', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ended up returning it even after getting a credit because the wireless did not work well and was extremely slow .\n->i ended up returning it even after getting a credit because the wireless did not work well and was extremely slow .\n[{'aspect': 'wireless', 'opinion': 'not work well', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}, {'aspect': 'wireless', 'opinion': 'slow', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n->", + "output": "{\"text\": \"As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\", \"labels\": \"[{'aspect': 'Lucky Strike', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was excellent as well as service , however , i left the four seasons very dissappointed .\n->the food was excellent as well as service , however , i left the four seasons very dissappointed .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'the four seasons', 'opinion': 'dissappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The staff there is very attentive and down to earth .\n->The staff there is very attentive and down to earth .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n->", + "output": "{\"text\": \"The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just received this product about an hour or so ago .\n->i just received this product about an hour or so ago .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: looks brand new and the battery life lasts a long time ( see photos )\n->looks brand new and the battery life lasts a long time ( see photos )\n[{'aspect': 'NULL', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->", + "output": "{\"text\": \"The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked-to-perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: THe back garden sitting area is very pleasant , where you can see their personal herb garden .\n->THe back garden sitting area is very pleasant , where you can see their personal herb garden .\n[{'aspect': 'back garden sitting area', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i really do like this computer , however the description is wrong .\n->i really do like this computer , however the description is wrong .\n[{'aspect': 'computer', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'description', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the best part about LS is the late night atmosphere , delightfully free of the BTs .\n->", + "output": "{\"text\": \"But the best part about LS is the late night atmosphere , delightfully free of the BTs .\", \"labels\": \"[{'aspect': 'late night atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i absolutely love everything about this chromebook .\n->i absolutely love everything about this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n->", + "output": "{\"text\": \"You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\", \"labels\": \"[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Vanilla Shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza is yummy and i like the atmoshpere .\n->the pizza is yummy and i like the atmoshpere .\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n->the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n[{'aspect': 'keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'back light', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n->", + "output": "{\"text\": \"The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\", \"labels\": \"[{'aspect': 'in-house lady DJ', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apple should ashamed to be associated with this cheap , lousy , clearly inferior , plastic nightmare - but then again , maybe that is exactly what they do want , so profits soar .\n->apple should ashamed to be associated with this cheap , lousy , clearly inferior , plastic nightmare - but then again , maybe that is exactly what they do want , so profits soar .\n[{'aspect': 'apple', 'opinion': 'ashamed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'inferior', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: as a chrome book it is excellent , but android support is unsatisfying .\n->as a chrome book it is excellent , but android support is unsatisfying .\n[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'android support', 'opinion': 'unsatisfying', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: Suan is a great place that I often take my friends ( classmates ) too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSuan is a great place that I often take my friends ( classmates ) too .\n->", + "output": "{\"text\": \"Suan is a great place that I often take my friends ( classmates ) too .\", \"labels\": \"[{'aspect': 'Suan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food .\n->great food .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i waited for a while before writing a review about this product .\n->i waited for a while before writing a review about this product .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIts location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->", + "output": "{\"text\": \"Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: barely have it for 6 months and everything ' s going haywire .\n->barely have it for 6 months and everything ' s going haywire .\n[{'aspect': 'NULL', 'opinion': 'haywire', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: he has thoroughly enjoyed it\n->he has thoroughly enjoyed it\n[{'aspect': 'NULL', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I LOVE their Thai\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI LOVE their Thai\n->", + "output": "{\"text\": \"I LOVE their Thai\", \"labels\": \"[{'aspect': 'Thai', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food there is so good that even to order out the wait is incredible .\n->The food there is so good that even to order out the wait is incredible .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'incredible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: noodles with shrimp and chicken and coconut juice is the MUST !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnoodles with shrimp and chicken and coconut juice is the MUST !\n->", + "output": "{\"text\": \"noodles with shrimp and chicken and coconut juice is the MUST !\", \"labels\": \"[{'aspect': 'noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hit the power button and plug it in , it will be ready before you are .\n->hit the power button and plug it in , it will be ready before you are .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The only fallback on this restaurant is the prices .\n->The only fallback on this restaurant is the prices .\n[{'aspect': 'restaurant', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'NULL'}]\ntext: In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n->", + "output": "{\"text\": \"In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\", \"labels\": \"[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was excellent - friendly and attentive .\n->The service was excellent - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The place was nice and calm .\n->The place was nice and calm .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'calm', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I can not imagine a friendlier staff working in a restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI can not imagine a friendlier staff working in a restaurant .\n->", + "output": "{\"text\": \"I can not imagine a friendlier staff working in a restaurant .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: terrible would be a compliment !\n->terrible would be a compliment !\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: in the beginning i felt a little weird about this trackpad .\n->in the beginning i felt a little weird about this trackpad .\n[{'aspect': 'trackpad', 'opinion': 'weird', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: I can not imagine better Indian food in all of the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI can not imagine better Indian food in all of the city .\n->", + "output": "{\"text\": \"I can not imagine better Indian food in all of the city .\", \"labels\": \"[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i ' ve had consistent issues with this laptop since i ' ve bought it .\n->but i ' ve had consistent issues with this laptop since i ' ve bought it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I liked the food at this quasi-thai restaurant .\n->I liked the food at this quasi-thai restaurant .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDuring the course of the past 3 months , the chef and staff changed and it was not for the better .\n->", + "output": "{\"text\": \"During the course of the past 3 months , the chef and staff changed and it was not for the better .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I expected quite a bit more from such an expensive menu .\n->I expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i like this laptop , for a 15 ` ` monitor laptop with i5 - 8250u cpu , the weight is acceptable for me to carry it to work between different office .\n->i like this laptop , for a 15 ` ` monitor laptop with i5 - 8250u cpu , the weight is acceptable for me to carry it to work between different office .\n[{'aspect': 'weight', 'opinion': 'acceptable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The food now is inconsistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food now is inconsistent .\n->", + "output": "{\"text\": \"The food now is inconsistent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its dark , and cozy . . there is always jazz music playing when we go .\n->its dark , and cozy . . there is always jazz music playing when we go .\n[{'aspect': 'NULL', 'opinion': 'cozy . .', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'dark', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: will never buy an asus product again .\n->will never buy an asus product again .\n[{'aspect': 'asus product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: This is the kind of place you 'd like to take all your friends to and still keep a secret .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the kind of place you 'd like to take all your friends to and still keep a secret .\n->", + "output": "{\"text\": \"This is the kind of place you 'd like to take all your friends to and still keep a secret .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: although we were told 10 - 15 minutes and it was more like 45 minutes .\n->although we were told 10 - 15 minutes and it was more like 45 minutes .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: if only they delivered , they ' d make a mint !\n->if only they delivered , they ' d make a mint !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: The setting is casual and romantic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe setting is casual and romantic .\n->", + "output": "{\"text\": \"The setting is casual and romantic .\", \"labels\": \"[{'aspect': 'setting', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for my daughter for school and she loves it .\n->i bought this for my daughter for school and she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this place is a must visit !\n->this place is a must visit !\n[{'aspect': 'place', 'opinion': 'must visit', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: if you 're daring , try the balsamic vinegar over icecream , it 's wonderful !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you 're daring , try the balsamic vinegar over icecream , it 's wonderful !\n->", + "output": "{\"text\": \"if you 're daring , try the balsamic vinegar over icecream , it 's wonderful !\", \"labels\": \"[{'aspect': 'balsamic vinegar over icecream', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balsamic vinegar over icecream', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A glass of Leaping Lizard , a glass of prosecco , and the mussels had everything happy .\n->A glass of Leaping Lizard , a glass of prosecco , and the mussels had everything happy .\n[{'aspect': 'glass of prosecco', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'glass of Leaping Lizard', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good looking screen , has been bright enough for daily use , including outdoor .\n->good looking screen , has been bright enough for daily use , including outdoor .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: The rest of the dim sum , though pricey by Chinatown standards , is worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe rest of the dim sum , though pricey by Chinatown standards , is worth it .\n->", + "output": "{\"text\": \"The rest of the dim sum , though pricey by Chinatown standards , is worth it .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quality ingredients preparation all around , and a very fair price for NYC .\n->Quality ingredients preparation all around , and a very fair price for NYC .\n[{'aspect': 'ingredients', 'opinion': 'Quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'fair', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the overall design is sleek and pleasing to look at and hold .\n->the overall design is sleek and pleasing to look at and hold .\n[{'aspect': 'design', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'pleasing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n->", + "output": "{\"text\": \"The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'sleek', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n->the wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'meal', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: no dvd drive , but who uses those anymore anyway ?\n->no dvd drive , but who uses those anymore anyway ?\n[{'aspect': 'dvd drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\ntext: A few tips : skip the turnip cake , roast pork buns and egg custards .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA few tips : skip the turnip cake , roast pork buns and egg custards .\n->", + "output": "{\"text\": \"A few tips : skip the turnip cake , roast pork buns and egg custards .\", \"labels\": \"[{'aspect': 'turnip cake', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'roast pork buns', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'egg custards', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - it ' s a bit heavy in tablet mode .\n->- it ' s a bit heavy in tablet mode .\n[{'aspect': 'tablet mode', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: i contacted asus and they could do nothing .\n->i contacted asus and they could do nothing .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: The food was exceptional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was exceptional .\n->", + "output": "{\"text\": \"The food was exceptional .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: decent sized track pad .\n->decent sized track pad .\n[{'aspect': 'track pad', 'opinion': 'decent', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: The food was average or above including some surprising tasty dishes .\n->The food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n->", + "output": "{\"text\": \"I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\", \"labels\": \"[{'aspect': 'braised lamb shank in red wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n->very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n[{'aspect': 'sound volume', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: for the price it is an amazing starting point and hard to beat especially with such an amazing brand such as asus .\n->for the price it is an amazing starting point and hard to beat especially with such an amazing brand such as asus .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: The service was friendly and the atmosphere was casual .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was friendly and the atmosphere was casual .\n->", + "output": "{\"text\": \"The service was friendly and the atmosphere was casual .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you are looking for a good quality , cheap eats - this is the place .\n->If you are looking for a good quality , cheap eats - this is the place .\n[{'aspect': 'eats', 'opinion': 'good quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n->My suggestion is to eat family style because you 'll want to try the other dishes .\n[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the pad se ew chicken was delicious , however the pad thai was far too oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pad se ew chicken was delicious , however the pad thai was far too oily .\n->", + "output": "{\"text\": \"the pad se ew chicken was delicious , however the pad thai was far too oily .\", \"labels\": \"[{'aspect': 'pad se ew chicken', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first and foremost , lets talk about the infamous keyboard .\n->first and foremost , lets talk about the infamous keyboard .\n[{'aspect': 'keyboard', 'opinion': 'infamous', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i did not find the battery to last a full ten hours .\n->i did not find the battery to last a full ten hours .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Have eaten at Ginger House several times , and it 's always good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHave eaten at Ginger House several times , and it 's always good .\n->", + "output": "{\"text\": \"Have eaten at Ginger House several times , and it 's always good .\", \"labels\": \"[{'aspect': 'Ginger House', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would recommend reservations on weekends though .\n->i would recommend reservations on weekends though .\n[{'aspect': 'reservations', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the wifi card recently died after 14 months .\n->the wifi card recently died after 14 months .\n[{'aspect': 'wifi card', 'opinion': 'died', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: The fried dumplings are GREAT !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fried dumplings are GREAT !\n->", + "output": "{\"text\": \"The fried dumplings are GREAT !\", \"labels\": \"[{'aspect': 'fried dumplings', 'opinion': 'GREAT', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff ignored my friends and I the entire time we were there .\n->The staff ignored my friends and I the entire time we were there .\n[{'aspect': 'staff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The portion sizes here are huge , and the sushi is good .\n->The portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Finally a reliable Chinese restaurant !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFinally a reliable Chinese restaurant !\n->", + "output": "{\"text\": \"Finally a reliable Chinese restaurant !\", \"labels\": \"[{'aspect': 'Chinese restaurant', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ca n ' t remember the last time i had such gross food in new york .\n->i ca n ' t remember the last time i had such gross food in new york .\n[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: highly recommended !\n->highly recommended !\n[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Terrible , terrible management - deserves to be shut-down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTerrible , terrible management - deserves to be shut-down .\n->", + "output": "{\"text\": \"Terrible , terrible management - deserves to be shut-down .\", \"labels\": \"[{'aspect': 'management', 'opinion': 'Terrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The ambience is authentic and relaxing and we have always received attentive and prompt service .\n->The ambience is authentic and relaxing and we have always received attentive and prompt service .\n[{'aspect': 'ambience', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my computer was used on average a couple hours a day .\n->my computer was used on average a couple hours a day .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: Spreads and toppings are great - though a bit pricey .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSpreads and toppings are great - though a bit pricey .\n->", + "output": "{\"text\": \"Spreads and toppings are great - though a bit pricey .\", \"labels\": \"[{'aspect': 'Spreads', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Spreads', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best pc bang for this level of buck .\n->best pc bang for this level of buck .\n[{'aspect': 'pc bang', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n->", + "output": "{\"text\": \"The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food , amazing service , this place is a class act .\n->great food , amazing service , this place is a class act .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'class act', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: for desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .\n->for desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .\n[{'aspect': 'frozen black sesame mousse', 'opinion': 'interesting', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'frozen black sesame mousse', 'opinion': 'extraordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'matcha ( powdered green tea ) and blueberry cheesecake', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: You must try the shrimp appetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou must try the shrimp appetizers .\n->", + "output": "{\"text\": \"You must try the shrimp appetizers .\", \"labels\": \"[{'aspect': 'shrimp appetizers', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n->I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n[{'aspect': 'lamb chop', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n->", + "output": "{\"text\": \"This place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'correct', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hostess was very pleasant .\n->the hostess was very pleasant .\n[{'aspect': 'hostess', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: feel totally comfortable with it , and will never go back to a pc .\n->feel totally comfortable with it , and will never go back to a pc .\n[{'aspect': 'NULL', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Great food , great prices , great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food , great prices , great service .\n->", + "output": "{\"text\": \"Great food , great prices , great service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this made me realize that arm processors are not ready for desktop - class browsing .\n->this made me realize that arm processors are not ready for desktop - class browsing .\n[{'aspect': 'arm processors', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\nExample:\ntext: i also really like the finish on the case .\n->i also really like the finish on the case .\n[{'aspect': 'case', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: If you are looking for a good quality , cheap eats - this is the place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you are looking for a good quality , cheap eats - this is the place .\n->", + "output": "{\"text\": \"If you are looking for a good quality , cheap eats - this is the place .\", \"labels\": \"[{'aspect': 'eats', 'opinion': 'good quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen turn black and won ' t turn on within a month rarely use .\n->screen turn black and won ' t turn on within a month rarely use .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: i am truly enjoying my laptop after one month .\n->i am truly enjoying my laptop after one month .\n[{'aspect': 'laptop', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it 's a perfect place to have a amazing indian food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit 's a perfect place to have a amazing indian food .\n->", + "output": "{\"text\": \"it 's a perfect place to have a amazing indian food .\", \"labels\": \"[{'aspect': 'indian food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n->To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i have the intel core i7 , soooo fast !\n->i have the intel core i7 , soooo fast !\n[{'aspect': 'intel core i7', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n->", + "output": "{\"text\": \"also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n->You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n[{'aspect': 'crabmeat lasagna', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: works good but right click on mouse pad wont wok have to use external mouse\n->works good but right click on mouse pad wont wok have to use external mouse\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\n->", + "output": "{\"text\": \"Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'fine', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , it works well and is easy to use .\n->overall , it works well and is easy to use .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: there is no excuse for such lousy service !\n->there is no excuse for such lousy service !\n[{'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Great bagels made the old-fashioned way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat bagels made the old-fashioned way .\n->", + "output": "{\"text\": \"Great bagels made the old-fashioned way .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even though its good seafood , the prices are too high .\n->even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'too high', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: and the tom kha soup was pathetic .\n->and the tom kha soup was pathetic .\n[{'aspect': 'tom kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The food was absolutely amazing ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was absolutely amazing ! !\n->", + "output": "{\"text\": \"The food was absolutely amazing ! !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n->a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: it took hours to restore to factory default settings , and it crashed once again days later .\n->it took hours to restore to factory default settings , and it crashed once again days later .\n[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The baked clams octopus we shared as appetizers were the best we 've ever had ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe baked clams octopus we shared as appetizers were the best we 've ever had ! !\n->", + "output": "{\"text\": \"The baked clams octopus we shared as appetizers were the best we 've ever had ! !\", \"labels\": \"[{'aspect': 'baked clams octopus', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n->I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: My husband and I enjoy Sangria .\n->My husband and I enjoy Sangria .\n[{'aspect': 'Sangria', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The lamb was tender so full of flavor , the dessert was divine ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe lamb was tender so full of flavor , the dessert was divine ! !\n->", + "output": "{\"text\": \"The lamb was tender so full of flavor , the dessert was divine ! !\", \"labels\": \"[{'aspect': 'lamb', 'opinion': 'tender', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'divine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n->the pizza was delivered cold and the cheese was n ' t even fully melted !\n[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: but if you keep it plugged in it ' s great !\n->but if you keep it plugged in it ' s great !\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The waiter was attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waiter was attentive .\n->", + "output": "{\"text\": \"The waiter was attentive .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was wonderful ;\n->service was wonderful ;\n[{'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: greatest thing i ' ve bought myself in a long time .\n->greatest thing i ' ve bought myself in a long time .\n[{'aspect': 'NULL', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The place itself is beautiful the bar scene seems to be happening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place itself is beautiful the bar scene seems to be happening .\n->", + "output": "{\"text\": \"The place itself is beautiful the bar scene seems to be happening .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n->bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n[{'aspect': 'specific unit', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'specific unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: All conveniently delivered right to the door .\n->All conveniently delivered right to the door .\n[{'aspect': 'delivered', 'opinion': 'conveniently', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDowntown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n->", + "output": "{\"text\": \"Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\", \"labels\": \"[{'aspect': 'Appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the display is perfect for me , plenty of brightness and a decent resolution .\n->the display is perfect for me , plenty of brightness and a decent resolution .\n[{'aspect': 'display', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'brightness', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n->the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSmall servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n->", + "output": "{\"text\": \"Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\", \"labels\": \"[{'aspect': 'salmon', 'opinion': 'wasnt impressed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servings for main entree', 'opinion': 'Small', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you go here , do it on his off - night .\n->if you go here , do it on his off - night .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n->i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'larger', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'hdmi', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: Dessert is a joke ... dont bother\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDessert is a joke ... dont bother\n->", + "output": "{\"text\": \"Dessert is a joke ... dont bother\", \"labels\": \"[{'aspect': 'Dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speakers sound tinny .\n->speakers sound tinny .\n[{'aspect': 'speakers', 'opinion': 'tinny', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is uniformly exceptional , with a very capable kitchen which will proudly whip up whatever you feel like eating , whether it 's on the menu or not .\n->The food is uniformly exceptional , with a very capable kitchen which will proudly whip up whatever you feel like eating , whether it 's on the menu or not .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kitchen', 'opinion': 'capable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The restaurant has a Family feel , not least with regard to the portions which are enormous ; the veal alone could have single-handedly solved third world famine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe restaurant has a Family feel , not least with regard to the portions which are enormous ; the veal alone could have single-handedly solved third world famine .\n->", + "output": "{\"text\": \"The restaurant has a Family feel , not least with regard to the portions which are enormous ; the veal alone could have single-handedly solved third world famine .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'Family feel', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good music , great food , speedy service affordable prices .\n->good music , great food , speedy service affordable prices .\n[{'aspect': 'music', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n->not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n[{'aspect': 'wait staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n->", + "output": "{\"text\": \"The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\", \"labels\": \"[{'aspect': 'anti-pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n->if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n[{'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i originally purchased this at the beginning of april after about a week it stopped charging ( would receive ` ` low power charger ` ` connected message and not charge ) .\n->i originally purchased this at the beginning of april after about a week it stopped charging ( would receive ` ` low power charger ` ` connected message and not charge ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: The wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n->", + "output": "{\"text\": \"The wine list is extensive and can easily hike up an otherwise reasonably priced meal .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the m key popped off during initial setup .\n->the m key popped off during initial setup .\n[{'aspect': 'm key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: bought this for my ten year old for school and creating .\n->bought this for my ten year old for school and creating .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: Still , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nStill , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...\n->", + "output": "{\"text\": \"Still , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...\", \"labels\": \"[{'aspect': 'measures of liquers', 'opinion': 'pour-your-own', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'measures of liquers', 'opinion': 'courtesey', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Do n't waste money on decor .\n->Do n't waste money on decor .\n[{'aspect': 'decor', 'opinion': 'waste', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: 10 minutes after finishing this review started having a new issue : touch pad started acting out .\n->10 minutes after finishing this review started having a new issue : touch pad started acting out .\n[{'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: Fantastic place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFantastic place .\n->", + "output": "{\"text\": \"Fantastic place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'Fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n->the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n[{'aspect': 'meal', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'meal', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'restaurant', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: - the touchscreen is very responsive , fast , and so far everything has scaled just fine during use\n->- the touchscreen is very responsive , fast , and so far everything has scaled just fine during use\n[{'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: Great food , great decor , great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food , great decor , great service .\n->", + "output": "{\"text\": \"Great food , great decor , great service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n->needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: do n ' t dine at tamarind for the vegetarian dishes , they are simply not up to par with the non - veg selections .\n->do n ' t dine at tamarind for the vegetarian dishes , they are simply not up to par with the non - veg selections .\n[{'aspect': 'vegetarian dishes', 'opinion': 'not up to par', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'non - veg selections', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->", + "output": "{\"text\": \"This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: , the advertised ram is 16gb and in the answer questions section has answers from the manufacturer saying ` ` this will have 16gb of memory ! ` `\n->, the advertised ram is 16gb and in the answer questions section has answers from the manufacturer saying ` ` this will have 16gb of memory ! ` `\n[{'aspect': 'ram', 'opinion': '16gb', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: battery life is good , screen looks fine , and all of the keys and ports are functional .\n->battery life is good , screen looks fine , and all of the keys and ports are functional .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keys', 'opinion': 'functional', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'ports', 'opinion': 'functional', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: Excellent spot for holiday get togethers with co-workers or friends that you have n't seen in a while .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nExcellent spot for holiday get togethers with co-workers or friends that you have n't seen in a while .\n->", + "output": "{\"text\": \"Excellent spot for holiday get togethers with co-workers or friends that you have n't seen in a while .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n->i loved this chromebook but i had to return it bevause it had sound issues .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keyboard and track pad are both quite good , although i always use a real mouse .\n->the keyboard and track pad are both quite good , although i always use a real mouse .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'track pad', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: I have been doing all of the above at the Heartland Brewery for over 5 years now and I HAVE NEVER BEEN DISAPPOINTED !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have been doing all of the above at the Heartland Brewery for over 5 years now and I HAVE NEVER BEEN DISAPPOINTED !\n->", + "output": "{\"text\": \"I have been doing all of the above at the Heartland Brewery for over 5 years now and I HAVE NEVER BEEN DISAPPOINTED !\", \"labels\": \"[{'aspect': 'Heartland Brewery', 'opinion': 'NEVER BEEN DISAPPOINTED', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the c302 is a great machine .\n->the c302 is a great machine .\n[{'aspect': 'c302', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n->so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\ntext: Not the typical NYC gimmick theme restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot the typical NYC gimmick theme restaurant .\n->", + "output": "{\"text\": \"Not the typical NYC gimmick theme restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'Not the typical', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is light - weight , durable and beautiful .\n->this chromebook is light - weight , durable and beautiful .\n[{'aspect': 'chromebook', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the computer is nice , blah blah it has nice features but it stops working after a few months .\n->the computer is nice , blah blah it has nice features but it stops working after a few months .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: A cool bar with great food , and tons of excellent beer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA cool bar with great food , and tons of excellent beer .\n->", + "output": "{\"text\": \"A cool bar with great food , and tons of excellent beer .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Friendly staff that actually lets you enjoy your meal and the company you 're with .\n->Friendly staff that actually lets you enjoy your meal and the company you 're with .\n[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The pizza was great .\n->The pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The shrimp scampi was excellent and the antipasti were plentiful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe shrimp scampi was excellent and the antipasti were plentiful .\n->", + "output": "{\"text\": \"The shrimp scampi was excellent and the antipasti were plentiful .\", \"labels\": \"[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the back lit keyboard is one of the nicest keyboards i have ever typed on .\n->the back lit keyboard is one of the nicest keyboards i have ever typed on .\n[{'aspect': 'back lit keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboards', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i ' m amazed at this product that has the same build as the other acer chromebooks .\n->i ' m amazed at this product that has the same build as the other acer chromebooks .\n[{'aspect': 'product', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The only thing more wonderful than the food ( which is exceptional ) is the service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only thing more wonderful than the food ( which is exceptional ) is the service .\n->", + "output": "{\"text\": \"The only thing more wonderful than the food ( which is exceptional ) is the service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n->my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n[{'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i just got this thing today & was really excited about it but this has been frustrating & disappointing .\n->i just got this thing today & was really excited about it but this has been frustrating & disappointing .\n[{'aspect': 'NULL', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Cozy romantic atomosphere with only around 15 tables at most .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCozy romantic atomosphere with only around 15 tables at most .\n->", + "output": "{\"text\": \"Cozy romantic atomosphere with only around 15 tables at most .\", \"labels\": \"[{'aspect': 'atomosphere', 'opinion': 'Cozy romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it wouldn ' t charge .\n->it wouldn ' t charge .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the force touch trackpad works great .\n->the force touch trackpad works great .\n[{'aspect': 'force touch trackpad', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\ntext: Service was very prompt but slightly rushed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was very prompt but slightly rushed .\n->", + "output": "{\"text\": \"Service was very prompt but slightly rushed .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'rushed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: right out of the box , this computer is really slow , but two simple steps easily fix that issue .\n->right out of the box , this computer is really slow , but two simple steps easily fix that issue .\n[{'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the steak tartare is a great bet , they fix it for you at the table .\n->the steak tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'steak tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: Food was very good , but not what I would consider out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was very good , but not what I would consider out of this world .\n->", + "output": "{\"text\": \"Food was very good , but not what I would consider out of this world .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also ordered the change mojito , which was out of this world .\n->i also ordered the change mojito , which was out of this world .\n[{'aspect': 'change mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: i ' ve gotten more use out of this thing than i first envisioned .\n->i ' ve gotten more use out of this thing than i first envisioned .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Too bad the food was n't of the same heritage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nToo bad the food was n't of the same heritage .\n->", + "output": "{\"text\": \"Too bad the food was n't of the same heritage .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: power button is right next to the delete key .\n->power button is right next to the delete key .\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n->the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->", + "output": "{\"text\": \"The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\", \"labels\": \"[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n->so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Mermaid Inn is an overall good restaurant with really good seafood .\n->Mermaid Inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: But that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\n->", + "output": "{\"text\": \"But that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\", \"labels\": \"[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great product !\n->great product !\n[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: yakitori ( bbq meats ) is tasty too .\n->yakitori ( bbq meats ) is tasty too .\n[{'aspect': 'yakitori ( bbq meats )', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: I really liked this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI really liked this place .\n->", + "output": "{\"text\": \"I really liked this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n->the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'staff', 'opinion': 'not seem knowledgeable', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: my screen stayed black more than it was on .\n->my screen stayed black more than it was on .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: I also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\n->", + "output": "{\"text\": \"I also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\", \"labels\": \"[{'aspect': 'rice dishes', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'congee ( rice porridge )', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can use this for school .\n->i can use this for school .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the food was undercooked -the sauce watery , and the vegetables raw .\n->the food was undercooked -the sauce watery , and the vegetables raw .\n[{'aspect': 'food', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'raw', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\ntext: It 's definately not a place to go if you want to impress someone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's definately not a place to go if you want to impress someone .\n->", + "output": "{\"text\": \"It 's definately not a place to go if you want to impress someone .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'impress', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n->Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n[{'aspect': 'Asian appetizers', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The pizza is delicious and the proprietor is one of the nicest in NYC .\n->The pizza is delicious and the proprietor is one of the nicest in NYC .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n->", + "output": "{\"text\": \"However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after seven months , the usb - c ports stopped charging .\n->after seven months , the usb - c ports stopped charging .\n[{'aspect': 'usb - c ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: - long battery life\n->- long battery life\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhen you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\n->", + "output": "{\"text\": \"When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\", \"labels\": \"[{'aspect': 'main dining room', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceiling', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceiling', 'opinion': 'hand-painted high', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the green curry ! ! !\n->Try the green curry ! ! !\n[{'aspect': 'green curry', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: dessert is a joke . . . dont bother\n->dessert is a joke . . . dont bother\n[{'aspect': 'dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The food is wonderful , tasty and filling , and the service is professional and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is wonderful , tasty and filling , and the service is professional and friendly .\n->", + "output": "{\"text\": \"The food is wonderful , tasty and filling , and the service is professional and friendly .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'filling', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would recommend Roxy 's for that , but not for their food .\n->I would recommend Roxy 's for that , but not for their food .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: I ca n't wait for summer , when they serve outside on their gigantic patio .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ca n't wait for summer , when they serve outside on their gigantic patio .\n->", + "output": "{\"text\": \"I ca n't wait for summer , when they serve outside on their gigantic patio .\", \"labels\": \"[{'aspect': 'patio', 'opinion': 'gigantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very helpful and it is very fast .\n->it is very helpful and it is very fast .\n[{'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: has ac wireless so you can see the 5ghz on your network , blazing fast .\n->has ac wireless so you can see the 5ghz on your network , blazing fast .\n[{'aspect': 'ac wireless', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: I recently tried Suan and I thought that it was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recently tried Suan and I thought that it was great .\n->", + "output": "{\"text\": \"I recently tried Suan and I thought that it was great .\", \"labels\": \"[{'aspect': 'Suan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: less than three minutes passed before i found myself doubled over the toilet .\n->less than three minutes passed before i found myself doubled over the toilet .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n->", + "output": "{\"text\": \"The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hot sauce', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n->I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'surrounding', 'opinion': 'heart warming', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: transferring files from a non - iphone phone , like android is extremely annoying .\n->transferring files from a non - iphone phone , like android is extremely annoying .\n[{'aspect': 'android', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'SOFTWARE#PORTABILITY'}]\ntext: Good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood food .\n->", + "output": "{\"text\": \"Good food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent product and experience with the purchase .\n->excellent product and experience with the purchase .\n[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: many apps won ' t download and work on it like they do on an ellipsis .\n->many apps won ' t download and work on it like they do on an ellipsis .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Good drink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood drink .\n->", + "output": "{\"text\": \"Good drink .\", \"labels\": \"[{'aspect': 'drink', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is superb\n->battery life is superb\n[{'aspect': 'battery life', 'opinion': 'superb', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: honestly the worst sushi my husband and i had in our entire lives .\n->honestly the worst sushi my husband and i had in our entire lives .\n[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Great spot , whether looking for a couple of drinks or quiet dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat spot , whether looking for a couple of drinks or quiet dinner .\n->", + "output": "{\"text\": \"Great spot , whether looking for a couple of drinks or quiet dinner .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thanks amazon for your great return policy !\n->thanks amazon for your great return policy !\n[{'aspect': 'amazon', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'return policy', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: Great food , great prices , great service .\n->Great food , great prices , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Warm and friendly in the winter and terrific outdoor seating in the warmer months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWarm and friendly in the winter and terrific outdoor seating in the warmer months .\n->", + "output": "{\"text\": \"Warm and friendly in the winter and terrific outdoor seating in the warmer months .\", \"labels\": \"[{'aspect': 'outdoor seating', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard feels nice to use , the keys have a satisfying travel .\n->the keyboard feels nice to use , the keys have a satisfying travel .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keys', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: on that scale , it ' s a world - beater .\n->on that scale , it ' s a world - beater .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: The food is great and they have a good selection of wines at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is great and they have a good selection of wines at reasonable prices .\n->", + "output": "{\"text\": \"The food is great and they have a good selection of wines at reasonable prices .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the internal flash memory is like greased lightning .\n->the internal flash memory is like greased lightning .\n[{'aspect': 'flash memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n->it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: While the ambiance and atmosphere were great , the food and service could have been a lot better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the ambiance and atmosphere were great , the food and service could have been a lot better .\n->", + "output": "{\"text\": \"While the ambiance and atmosphere were great , the food and service could have been a lot better .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have to say I have never had a disapointing meal here .\n->I have to say I have never had a disapointing meal here .\n[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: our visit their to say the least , was an unpleasant and costly experience !\n->our visit their to say the least , was an unpleasant and costly experience !\n[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'costly', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: i 've been to sapphire twice and both times the food was fine , if not good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni 've been to sapphire twice and both times the food was fine , if not good .\n->", + "output": "{\"text\": \"i 've been to sapphire twice and both times the food was fine , if not good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is a lot of variety even for people who eat vegetarian like me .\n->there is a lot of variety even for people who eat vegetarian like me .\n[{'aspect': 'NULL', 'opinion': 'a lot of variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n->on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n[{'aspect': 'trackpad', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: stick with the chicken , beef , and lamb dishes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstick with the chicken , beef , and lamb dishes .\n->", + "output": "{\"text\": \"stick with the chicken , beef , and lamb dishes .\", \"labels\": \"[{'aspect': 'chicken', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb dishes', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The beverages were excellent , and the dessert was good .\n->The beverages were excellent , and the dessert was good .\n[{'aspect': 'beverages', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i completely recommend casa la femme for any special occasion and to really impress your date .\n->i completely recommend casa la femme for any special occasion and to really impress your date .\n[{'aspect': 'casa la femme', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: service is friendly , and never had a problem walking in and getting a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is friendly , and never had a problem walking in and getting a table .\n->", + "output": "{\"text\": \"service is friendly , and never had a problem walking in and getting a table .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have used it for business and school and wouldn ' t want any other computer for the job .\n->i have used it for business and school and wouldn ' t want any other computer for the job .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: quacamole at pacifico is yummy , as are the wings with chimmichuri .\n->quacamole at pacifico is yummy , as are the wings with chimmichuri .\n[{'aspect': 'quacamole', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wings with chimmichuri', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: skip dessert .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nskip dessert .\n->", + "output": "{\"text\": \"skip dessert .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: works well .\n->works well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the atmosphere was great .\n->the atmosphere was great .\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: Best Reuben sandwich ever !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest Reuben sandwich ever !\n->", + "output": "{\"text\": \"Best Reuben sandwich ever !\", \"labels\": \"[{'aspect': 'Reuben sandwich', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was the friendliest that have seen in New York .\n->The staff was the friendliest that have seen in New York .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: boot speed : likewise , very fast -\n->boot speed : likewise , very fast -\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Do n't miss Bloom 's on your next trip to Manhatten .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDo n't miss Bloom 's on your next trip to Manhatten .\n->", + "output": "{\"text\": \"Do n't miss Bloom 's on your next trip to Manhatten .\", \"labels\": \"[{'aspect': \"Bloom 's\", 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s extremely fast and has very little lag when opening pages or surfing the web .\n->it ' s extremely fast and has very little lag when opening pages or surfing the web .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Every course was better than the next .\n->Every course was better than the next .\n[{'aspect': 'course', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Thanks Bloom 's for a lovely trip .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThanks Bloom 's for a lovely trip .\n->", + "output": "{\"text\": \"Thanks Bloom 's for a lovely trip .\", \"labels\": \"[{'aspect': \"Bloom 's\", 'opinion': 'Thanks', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Bloom 's\", 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n->the wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'meal', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: and the worst customer service ever !\n->and the worst customer service ever !\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: The food was not fresh , the sauces were bland and very oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was not fresh , the sauces were bland and very oily .\n->", + "output": "{\"text\": \"The food was not fresh , the sauces were bland and very oily .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromebooks are a waste of time / money .\n->chromebooks are a waste of time / money .\n[{'aspect': 'chromebooks', 'opinion': 'waste', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i just love my asus chromebook , i take it everywhere .\n->i just love my asus chromebook , i take it everywhere .\n[{'aspect': 'asus chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Pizza was a little soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza was a little soggy .\n->", + "output": "{\"text\": \"Pizza was a little soggy .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my only issue is that the fan is very noisy and gets stuck at times , causing worry about the laptop getting overheated .\n->my only issue is that the fan is very noisy and gets stuck at times , causing worry about the laptop getting overheated .\n[{'aspect': 'fan', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\nExample:\ntext: samsung keeps telling me that the serial number is invalid .\n->samsung keeps telling me that the serial number is invalid .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: Ravioli was good ... but I have to say that I found everything a bit overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRavioli was good ... but I have to say that I found everything a bit overpriced .\n->", + "output": "{\"text\": \"Ravioli was good ... but I have to say that I found everything a bit overpriced .\", \"labels\": \"[{'aspect': 'Ravioli', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ravioli', 'opinion': 'overpriced', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'trendi', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: only thing is i am not sure if there is a delete key , something i use a lot\n->only thing is i am not sure if there is a delete key , something i use a lot\n[{'aspect': 'delete key', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: Not enough wines by the glass either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot enough wines by the glass either .\n->", + "output": "{\"text\": \"Not enough wines by the glass either .\", \"labels\": \"[{'aspect': 'wines by the glass', 'opinion': 'Not enough', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sad part is i truly do like acer products , but this made me rethink this purchase .\n->the sad part is i truly do like acer products , but this made me rethink this purchase .\n[{'aspect': 'acer products', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'acer products', 'opinion': 'sad', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food is terrible and overall , I would have to say avoid at all costs .\n->The food is terrible and overall , I would have to say avoid at all costs .\n[{'aspect': 'food', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n->", + "output": "{\"text\": \"Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speaking of asus flip itself - it is great in every way !\n->speaking of asus flip itself - it is great in every way !\n[{'aspect': 'asus flip', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: on the other hand , it is not an easy task to open and replace hdd .\n->on the other hand , it is not an easy task to open and replace hdd .\n[{'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: This place is a great bargain .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is a great bargain .\n->", + "output": "{\"text\": \"This place is a great bargain .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the filet mignon dish was superb !\n->the filet mignon dish was superb !\n[{'aspect': 'filet mignon dish', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: all in all a great cheap gaming laptop that even with the cons i am not dissatisfied with the product .\n->all in all a great cheap gaming laptop that even with the cons i am not dissatisfied with the product .\n[{'aspect': 'gaming laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Authentic Pakistani food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAuthentic Pakistani food .\n->", + "output": "{\"text\": \"Authentic Pakistani food .\", \"labels\": \"[{'aspect': 'Pakistani food', 'opinion': 'Authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n->there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n[{'aspect': 'screen resolution', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'not working well', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: Because of the delicate thin crust , take-out pies get soggy in their boxes .\n->Because of the delicate thin crust , take-out pies get soggy in their boxes .\n[{'aspect': 'take-out pies', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'delicate', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Just straight up cheap , good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nJust straight up cheap , good food .\n->", + "output": "{\"text\": \"Just straight up cheap , good food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n->a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n[{'aspect': 'chrome os devices', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: Faan is sooo good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFaan is sooo good .\n->", + "output": "{\"text\": \"Faan is sooo good .\", \"labels\": \"[{'aspect': 'Faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hands down the best pizza on the planet .\n->hands down the best pizza on the planet .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The best pad thai i 've ever had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe best pad thai i 've ever had .\n->", + "output": "{\"text\": \"The best pad thai i 've ever had .\", \"labels\": \"[{'aspect': 'pad thai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i recieved the item i was amazed at the quality of it .\n->when i recieved the item i was amazed at the quality of it .\n[{'aspect': 'item', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n->even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: The design and atmosphere is just as good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe design and atmosphere is just as good .\n->", + "output": "{\"text\": \"The design and atmosphere is just as good .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love pizza 33 . . .\n->love pizza 33 . . .\n[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i already returned the first laptop because i had to press extremely hard to get the left click to work .\n->i already returned the first laptop because i had to press extremely hard to get the left click to work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\n->", + "output": "{\"text\": \"The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\", \"labels\": \"[{'aspect': 'mussles', 'opinion': 'fishiest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seabass', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'goat cheese salad', 'opinion': 'missing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'penne w/ chicken', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: highly recommend it !\n->highly recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: great buy .\n->great buy .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Nice atmosphere , the service was very pleasant and the desert was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNice atmosphere , the service was very pleasant and the desert was good .\n->", + "output": "{\"text\": \"Nice atmosphere , the service was very pleasant and the desert was good .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'Nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen resolution is good .\n->screen resolution is good .\n[{'aspect': 'screen resolution', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: it worked beautifully and smoothly .\n->it worked beautifully and smoothly .\n[{'aspect': 'NULL', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The food is amazing , rich pastas and fresh doughy pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is amazing , rich pastas and fresh doughy pizza .\n->", + "output": "{\"text\": \"The food is amazing , rich pastas and fresh doughy pizza .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastas', 'opinion': 'rich', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'fresh doughy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Unlike other places in NYC where the sandwiches you want only come as a triple-decker , here you can get what you want in a reasonably-sized portion ( and price ) .\n->Unlike other places in NYC where the sandwiches you want only come as a triple-decker , here you can get what you want in a reasonably-sized portion ( and price ) .\n[{'aspect': 'portion', 'opinion': 'reasonably-sized', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the pro is by far the best .\n->the pro is by far the best .\n[{'aspect': 'pro', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Best of all is the warm vibe , the owner is super friendly and service is fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest of all is the warm vibe , the owner is super friendly and service is fast .\n->", + "output": "{\"text\": \"Best of all is the warm vibe , the owner is super friendly and service is fast .\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n->5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n[{'aspect': 'emmc storage', 'opinion': 'slower', 'polarity': 'negative', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: they have great rolls , the triple color and norwegetan rolls , are awesome and filling .\n->they have great rolls , the triple color and norwegetan rolls , are awesome and filling .\n[{'aspect': 'rolls', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'triple color and norwegetan rolls', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'triple color and norwegetan rolls', 'opinion': 'filling', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: Ask for Usha , the nicest bartender in manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAsk for Usha , the nicest bartender in manhattan .\n->", + "output": "{\"text\": \"Ask for Usha , the nicest bartender in manhattan .\", \"labels\": \"[{'aspect': 'Usha', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n->i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i wo n ' t go back unless someone else is footing the bill .\n->i wo n ' t go back unless someone else is footing the bill .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: My fav was the sassy lassi ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy fav was the sassy lassi ...\n->", + "output": "{\"text\": \"My fav was the sassy lassi ...\", \"labels\": \"[{'aspect': 'sassy lassi', 'opinion': 'fav', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little guy fits the bill perfectly .\n->this little guy fits the bill perfectly .\n[{'aspect': 'guy', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: its a good computer for the price but it needs to work awhile without crashing within 6 months .\n->its a good computer for the price but it needs to work awhile without crashing within 6 months .\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: Toons has recently been redone , so it 's now a very attractive space .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nToons has recently been redone , so it 's now a very attractive space .\n->", + "output": "{\"text\": \"Toons has recently been redone , so it 's now a very attractive space .\", \"labels\": \"[{'aspect': 'Toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is my only device with this issue in my home .\n->it is my only device with this issue in my home .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: price is good\n->price is good\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The food 's as good as ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food 's as good as ever .\n->", + "output": "{\"text\": \"The food 's as good as ever .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mac really knows how to build a good dev laptop\n->mac really knows how to build a good dev laptop\n[{'aspect': 'mac', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: service was also very good .\n->service was also very good .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: In an area sadly lacking in decent Thai food , this is one of the best spots .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn an area sadly lacking in decent Thai food , this is one of the best spots .\n->", + "output": "{\"text\": \"In an area sadly lacking in decent Thai food , this is one of the best spots .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n->we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: most of my android apps have worked well ( i have had minor issues with a couple ) .\n->most of my android apps have worked well ( i have had minor issues with a couple ) .\n[{'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Went here last night - nice decor , good service , but the food was surprisingly excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWent here last night - nice decor , good service , but the food was surprisingly excellent .\n->", + "output": "{\"text\": \"Went here last night - nice decor , good service , but the food was surprisingly excellent .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard is a bit nicer , not tons .\n->keyboard is a bit nicer , not tons .\n[{'aspect': 'keyboard', 'opinion': 'nicer', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: nice laptop !\n->nice laptop !\n[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n->", + "output": "{\"text\": \"The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n->sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n[{'aspect': 'c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: problem is nothing at prune is particularly memorable .\n->problem is nothing at prune is particularly memorable .\n[{'aspect': 'prune', 'opinion': 'memorable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: The service is good and the resturant is clean .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is good and the resturant is clean .\n->", + "output": "{\"text\": \"The service is good and the resturant is clean .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'resturant', 'opinion': 'clean', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , i think it is a great purchase .\n->overall , i think it is a great purchase .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: you should travel from the bronx to try it . . .\n->you should travel from the bronx to try it . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Rao 's has the best service and atmosphere in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRao 's has the best service and atmosphere in NYC .\n->", + "output": "{\"text\": \"Rao 's has the best service and atmosphere in NYC .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: atmosphere is nice and relaxed too . . .\n->atmosphere is nice and relaxed too . . .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: an awesome product , well built - well worth your time and money .\n->an awesome product , well built - well worth your time and money .\n[{'aspect': 'product', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'well built', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'product', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: My roommate and I LOVE this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy roommate and I LOVE this place .\n->", + "output": "{\"text\": \"My roommate and I LOVE this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the pad thai , it ' s fabulous and their prices are so cheap !\n->try the pad thai , it ' s fabulous and their prices are so cheap !\n[{'aspect': 'pad thai', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'so cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: - the screen is great in all aspects .\n->- the screen is great in all aspects .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n->", + "output": "{\"text\": \"We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\", \"labels\": \"[{'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just one question , now i am running the os on the ssd and the original 1tb harddisk just for storing file , its boot up and start softwares very fast , but operating with file explore is very slow , i already set the file explore not open the quick access but it still slow , rename will effective after seconds , copy file and even right click sometimes need wait for minutes , who know how can i solve it ?\n->just one question , now i am running the os on the ssd and the original 1tb harddisk just for storing file , its boot up and start softwares very fast , but operating with file explore is very slow , i already set the file explore not open the quick access but it still slow , rename will effective after seconds , copy file and even right click sometimes need wait for minutes , who know how can i solve it ?\n[{'aspect': 'boot up', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n->this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: Indoor was very cozy and cute .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIndoor was very cozy and cute .\n->", + "output": "{\"text\": \"Indoor was very cozy and cute .\", \"labels\": \"[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n->the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n[{'aspect': 'aluminum casing', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it was divine melts in your mouth .\n->it was divine melts in your mouth .\n[{'aspect': 'NULL', 'opinion': 'divine', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The portion sizes here are huge , and the sushi is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portion sizes here are huge , and the sushi is good .\n->", + "output": "{\"text\": \"The portion sizes here are huge , and the sushi is good .\", \"labels\": \"[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n->The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n[{'aspect': 'thai food', 'opinion': 'better', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n->For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n[{'aspect': 'Paneer Roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Excellent dumplings served amid clean , chic decor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nExcellent dumplings served amid clean , chic decor .\n->", + "output": "{\"text\": \"Excellent dumplings served amid clean , chic decor .\", \"labels\": \"[{'aspect': 'dumplings', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'clean', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'chic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there was a small wait , but shorter than i expected .\n->there was a small wait , but shorter than i expected .\n[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Bagels are ok , but be sure not to make any special requests !\n->Bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The food was delicious but do not come here on a empty stomach .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was delicious but do not come here on a empty stomach .\n->", + "output": "{\"text\": \"The food was delicious but do not come here on a empty stomach .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am still disastified even if this was a replacement\n->i am still disastified even if this was a replacement\n[{'aspect': 'replacement', 'opinion': 'disastified', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n->i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n[{'aspect': 'waiter', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The portions are small but being that the food was so good makes up for that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portions are small but being that the food was so good makes up for that .\n->", + "output": "{\"text\": \"The portions are small but being that the food was so good makes up for that .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n->i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: this is a great notebook .\n->this is a great notebook .\n[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The staff there is very attentive and down to earth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff there is very attentive and down to earth .\n->", + "output": "{\"text\": \"The staff there is very attentive and down to earth .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The combination of super-fresh ingredients in the dishes are unusual but really delicious .\n->The combination of super-fresh ingredients in the dishes are unusual but really delicious .\n[{'aspect': 'ingredients', 'opinion': 'super-fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: overall , it ' s a great machine .\n->overall , it ' s a great machine .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Great Indian food and the service is incredible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat Indian food and the service is incredible .\n->", + "output": "{\"text\": \"Great Indian food and the service is incredible .\", \"labels\": \"[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop is a little bigger than i had hoped and heavier than i expected it to feel .\n->the laptop is a little bigger than i had hoped and heavier than i expected it to feel .\n[{'aspect': 'laptop', 'opinion': 'bigger', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i like the ease of connecting to the internet wi - fi .\n->i like the ease of connecting to the internet wi - fi .\n[{'aspect': 'wi - fi', 'opinion': 'ease', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: Great food and the prices are very reasonable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food and the prices are very reasonable .\n->", + "output": "{\"text\": \"Great food and the prices are very reasonable .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: planet thai is great !\n->planet thai is great !\n[{'aspect': 'planet thai', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: prices are very good .\n->prices are very good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food here does a great service to the name ( Cantonese that is ... ) .\n->", + "output": "{\"text\": \"The food here does a great service to the name ( Cantonese that is ... ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n->i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n->", + "output": "{\"text\": \"I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\", \"labels\": \"[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has a quality construction making it feel like a much more expensive laptop and the performance is perfect for those who use a chromebook for everyday computing and entertainment .\n->it has a quality construction making it feel like a much more expensive laptop and the performance is perfect for those who use a chromebook for everyday computing and entertainment .\n[{'aspect': 'performance', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the sound is not real loud from the speakers , but i am reasonably pleased with the quality .\n->the sound is not real loud from the speakers , but i am reasonably pleased with the quality .\n[{'aspect': 'sound', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'speakers', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: This dish is my favorite and I always get it when I go there and never get tired of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis dish is my favorite and I always get it when I go there and never get tired of it .\n->", + "output": "{\"text\": \"This dish is my favorite and I always get it when I go there and never get tired of it .\", \"labels\": \"[{'aspect': 'dish', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n->The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n[{'aspect': 'parathas', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kebabs', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: so latest update the charger only last about one year and the half .\n->so latest update the charger only last about one year and the half .\n[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n->", + "output": "{\"text\": \"Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\", \"labels\": \"[{'aspect': 'Ow Ley Soh', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ow Ley Soh', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Love YUKA .\n->Love YUKA .\n[{'aspect': 'YUKA', 'opinion': 'Love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n->this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n[{'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: Simply some good tasting Chinese food at incredible prices ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSimply some good tasting Chinese food at incredible prices ...\n->", + "output": "{\"text\": \"Simply some good tasting Chinese food at incredible prices ...\", \"labels\": \"[{'aspect': 'Chinese food', 'opinion': 'good tasting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: fish is so very fresh .\n->fish is so very fresh .\n[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Big Wong is a great place to eat and fill your stomach .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBig Wong is a great place to eat and fill your stomach .\n->", + "output": "{\"text\": \"Big Wong is a great place to eat and fill your stomach .\", \"labels\": \"[{'aspect': 'Big Wong', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fast delivery , brand new as expected .\n->fast delivery , brand new as expected .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\nExample:\ntext: great except came with a bad left hinge .\n->great except came with a bad left hinge .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hinge', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: good music , great food , speedy service affordable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood music , great food , speedy service affordable prices .\n->", + "output": "{\"text\": \"good music , great food , speedy service affordable prices .\", \"labels\": \"[{'aspect': 'music', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first of all dal bukhara rocks .\n->first of all dal bukhara rocks .\n[{'aspect': 'dal bukhara rocks .', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i love this chromebook !\n->i love this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Consistently good Japanese Tapas .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nConsistently good Japanese Tapas .\n->", + "output": "{\"text\": \"Consistently good Japanese Tapas .\", \"labels\": \"[{'aspect': 'Japanese Tapas', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lenovo should put a better battery in it , and should make a retrofit available .\n->lenovo should put a better battery in it , and should make a retrofit available .\n[{'aspect': 'better', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#DESIGN_FEATURES'}]\nExample:\ntext: This place , however , has a lot less pretension than Joya and the Thai food is still above average .\n->This place , however , has a lot less pretension than Joya and the Thai food is still above average .\n[{'aspect': 'Thai food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Always good drinks and service is pretty good ;\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlways good drinks and service is pretty good ;\n->", + "output": "{\"text\": \"Always good drinks and service is pretty good ;\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great value for the quality ingredients .\n->Great value for the quality ingredients .\n[{'aspect': 'ingredients', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Super friendly and knowledgable staff , fabulous bistro fare and a wonderful jazz brunch with great live jazz ( the chilaquiles were awesome !\n->Super friendly and knowledgable staff , fabulous bistro fare and a wonderful jazz brunch with great live jazz ( the chilaquiles were awesome !\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bistro fare', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chilaquiles', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'jazz brunch', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'live jazz', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Atmosphere is nice and relaxed too ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAtmosphere is nice and relaxed too ...\n->", + "output": "{\"text\": \"Atmosphere is nice and relaxed too ...\", \"labels\": \"[{'aspect': 'Atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n->i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n[{'aspect': 'computer', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this restaurant was way overhyped .\n->this restaurant was way overhyped .\n[{'aspect': 'restaurant', 'opinion': 'overhyped', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: A great place to meet up for some food and drinks ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA great place to meet up for some food and drinks ...\n->", + "output": "{\"text\": \"A great place to meet up for some food and drinks ...\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n->service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'setting / atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n->a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n[{'aspect': 'gentleman', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Yakitori ( bbq meats ) is tasty too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYakitori ( bbq meats ) is tasty too .\n->", + "output": "{\"text\": \"Yakitori ( bbq meats ) is tasty too .\", \"labels\": \"[{'aspect': 'Yakitori ( bbq meats )', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n->We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n[{'aspect': 'desserts', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cannoli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i love how i can charge my laptop on both sides of the device .\n->i love how i can charge my laptop on both sides of the device .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: If you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n->", + "output": "{\"text\": \"If you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'low quality', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi chef', 'opinion': 'miserable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is quite disappointing .\n->it is quite disappointing .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: while this thing is gorgeous and the perfect size for what i was looking for , my initial display died after not even 12 hours of use .\n->while this thing is gorgeous and the perfect size for what i was looking for , my initial display died after not even 12 hours of use .\n[{'aspect': 'NULL', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Fabulous decor - makes you feel like you 're in a trendy Manhattan restaurant , very very good food , cheaply-priced , generally friendly staff , and if you 're a Manhattanite , or spend most of your time in Manhattan , Rice Avenue will make you feel at home ... ..very Soho/Village/Upper West Side minus the expensive prices and pretentious clientele ... ..all on Roosevelt Avenue !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFabulous decor - makes you feel like you 're in a trendy Manhattan restaurant , very very good food , cheaply-priced , generally friendly staff , and if you 're a Manhattanite , or spend most of your time in Manhattan , Rice Avenue will make you feel at home ... ..very Soho/Village/Upper West Side minus the expensive prices and pretentious clientele ... ..all on Roosevelt Avenue !\n->", + "output": "{\"text\": \"Fabulous decor - makes you feel like you 're in a trendy Manhattan restaurant , very very good food , cheaply-priced , generally friendly staff , and if you 're a Manhattanite , or spend most of your time in Manhattan , Rice Avenue will make you feel at home ... ..very Soho/Village/Upper West Side minus the expensive prices and pretentious clientele ... ..all on Roosevelt Avenue !\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'Fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'cheaply-priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service has always been friendly and efficient .\n->Service has always been friendly and efficient .\n[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The wine list is also really nice .\n->The wine list is also really nice .\n[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOne would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n->", + "output": "{\"text\": \"One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'snobby', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is one of my favorite restaurants and it is not to be missed .\n->this is one of my favorite restaurants and it is not to be missed .\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: We have been to this place many times , and always have great food , wine , and service .\n->We have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWith so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n->", + "output": "{\"text\": \"With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait-staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen is nice .\n->screen is nice .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n->my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n[{'aspect': 'place', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n->", + "output": "{\"text\": \"I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: surprisingly to me , the tablet form has been better than expected for reading .\n->surprisingly to me , the tablet form has been better than expected for reading .\n[{'aspect': 'tablet form', 'opinion': 'surprisingly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'tablet form', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: all of it feels very strudy ( within reason ) and has a nice , modern look and feel to it .\n->all of it feels very strudy ( within reason ) and has a nice , modern look and feel to it .\n[{'aspect': 'NULL', 'opinion': 'strudy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'modern', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The only problem is that the manager is a complete incompetent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only problem is that the manager is a complete incompetent .\n->", + "output": "{\"text\": \"The only problem is that the manager is a complete incompetent .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very junky\n->very junky\n[{'aspect': 'junky', 'opinion': 'junky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n->once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n[{'aspect': 'cosette', 'opinion': 'off - the - beaten', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: He offers subpar service and has no personality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHe offers subpar service and has no personality .\n->", + "output": "{\"text\": \"He offers subpar service and has no personality .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the people who want great food plus great service , roxy is a place to avoid !\n->for the people who want great food plus great service , roxy is a place to avoid !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Try the green curry ! ! !\n->Try the green curry ! ! !\n[{'aspect': 'green curry', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: There is no excuse for such lousy service !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere is no excuse for such lousy service !\n->", + "output": "{\"text\": \"There is no excuse for such lousy service !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n->while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n[{'aspect': 'stock aluminum case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n->the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'plus', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: I have never before eaten 40 pieces of relatively good nigiri .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have never before eaten 40 pieces of relatively good nigiri .\n->", + "output": "{\"text\": \"I have never before eaten 40 pieces of relatively good nigiri .\", \"labels\": \"[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n->a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n[{'aspect': 'gentleman', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: delicious , creative and fun .\n->delicious , creative and fun .\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n->", + "output": "{\"text\": \"I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\", \"labels\": \"[{'aspect': 'Areo', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We got a little tipsy from the sake but is n't that what Saturday nights with the girlfriends are all about ?\n->We got a little tipsy from the sake but is n't that what Saturday nights with the girlfriends are all about ?\n[{'aspect': 'sake', 'opinion': 'tipsy', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: this little computer is awesome and it was so inexpensive for what you get !\n->this little computer is awesome and it was so inexpensive for what you get !\n[{'aspect': 'computer', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: Most of the servers are very attentive , friendly and quite attractive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMost of the servers are very attentive , friendly and quite attractive .\n->", + "output": "{\"text\": \"Most of the servers are very attentive , friendly and quite attractive .\", \"labels\": \"[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my shiny speedy asus chromebook froze after only one month of use and i am returning it today for a full refund .\n->my shiny speedy asus chromebook froze after only one month of use and i am returning it today for a full refund .\n[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: highly impressed from the decor to the food to the hospitality to the great night i had !\n->highly impressed from the decor to the food to the hospitality to the great night i had !\n[{'aspect': 'decor', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'hospitality', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n->", + "output": "{\"text\": \"Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'view of the new york city skiline', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he ' s very happy with it .\n->he ' s very happy with it .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great build materials and quality .\n->great build materials and quality .\n[{'aspect': 'build materials', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: I would highly recommand requesting a table by the window .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would highly recommand requesting a table by the window .\n->", + "output": "{\"text\": \"I would highly recommand requesting a table by the window .\", \"labels\": \"[{'aspect': 'table by the window', 'opinion': 'recommand', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i love how much you can customize this and also it is pretty speedy !\n->i love how much you can customize this and also it is pretty speedy !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlthough they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n->", + "output": "{\"text\": \"Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They have authentic Indian at amazin prices .\n->They have authentic Indian at amazin prices .\n[{'aspect': 'Indian', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'amazin', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n->5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n[{'aspect': 'emmc storage', 'opinion': 'slower', 'polarity': 'negative', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: The food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is great .\n->", + "output": "{\"text\": \"The food is great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n->Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i love it , it is as shown on the picture\n->i love it , it is as shown on the picture\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Personal pans are the perfect size for those hungry nights .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPersonal pans are the perfect size for those hungry nights .\n->", + "output": "{\"text\": \"Personal pans are the perfect size for those hungry nights .\", \"labels\": \"[{'aspect': 'Personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s fast , connects quickly to wifi , and the screen is quite nice .\n->it ' s fast , connects quickly to wifi , and the screen is quite nice .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: And they provided a delicious dessert on the house !\n->And they provided a delicious dessert on the house !\n[{'aspect': 'dessert', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n->", + "output": "{\"text\": \"There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\", \"labels\": \"[{'aspect': 'delivery guys', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n->still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n[{'aspect': 'measures of liquers', 'opinion': 'pour - your - own', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'measures of liquers', 'opinion': 'courtesey', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n->apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#OPERATION_PERFORMANCE'}]\ntext: Love the scene first off- the place has a character and nice light to it..very fortunate , location wise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLove the scene first off- the place has a character and nice light to it..very fortunate , location wise .\n->", + "output": "{\"text\": \"Love the scene first off- the place has a character and nice light to it..very fortunate , location wise .\", \"labels\": \"[{'aspect': 'scene', 'opinion': 'Love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'location', 'opinion': 'fortunate', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n->most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: great aesthetics .\n->great aesthetics .\n[{'aspect': 'aesthetics', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The pizza was pretty good and huge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza was pretty good and huge .\n->", + "output": "{\"text\": \"The pizza was pretty good and huge .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the backlit keyboard looks nice .\n->- the backlit keyboard looks nice .\n[{'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: A few tips : skip the turnip cake , roast pork buns and egg custards .\n->A few tips : skip the turnip cake , roast pork buns and egg custards .\n[{'aspect': 'turnip cake', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'roast pork buns', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'egg custards', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}]\ntext: We were 4 and got the family size penne a la vodka which was tremendously gigantic portion ... a bucket of food literally .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were 4 and got the family size penne a la vodka which was tremendously gigantic portion ... a bucket of food literally .\n->", + "output": "{\"text\": \"We were 4 and got the family size penne a la vodka which was tremendously gigantic portion ... a bucket of food literally .\", \"labels\": \"[{'aspect': 'penne a la vodka', 'opinion': 'gigantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have used this computer daily now for about 6 months , spending hours per day on it for an emt / paramedic class .\n->i have used this computer daily now for about 6 months , spending hours per day on it for an emt / paramedic class .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: i am fully satisfied with the purchase and communication with the seller was great .\n->i am fully satisfied with the purchase and communication with the seller was great .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'seller', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: The pasta penne was pretty extra buttery , creamy which means a big task to diggest.. tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne ... got a little moody afterwards cause was stuffed ... lol\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pasta penne was pretty extra buttery , creamy which means a big task to diggest.. tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne ... got a little moody afterwards cause was stuffed ... lol\n->", + "output": "{\"text\": \"The pasta penne was pretty extra buttery , creamy which means a big task to diggest.. tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne ... got a little moody afterwards cause was stuffed ... lol\", \"labels\": \"[{'aspect': 'pasta penne', 'opinion': 'buttery', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the view is spectacular , and the food is great .\n->the view is spectacular , and the food is great .\n[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: speed : it ' s very fast .\n->speed : it ' s very fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: La Rosa waltzes in , and I think they are doing it the best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLa Rosa waltzes in , and I think they are doing it the best .\n->", + "output": "{\"text\": \"La Rosa waltzes in , and I think they are doing it the best .\", \"labels\": \"[{'aspect': 'La Rosa', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service is top notch .\n->Service is top notch .\n[{'aspect': 'Service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: everything looked great .\n->everything looked great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Interesting selection , good wines , service fine , fun decor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nInteresting selection , good wines , service fine , fun decor .\n->", + "output": "{\"text\": \"Interesting selection , good wines , service fine , fun decor .\", \"labels\": \"[{'aspect': 'wines', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'selection', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n->6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n[{'aspect': 'hd touch', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'intel celeron n3150', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'CPU#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#GENERAL'}, {'aspect': 'cb5 - 132t - c1lk', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: good music , great food , speedy service affordable prices .\n->good music , great food , speedy service affordable prices .\n[{'aspect': 'music', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: I found it on a cold night , the perfect spot to warm up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI found it on a cold night , the perfect spot to warm up .\n->", + "output": "{\"text\": \"I found it on a cold night , the perfect spot to warm up .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The shrimp scampi was excellent and the antipasti were plentiful .\n->The shrimp scampi was excellent and the antipasti were plentiful .\n[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Always good drinks and service is pretty good ;\n->Always good drinks and service is pretty good ;\n[{'aspect': 'drinks', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I recieved prompt service with a smile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recieved prompt service with a smile .\n->", + "output": "{\"text\": \"I recieved prompt service with a smile .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was a great surprise .\n->this was a great surprise .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n->so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: This place blew me away ... by far my new favorite restaurant on the uppereast side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place blew me away ... by far my new favorite restaurant on the uppereast side .\n->", + "output": "{\"text\": \"This place blew me away ... by far my new favorite restaurant on the uppereast side .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stay far away from this laptop !\n->stay far away from this laptop !\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: big screen !\n->big screen !\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: The wine list is extensive and impressive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is extensive and impressive .\n->", + "output": "{\"text\": \"The wine list is extensive and impressive .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n->you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: from the moment i opened it , i was thoroughly pleased .\n->from the moment i opened it , i was thoroughly pleased .\n[{'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: LOVE the atmosphere - felt like I was in Paris .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLOVE the atmosphere - felt like I was in Paris .\n->", + "output": "{\"text\": \"LOVE the atmosphere - felt like I was in Paris .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere is relaxed and casual .\n->the atmosphere is relaxed and casual .\n[{'aspect': 'atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: If you love wine and cheese and delicious french fare , you 'll love Artisanal !\n->If you love wine and cheese and delicious french fare , you 'll love Artisanal !\n[{'aspect': 'wine', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french fare', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n->", + "output": "{\"text\": \"The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\", \"labels\": \"[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only real upgrade for the new one , before adding on options is faster memory .\n->the only real upgrade for the new one , before adding on options is faster memory .\n[{'aspect': 'memory', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The best Chicken pad tai , I 've ever had .\n->The best Chicken pad tai , I 've ever had .\n[{'aspect': 'Chicken pad tai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n->", + "output": "{\"text\": \"I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n->My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this acer is a web surfer that ' s easy to travel with .\n->this acer is a web surfer that ' s easy to travel with .\n[{'aspect': 'acer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOver the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n->", + "output": "{\"text\": \"Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\", \"labels\": \"[{'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly .\n->Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I am not a vegetarian but , almost all the dishes were great .\n->I am not a vegetarian but , almost all the dishes were great .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I 've also been amazed at all the new additions in the past few years : A new Jazz Bar , the most fantastic Dining Garden , the Best Thin Crust Pizzas , and now a Lasagna Menu which is to die for ( these are not your average lasagnas ) !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've also been amazed at all the new additions in the past few years : A new Jazz Bar , the most fantastic Dining Garden , the Best Thin Crust Pizzas , and now a Lasagna Menu which is to die for ( these are not your average lasagnas ) !\n->", + "output": "{\"text\": \"I 've also been amazed at all the new additions in the past few years : A new Jazz Bar , the most fantastic Dining Garden , the Best Thin Crust Pizzas , and now a Lasagna Menu which is to die for ( these are not your average lasagnas ) !\", \"labels\": \"[{'aspect': 'Dining Garden', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Jazz Bar', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thin Crust Pizzas', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Lasagna Menu', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the home screen is blank with a customizable photo that you can add but that ' s it .\n->the home screen is blank with a customizable photo that you can add but that ' s it .\n[{'aspect': 'home screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s already bricked and i did n ' t even use it for more than one day .\n->it ' s already bricked and i did n ' t even use it for more than one day .\n[{'aspect': 'NULL', 'opinion': 'bricked', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: I lOVE THIS PLACE !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI lOVE THIS PLACE !\n->", + "output": "{\"text\": \"I lOVE THIS PLACE !\", \"labels\": \"[{'aspect': 'PLACE', 'opinion': 'lOVE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n->this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n[{'aspect': 'restaurant', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: for the price , you can not eat this well in manhattan .\n->for the price , you can not eat this well in manhattan .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: I have to say I have never had a disapointing meal here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have to say I have never had a disapointing meal here .\n->", + "output": "{\"text\": \"I have to say I have never had a disapointing meal here .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was good not great not worth the wait or another visit\n->food was good not great not worth the wait or another visit\n[{'aspect': 'food', 'opinion': 'good not great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Their whitefish salad is excellent -- all whitefish with a little mayo .\n->Their whitefish salad is excellent -- all whitefish with a little mayo .\n[{'aspect': 'whitefish salad', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'whitefish', 'opinion': 'all', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mayo', 'opinion': 'little', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We could have made a meal of the yummy dumplings from the dumpling menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe could have made a meal of the yummy dumplings from the dumpling menu .\n->", + "output": "{\"text\": \"We could have made a meal of the yummy dumplings from the dumpling menu .\", \"labels\": \"[{'aspect': 'dumplings', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n->The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n[{'aspect': 'service', 'opinion': 'busy', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Amma is nothing special .\n->Amma is nothing special .\n[{'aspect': 'Amma', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Luckily we saved room for the BBQ Salmon , Sea Bass and Crispy Duck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLuckily we saved room for the BBQ Salmon , Sea Bass and Crispy Duck .\n->", + "output": "{\"text\": \"Luckily we saved room for the BBQ Salmon , Sea Bass and Crispy Duck .\", \"labels\": \"[{'aspect': 'BBQ Salmon', 'opinion': 'Luckily', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Sea Bass', 'opinion': 'Luckily', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Crispy Duck', 'opinion': 'Luckily', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not having to switch to / from desktop version of websites is great .\n->not having to switch to / from desktop version of websites is great .\n[{'aspect': 'websites', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n->If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: But the staff was so horrible to us .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the staff was so horrible to us .\n->", + "output": "{\"text\": \"But the staff was so horrible to us .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n->google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n[{'aspect': \"google ' s own services\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: the waitstaffs are nice though .\n->the waitstaffs are nice though .\n[{'aspect': 'waitstaffs', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTo be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n->", + "output": "{\"text\": \"To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their calzones are horrific , bad , vomit-inducing , YUCK .\n->Their calzones are horrific , bad , vomit-inducing , YUCK .\n[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'vomit-inducing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'YUCK', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: first went here to enjoy their garden terrace .\n->first went here to enjoy their garden terrace .\n[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The food is uniformly exceptional , with a very capable kitchen which will proudly whip up whatever you feel like eating , whether it 's on the menu or not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is uniformly exceptional , with a very capable kitchen which will proudly whip up whatever you feel like eating , whether it 's on the menu or not .\n->", + "output": "{\"text\": \"The food is uniformly exceptional , with a very capable kitchen which will proudly whip up whatever you feel like eating , whether it 's on the menu or not .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kitchen', 'opinion': 'capable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: surprisingly nothing could be further from the truth .\n->surprisingly nothing could be further from the truth .\n[{'aspect': 'NULL', 'opinion': 'surprisingly', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: anyway , if you are contemplating buying one of these and they are still available , do it .\n->anyway , if you are contemplating buying one of these and they are still available , do it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Our agreed favorite is the orrechiete with sausage and chicken ( usually the waiters are kind enough to split the dish in half so you get to sample both meats ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur agreed favorite is the orrechiete with sausage and chicken ( usually the waiters are kind enough to split the dish in half so you get to sample both meats ) .\n->", + "output": "{\"text\": \"Our agreed favorite is the orrechiete with sausage and chicken ( usually the waiters are kind enough to split the dish in half so you get to sample both meats ) .\", \"labels\": \"[{'aspect': 'orrechiete with sausage and chicken', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Finally a reliable Chinese restaurant !\n->Finally a reliable Chinese restaurant !\n[{'aspect': 'Chinese restaurant', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: People are always friendly .\n->People are always friendly .\n[{'aspect': 'People', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n->", + "output": "{\"text\": \"The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\", \"labels\": \"[{'aspect': 'Bagels', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'chewy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'gummy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n->for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: although 4 gigs of ram doesn ' t seem like a lot to some of us , because of the simple software , it is very fast with no lag .\n->although 4 gigs of ram doesn ' t seem like a lot to some of us , because of the simple software , it is very fast with no lag .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#QUALITY'}, {'aspect': 'software', 'opinion': 'simple', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Nevertheless the food itself is pretty good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNevertheless the food itself is pretty good .\n->", + "output": "{\"text\": \"Nevertheless the food itself is pretty good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very low quality build and quality support .\n->very low quality build and quality support .\n[{'aspect': 'quality build', 'opinion': 'low', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'quality support', 'opinion': 'low', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: during the course of the past 3 months , the chef and staff changed and it was not for the better .\n->during the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: They did not have mayonnaise , forgot our toast , left out ingredients ( ie cheese in an omelet ) , below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey did not have mayonnaise , forgot our toast , left out ingredients ( ie cheese in an omelet ) , below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it .\n->", + "output": "{\"text\": \"They did not have mayonnaise , forgot our toast , left out ingredients ( ie cheese in an omelet ) , below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it .\", \"labels\": \"[{'aspect': 'toast', 'opinion': 'forgot', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bacon', 'opinion': 'over cooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'left out', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'left out', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'plate', 'opinion': 'over cooked', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'omelet', 'opinion': 'left out', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the support website is incompetent .\n->the support website is incompetent .\n[{'aspect': 'support website', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: customer service told me that i ' d have to buy a new one , item is still under warranty .\n->customer service told me that i ' d have to buy a new one , item is still under warranty .\n[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: The design and atmosphere is just as good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe design and atmosphere is just as good .\n->", + "output": "{\"text\": \"The design and atmosphere is just as good .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: stable , long battery life , and great build .\n->stable , long battery life , and great build .\n[{'aspect': 'battery life', 'opinion': 'stable', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The seats are uncomfortable if you are sitting against the wall on wooden benches .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe seats are uncomfortable if you are sitting against the wall on wooden benches .\n->", + "output": "{\"text\": \"The seats are uncomfortable if you are sitting against the wall on wooden benches .\", \"labels\": \"[{'aspect': 'seats', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n->The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n[{'aspect': 'parathas', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kebabs', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good laptop , but not great .\n->good laptop , but not great .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy suggestion is to eat family style because you 'll want to try the other dishes .\n->", + "output": "{\"text\": \"My suggestion is to eat family style because you 'll want to try the other dishes .\", \"labels\": \"[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend the Sophia pizza .\n->I highly recommend the Sophia pizza .\n[{'aspect': 'Sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: whoever the jazz duo was , they were on point .\n->whoever the jazz duo was , they were on point .\n[{'aspect': 'jazz duo', 'opinion': 'on point', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: Best of all is the warm vibe , the owner is super friendly and service is fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest of all is the warm vibe , the owner is super friendly and service is fast .\n->", + "output": "{\"text\": \"Best of all is the warm vibe , the owner is super friendly and service is fast .\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n->the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'storage', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'intel processor', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: the wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she ' s - way - cuter - than - me - that - b @ # $ * way ) .\n->the wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she ' s - way - cuter - than - me - that - b @ # $ * way ) .\n[{'aspect': 'wait staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait staff', 'opinion': 'fun', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait staff', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: Faan 's got a great concept but a little rough on the delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFaan 's got a great concept but a little rough on the delivery .\n->", + "output": "{\"text\": \"Faan 's got a great concept but a little rough on the delivery .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'rough', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: very pleased\n->very pleased\n[{'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrom the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n->", + "output": "{\"text\": \"From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a fun restaurant to go to .\n->this is a fun restaurant to go to .\n[{'aspect': 'restaurant', 'opinion': 'fun', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: All in all the food was above average and I would return to see how they operate with four or less dinners .\n->All in all the food was above average and I would return to see how they operate with four or less dinners .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great food at REASONABLE prices , makes for an evening that ca n't be beat !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food at REASONABLE prices , makes for an evening that ca n't be beat !\n->", + "output": "{\"text\": \"Great food at REASONABLE prices , makes for an evening that ca n't be beat !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'REASONABLE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it !\n->love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\n->Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\n[{'aspect': 'bagels', 'opinion': 'fine', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this little place has a cute interior decor and affordable city prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little place has a cute interior decor and affordable city prices .\n->", + "output": "{\"text\": \"this little place has a cute interior decor and affordable city prices .\", \"labels\": \"[{'aspect': 'interior decor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well macbook is no less than expected .\n->well macbook is no less than expected .\n[{'aspect': 'macbook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this makes this chromebook closer to a real computer .\n->this makes this chromebook closer to a real computer .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Two words : Free wine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTwo words : Free wine .\n->", + "output": "{\"text\": \"Two words : Free wine .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'Free', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recommend this spot to anyone who enjoys fine cuisine at reasonable prices .\n->I recommend this spot to anyone who enjoys fine cuisine at reasonable prices .\n[{'aspect': 'cuisine', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n->if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n[{'aspect': 'corona', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The price is reasonable although the service is poor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe price is reasonable although the service is poor .\n->", + "output": "{\"text\": \"The price is reasonable although the service is poor .\", \"labels\": \"[{'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n->the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n[{'aspect': 'pear torte', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'unable to provide', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Odd for Ave B , not just odd , The place attracts an eclectic crowd to say the least .\n->Odd for Ave B , not just odd , The place attracts an eclectic crowd to say the least .\n[{'aspect': 'place', 'opinion': 'odd', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The quantity is also very good , you will come out satisfied .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe quantity is also very good , you will come out satisfied .\n->", + "output": "{\"text\": \"The quantity is also very good , you will come out satisfied .\", \"labels\": \"[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The location is perfect .\n->The location is perfect .\n[{'aspect': 'location', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n->not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The fried rice is amazing here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fried rice is amazing here .\n->", + "output": "{\"text\": \"The fried rice is amazing here .\", \"labels\": \"[{'aspect': 'fried rice', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Decor is charming .\n->Decor is charming .\n[{'aspect': 'Decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: LOVE the atmosphere - felt like I was in Paris .\n->LOVE the atmosphere - felt like I was in Paris .\n[{'aspect': 'atmosphere', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThree courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n->", + "output": "{\"text\": \"Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\", \"labels\": \"[{'aspect': 'mussels', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'puff pastry goat cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salad with a delicious dressing', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hanger steak au poivre', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is great .\n->the build quality is great .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: other than that , i like it , and this is my first chromebook .\n->other than that , i like it , and this is my first chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The place is so cool and the service is prompt and curtious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is so cool and the service is prompt and curtious .\n->", + "output": "{\"text\": \"The place is so cool and the service is prompt and curtious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when you ' re sitting in their main dining room ( which has a spectacular , hand - painted high ceiling ) you ' d never know there was a world outside .\n->when you ' re sitting in their main dining room ( which has a spectacular , hand - painted high ceiling ) you ' d never know there was a world outside .\n[{'aspect': 'main dining room', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ceiling', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ceiling', 'opinion': 'hand - painted high', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the food arrived 20 minutes after i called , cold and soggy .\n->the food arrived 20 minutes after i called , cold and soggy .\n[{'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAt the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n->", + "output": "{\"text\": \"At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\", \"labels\": \"[{'aspect': 'broth with noodles', 'opinion': 'mild', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n->It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n[{'aspect': 'table', 'opinion': 'impossible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this chromebook not only meets those requirements it has exceeded my expectations .\n->this chromebook not only meets those requirements it has exceeded my expectations .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I just wonder how you can have such a delicious meal for such little money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI just wonder how you can have such a delicious meal for such little money .\n->", + "output": "{\"text\": \"I just wonder how you can have such a delicious meal for such little money .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'money', 'opinion': 'little', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: easy to use .\n->easy to use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: located at the end of a magnificent block .\n->located at the end of a magnificent block .\n[{'aspect': 'NULL', 'opinion': 'magnificent', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: The wine list is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is excellent .\n->", + "output": "{\"text\": \"The wine list is excellent .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n->the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n[{'aspect': 'crust', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'light', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n->everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n[{'aspect': 'zucchero pomodori', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIve been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n->", + "output": "{\"text\": \"Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great taste\n->great taste\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: love these chromebooks !\n->love these chromebooks !\n[{'aspect': 'chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: This is a consistently great place to dine for lunch or dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a consistently great place to dine for lunch or dinner .\n->", + "output": "{\"text\": \"This is a consistently great place to dine for lunch or dinner .\", \"labels\": \"[{'aspect': 'dine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: enjoyed a very nice caesar salad while my wife had arugula and goat cheese . . . . both very tasty .\n->enjoyed a very nice caesar salad while my wife had arugula and goat cheese . . . . both very tasty .\n[{'aspect': 'caesar salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'caesar salad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'arugula and goat cheese', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i would never recommend this place to anybody even for a casual dinner .\n->i would never recommend this place to anybody even for a casual dinner .\n[{'aspect': 'place', 'opinion': 'never recommend', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'never recommend', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Nice atmosphere , the service was very pleasant and the desert was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNice atmosphere , the service was very pleasant and the desert was good .\n->", + "output": "{\"text\": \"Nice atmosphere , the service was very pleasant and the desert was good .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'Nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n->my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n[{'aspect': 'bagel with lox spread', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagles', 'opinion': 'unbeliavably good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: * the screen is more than adequate for me , although i have not used it outside much yet .\n->* the screen is more than adequate for me , although i have not used it outside much yet .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: After really enjoying ourselves at the bar we sat down at a table and had dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAfter really enjoying ourselves at the bar we sat down at a table and had dinner .\n->", + "output": "{\"text\": \"After really enjoying ourselves at the bar we sat down at a table and had dinner .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'table', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It was so bad I actually refused to pay for my food .\n->It was so bad I actually refused to pay for my food .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the bread we received was horrible - rock hard and cold - and the ` ` free ' ' appetizer of olives was disappointing .\n->the bread we received was horrible - rock hard and cold - and the ` ` free ' ' appetizer of olives was disappointing .\n[{'aspect': 'bread', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'bread', 'opinion': 'rock hard', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'bread', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'appetizer of olives', 'opinion': 'disappointing', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: I liked the beer selection !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI liked the beer selection !\n->", + "output": "{\"text\": \"I liked the beer selection !\", \"labels\": \"[{'aspect': 'beer selection', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was very well prepared .\n->The food was very well prepared .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: keyboard is the best laptop keyboard i have ever used .\n->keyboard is the best laptop keyboard i have ever used .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'laptop keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: Great food , good size menu , great service and an unpretentious setting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food , good size menu , great service and an unpretentious setting .\n->", + "output": "{\"text\": \"Great food , good size menu , great service and an unpretentious setting .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'unpretentious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - for such a premium price , this is definitely the worst purchase quality wise that i have made .\n->- for such a premium price , this is definitely the worst purchase quality wise that i have made .\n[{'aspect': 'NULL', 'opinion': 'premium', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: they have refused to fix it to date .\n->they have refused to fix it to date .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: Wine list selection is good and wine-by-the-glass was generously filled to the top .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWine list selection is good and wine-by-the-glass was generously filled to the top .\n->", + "output": "{\"text\": \"Wine list selection is good and wine-by-the-glass was generously filled to the top .\", \"labels\": \"[{'aspect': 'Wine list selection', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine-by-the-glass', 'opinion': 'generously filled', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: loved it !\n->loved it !\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu is very limited - i think we counted 4 or 5 entrees .\n->", + "output": "{\"text\": \"The menu is very limited - i think we counted 4 or 5 entrees .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not sure where the previous reviewer , lonk , dined , but saul is in a great neighborhood and has great food !\n->not sure where the previous reviewer , lonk , dined , but saul is in a great neighborhood and has great food !\n[{'aspect': 'neighborhood', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i have had no issues with it since i bought it i would highly recommend it .\n->i have had no issues with it since i bought it i would highly recommend it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The menu is limited but almost all of the dishes are excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu is limited but almost all of the dishes are excellent .\n->", + "output": "{\"text\": \"The menu is limited but almost all of the dishes are excellent .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: faan is sooo good .\n->faan is sooo good .\n[{'aspect': 'faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: my chow fun and chow see was really bland and oily .\n->my chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Great bagels , spreads and a good place to hang out in .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat bagels , spreads and a good place to hang out in .\n->", + "output": "{\"text\": \"Great bagels , spreads and a good place to hang out in .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spreads', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: trackbad : it could be better but it ' s not bad 8 .\n->trackbad : it could be better but it ' s not bad 8 .\n[{'aspect': 'trackbad', 'opinion': 'better', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackbad', 'opinion': 'not bad', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: only been using it for about a week , continuously getting the error that it can not connect to speakers .\n->only been using it for about a week , continuously getting the error that it can not connect to speakers .\n[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nUnfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n->", + "output": "{\"text\": \"Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: usb - c connectors on both sides can both charge , making the power cord location an option .\n->usb - c connectors on both sides can both charge , making the power cord location an option .\n[{'aspect': 'usb - c connectors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\nExample:\ntext: i recommend this place to everyone .\n->i recommend this place to everyone .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: We always have a delicious meal and always leave feeling satisfied .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe always have a delicious meal and always leave feeling satisfied .\n->", + "output": "{\"text\": \"We always have a delicious meal and always leave feeling satisfied .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent lapto , just as they show it .\n->excellent lapto , just as they show it .\n[{'aspect': 'lapto', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->The place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .\n[{'aspect': 'place', 'opinion': 'BISTRO', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'served efficiently', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'NULL'}]\ntext: First went here to enjoy their garden terrace .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFirst went here to enjoy their garden terrace .\n->", + "output": "{\"text\": \"First went here to enjoy their garden terrace .\", \"labels\": \"[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: note that this laptop is only 45 daysish old .\n->note that this laptop is only 45 daysish old .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Dessert is a joke ... dont bother\n->Dessert is a joke ... dont bother\n[{'aspect': 'Dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The pizza was pretty good and huge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza was pretty good and huge .\n->", + "output": "{\"text\": \"The pizza was pretty good and huge .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our agreed favorite is the orrechiete with sausage and chicken ( usually the waiters are kind enough to split the dish in half so you get to sample both meats ) .\n->Our agreed favorite is the orrechiete with sausage and chicken ( usually the waiters are kind enough to split the dish in half so you get to sample both meats ) .\n[{'aspect': 'orrechiete with sausage and chicken', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: also on this model the ssd is not replaceable .\n->also on this model the ssd is not replaceable .\n[{'aspect': 'ssd', 'opinion': 'not replaceable', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n->", + "output": "{\"text\": \"The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\", \"labels\": \"[{'aspect': 'cuisine', 'opinion': 'different', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n->Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n[{'aspect': 'prices', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: * * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n->* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: I almost hesititate to write a review because the atmosphere was so great and I would hate for it too become to crowded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI almost hesititate to write a review because the atmosphere was so great and I would hate for it too become to crowded .\n->", + "output": "{\"text\": \"I almost hesititate to write a review because the atmosphere was so great and I would hate for it too become to crowded .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the crunchy tuna , it is to die for .\n->Try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Grilled whole fish wonderful , great spicing .\n->Grilled whole fish wonderful , great spicing .\n[{'aspect': 'fish', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: They are often crowded on the weekends but they are efficient and accurate with their service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey are often crowded on the weekends but they are efficient and accurate with their service .\n->", + "output": "{\"text\": \"They are often crowded on the weekends but they are efficient and accurate with their service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crowded', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after 3 weeks using this flip , i am quite happy with its performance , design .\n->after 3 weeks using this flip , i am quite happy with its performance , design .\n[{'aspect': 'flip', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'flip', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it took hours to restore to factory default settings , and it crashed once again days later .\n->it took hours to restore to factory default settings , and it crashed once again days later .\n[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The atmosphere is unheralded , the service impeccable , and the food magnificant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is unheralded , the service impeccable , and the food magnificant .\n->", + "output": "{\"text\": \"The atmosphere is unheralded , the service impeccable , and the food magnificant .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my girlfriend works from home with it and has no problems at all to do online classes with it .\n->my girlfriend works from home with it and has no problems at all to do online classes with it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this by far one of the best laptops i ' ve ever purchased .\n->this by far one of the best laptops i ' ve ever purchased .\n[{'aspect': 'laptops', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->", + "output": "{\"text\": \"We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\", \"labels\": \"[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i fed up with the price it cost to upgrade the product as well as the software .\n->but i fed up with the price it cost to upgrade the product as well as the software .\n[{'aspect': 'NULL', 'opinion': 'fed up', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: had an awful experience at casa la femme on a saturday dinner .\n->had an awful experience at casa la femme on a saturday dinner .\n[{'aspect': 'casa la femme', 'opinion': 'awful', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: Right off the L in Brooklyn this is a nice cozy place with good pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRight off the L in Brooklyn this is a nice cozy place with good pizza .\n->", + "output": "{\"text\": \"Right off the L in Brooklyn this is a nice cozy place with good pizza .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'nice cozy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend it .\n->i highly recommend it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n->first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n[{'aspect': 'keyboard', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: Le Pere Pinard has a $ 15 pre-theater menu that is outstanding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLe Pere Pinard has a $ 15 pre-theater menu that is outstanding .\n->", + "output": "{\"text\": \"Le Pere Pinard has a $ 15 pre-theater menu that is outstanding .\", \"labels\": \"[{'aspect': 'pre-theater menu', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very slow , and always hangs\n->very slow , and always hangs\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n->i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n[{'aspect': 'word on line', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: The strong scents coming from the left and right of me negatively affected my taste buds .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe strong scents coming from the left and right of me negatively affected my taste buds .\n->", + "output": "{\"text\": \"The strong scents coming from the left and right of me negatively affected my taste buds .\", \"labels\": \"[{'aspect': 'scents', 'opinion': 'strong', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I ca n't wait for summer , when they serve outside on their gigantic patio .\n->I ca n't wait for summer , when they serve outside on their gigantic patio .\n[{'aspect': 'patio', 'opinion': 'gigantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n->Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n[{'aspect': 'lunch', 'opinion': 'busier', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'seldom crowded', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: We had the lobster sandwich and it was FANTASTIC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had the lobster sandwich and it was FANTASTIC .\n->", + "output": "{\"text\": \"We had the lobster sandwich and it was FANTASTIC .\", \"labels\": \"[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a fun restaurant to go to .\n->this is a fun restaurant to go to .\n[{'aspect': 'restaurant', 'opinion': 'fun', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n->His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n[{'aspect': 'hostess', 'opinion': 'delightfully warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'gracious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'comforting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Though the Spider Roll may look like a challenge to eat , with soft shell crab hanging out of the roll , it is well worth the price you pay for them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThough the Spider Roll may look like a challenge to eat , with soft shell crab hanging out of the roll , it is well worth the price you pay for them .\n->", + "output": "{\"text\": \"Though the Spider Roll may look like a challenge to eat , with soft shell crab hanging out of the roll , it is well worth the price you pay for them .\", \"labels\": \"[{'aspect': 'price', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shell crab', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great sake !\n->great sake !\n[{'aspect': 'sake', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: check this place out !\n->check this place out !\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Deep Fried Skewers are good and still rare to find in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDeep Fried Skewers are good and still rare to find in NYC .\n->", + "output": "{\"text\": \"Deep Fried Skewers are good and still rare to find in NYC .\", \"labels\": \"[{'aspect': 'Deep Fried Skewers', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Deep Fried Skewers', 'opinion': 'rare', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the crust was imazingly cooked well and pizza was fully loaded : ) : ) : )\n->the crust was imazingly cooked well and pizza was fully loaded : ) : ) : )\n[{'aspect': 'crust', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'fully loaded', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n->i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n[{'aspect': '2012 chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\n->", + "output": "{\"text\": \"I also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\", \"labels\": \"[{'aspect': 'rice dishes', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'congee ( rice porridge )', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n->The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n[{'aspect': 'spot lights', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: and the tom kha soup was pathetic .\n->and the tom kha soup was pathetic .\n[{'aspect': 'tom kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Their tuna tartar appetizer is to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir tuna tartar appetizer is to die for .\n->", + "output": "{\"text\": \"Their tuna tartar appetizer is to die for .\", \"labels\": \"[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: highly recommend this laptop for mobile workers .\n->highly recommend this laptop for mobile workers .\n[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: 8 + lbs , this one is right under 5 so it makes it nice and portable .\n->8 + lbs , this one is right under 5 so it makes it nice and portable .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAn oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n->", + "output": "{\"text\": \"An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'passion', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish dishes', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soups', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kitchen', 'opinion': 'precise execution', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best in all of nyc\n->best in all of nyc\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the appetizers are also delicious !\n->the appetizers are also delicious !\n[{'aspect': 'appetizers', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: If you love wine and cheese and delicious french fare , you 'll love Artisanal !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you love wine and cheese and delicious french fare , you 'll love Artisanal !\n->", + "output": "{\"text\": \"If you love wine and cheese and delicious french fare , you 'll love Artisanal !\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french fare', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n->The Steak Tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - boot time , sleep time and wake time are crazy fast .\n->- boot time , sleep time and wake time are crazy fast .\n[{'aspect': 'boot time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'boot time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\ntext: I love Indian food and consider myself to be quite an expert on it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI love Indian food and consider myself to be quite an expert on it .\n->", + "output": "{\"text\": \"I love Indian food and consider myself to be quite an expert on it .\", \"labels\": \"[{'aspect': 'Indian food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with ssd lightning fast start up\n->with ssd lightning fast start up\n[{'aspect': 'ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: update : i have had this computer for about 3 months now , and it is full of problems .\n->update : i have had this computer for about 3 months now , and it is full of problems .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The lava cake dessert was incredible and I recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe lava cake dessert was incredible and I recommend it .\n->", + "output": "{\"text\": \"The lava cake dessert was incredible and I recommend it .\", \"labels\": \"[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service has always been friendly and efficient .\n->Service has always been friendly and efficient .\n[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: screen quality is perfect and matte so no annoying glare !\n->screen quality is perfect and matte so no annoying glare !\n[{'aspect': 'screen quality', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: From the terrible service , to the bland food , not to mention the unaccommodating managers , the overall experience was horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrom the terrible service , to the bland food , not to mention the unaccommodating managers , the overall experience was horrible .\n->", + "output": "{\"text\": \"From the terrible service , to the bland food , not to mention the unaccommodating managers , the overall experience was horrible .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'managers', 'opinion': 'unaccommodating', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hard drive is definitely slow .\n->the hard drive is definitely slow .\n[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n->small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Lahore is a great place to duck into late-night when you need some really tasty food on the cheap -- you 'll likely have trouble finishing the amount of food you get for FOUR DOLLARS .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLahore is a great place to duck into late-night when you need some really tasty food on the cheap -- you 'll likely have trouble finishing the amount of food you get for FOUR DOLLARS .\n->", + "output": "{\"text\": \"Lahore is a great place to duck into late-night when you need some really tasty food on the cheap -- you 'll likely have trouble finishing the amount of food you get for FOUR DOLLARS .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n->The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n[{'aspect': 'Bagels', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'chewy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'gummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: good selection of wines ranging from affordable to high end .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood selection of wines ranging from affordable to high end .\n->", + "output": "{\"text\": \"good selection of wines ranging from affordable to high end .\", \"labels\": \"[{'aspect': 'selection of wines', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , i ' m very happy i chose this model .\n->overall , i ' m very happy i chose this model .\n[{'aspect': 'model', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: battery life is between 4 to 7 hours depending on what i ' m doing .\n->battery life is between 4 to 7 hours depending on what i ' m doing .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Nice restaurant overall , with classic upscale Italian decor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNice restaurant overall , with classic upscale Italian decor .\n->", + "output": "{\"text\": \"Nice restaurant overall , with classic upscale Italian decor .\", \"labels\": \"[{'aspect': 'Italian decor', 'opinion': 'classic upscale', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a little crowded but they move that line really fast !\n->a little crowded but they move that line really fast !\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n->If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Not impressed with the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot impressed with the food .\n->", + "output": "{\"text\": \"Not impressed with the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - touch pad seems a little off to me .\n->- touch pad seems a little off to me .\n[{'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s extremely useful as a laptop as well as , most of the time at least , a tablet .\n->it ' s extremely useful as a laptop as well as , most of the time at least , a tablet .\n[{'aspect': 'laptop', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'tablet', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: The entire dining experience was wonderful !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe entire dining experience was wonderful !\n->", + "output": "{\"text\": \"The entire dining experience was wonderful !\", \"labels\": \"[{'aspect': 'dining experience', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: consequently , their burgers fell apart in their hands and made such a mess that they did ' nt feel like finishing them .\n->consequently , their burgers fell apart in their hands and made such a mess that they did ' nt feel like finishing them .\n[{'aspect': 'burgers', 'opinion': 'fell apart', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: I REALLY ENJOYED THE SHOWS PUT ON BY THE ACTORS .\n->I REALLY ENJOYED THE SHOWS PUT ON BY THE ACTORS .\n[{'aspect': 'SHOWS', 'opinion': 'ENJOYED', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ACTORS', 'opinion': 'ENJOYED', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The wine selection ( by the glass and bottle ) is wonderful and I always recommend that friends make a reservation if they 're going to be in town .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine selection ( by the glass and bottle ) is wonderful and I always recommend that friends make a reservation if they 're going to be in town .\n->", + "output": "{\"text\": \"The wine selection ( by the glass and bottle ) is wonderful and I always recommend that friends make a reservation if they 're going to be in town .\", \"labels\": \"[{'aspect': 'wine selection', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'reservation', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish i could be refunded !\n->i wish i could be refunded !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: that seemed to correct problem , but problem returned next day and the battery would only charge up to 1 % with charger plugged in .\n->that seemed to correct problem , but problem returned next day and the battery would only charge up to 1 % with charger plugged in .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Order the panang duck , it 's fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOrder the panang duck , it 's fantastic .\n->", + "output": "{\"text\": \"Order the panang duck , it 's fantastic .\", \"labels\": \"[{'aspect': 'panang duck', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a good product based on my experience - i have used this for almost a whole month .\n->this is a good product based on my experience - i have used this for almost a whole month .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the m3 is great , never slow or laggy .\n->the m3 is great , never slow or laggy .\n[{'aspect': 'm3', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'm3', 'opinion': 'never slow or laggy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n->", + "output": "{\"text\": \"Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\", \"labels\": \"[{'aspect': 'raw vegatables', 'opinion': 'wondered', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n->it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: The pizza was pretty good and huge .\n->The pizza was pretty good and huge .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Other than the crappy service from two individuals , it 's great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOther than the crappy service from two individuals , it 's great .\n->", + "output": "{\"text\": \"Other than the crappy service from two individuals , it 's great .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n->You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Vanilla Shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is awesome - definitely try the striped bass .\n->The food is awesome - definitely try the striped bass .\n[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'striped bass', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: They have authentic Indian at amazin prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey have authentic Indian at amazin prices .\n->", + "output": "{\"text\": \"They have authentic Indian at amazin prices .\", \"labels\": \"[{'aspect': 'Indian', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'amazin', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: beef noodle soup is good as well .\n->beef noodle soup is good as well .\n[{'aspect': 'beef noodle soup', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the computer is nice , blah blah it has nice features but it stops working after a few months .\n->the computer is nice , blah blah it has nice features but it stops working after a few months .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Ambiance -- relaxed and stylish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmbiance -- relaxed and stylish .\n->", + "output": "{\"text\": \"Ambiance -- relaxed and stylish .\", \"labels\": \"[{'aspect': 'Ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n->the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n[{'aspect': 'crust', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'light', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: their bagels are fine , but they are a little overcooked , and not really a ' special ' bagel experience .\n->their bagels are fine , but they are a little overcooked , and not really a ' special ' bagel experience .\n[{'aspect': 'bagels', 'opinion': 'fine', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\n->", + "output": "{\"text\": \"Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is more than sufficient for non - gaming , business use , and it seems as fast as expected .\n->the screen is more than sufficient for non - gaming , business use , and it seems as fast as expected .\n[{'aspect': 'screen', 'opinion': 'sufficient', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand yes Dal Bukhara is so dam good and so are all the kababs .\n->", + "output": "{\"text\": \"and yes Dal Bukhara is so dam good and so are all the kababs .\", \"labels\": \"[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great place , great value .\n->great place , great value .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n->suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n[{'aspect': 'keyboard', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: I look forward to eating here again\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI look forward to eating here again\n->", + "output": "{\"text\": \"I look forward to eating here again\", \"labels\": \"[{'aspect': 'eating', 'opinion': 'look forward', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: patsy ' s pizza = true love\n->patsy ' s pizza = true love\n[{'aspect': \"patsy ' s pizza\", 'opinion': 'true love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The hostess is rude to the point of being offensive .\n->The hostess is rude to the point of being offensive .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Tuk Tuk is one of those comfortable neighborhood joints where you know you will always have a good meal at a fair price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTuk Tuk is one of those comfortable neighborhood joints where you know you will always have a good meal at a fair price .\n->", + "output": "{\"text\": \"Tuk Tuk is one of those comfortable neighborhood joints where you know you will always have a good meal at a fair price .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'fair', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s delicious !\n->it ' s delicious !\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n->the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n[{'aspect': 'mouse', 'opinion': 'good', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\ntext: A glass of Leaping Lizard , a glass of prosecco , and the mussels had everything happy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA glass of Leaping Lizard , a glass of prosecco , and the mussels had everything happy .\n->", + "output": "{\"text\": \"A glass of Leaping Lizard , a glass of prosecco , and the mussels had everything happy .\", \"labels\": \"[{'aspect': 'glass of prosecco', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'glass of Leaping Lizard', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so delicious ! ! ! ! ! !\n->so delicious ! ! ! ! ! !\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: having had it for just over a month , i have to say i am thoroughly impressed by its versatility and how stable the os is .\n->having had it for just over a month , i have to say i am thoroughly impressed by its versatility and how stable the os is .\n[{'aspect': 'os', 'opinion': 'versatility', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'stable', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n->", + "output": "{\"text\": \"Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'creme brulee', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sugar', 'opinion': 'charred', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there was no tap beer that evening , which was a disappointment .\n->there was no tap beer that evening , which was a disappointment .\n[{'aspect': 'beer', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n->if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n[{'aspect': 'bottle', 'opinion': 'love', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}, {'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: The food always tastes fresh and served promptly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food always tastes fresh and served promptly .\n->", + "output": "{\"text\": \"The food always tastes fresh and served promptly .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wonderful strawberry daiquiries as well !\n->wonderful strawberry daiquiries as well !\n[{'aspect': 'strawberry daiquiries', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n->the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n[{'aspect': 'number pad', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: The pizza here is delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza here is delicious .\n->", + "output": "{\"text\": \"The pizza here is delicious .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n->My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n[{'aspect': 'food', 'opinion': 'opposite', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The takeout menu says to keep an eye out for an expanded menu offering more italian dishes , I ca n't wait !\n->The takeout menu says to keep an eye out for an expanded menu offering more italian dishes , I ca n't wait !\n[{'aspect': 'menu', 'opinion': 'expanded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'italian dishes', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->", + "output": "{\"text\": \"This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen resolution super ( retina ) .\n->screen resolution super ( retina ) .\n[{'aspect': 'screen resolution', 'opinion': 'super', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: unfortunately , on day 2 since i received the chromebook pro , when i flipped the lid to turn it into a tablet / tent mode , screen started flickering all over the place .\n->unfortunately , on day 2 since i received the chromebook pro , when i flipped the lid to turn it into a tablet / tent mode , screen started flickering all over the place .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: Hats off to the chef .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHats off to the chef .\n->", + "output": "{\"text\": \"Hats off to the chef .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'Hats off', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i looove their eggplant pizza , as well as their pastas !\n->i looove their eggplant pizza , as well as their pastas !\n[{'aspect': 'eggplant pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i always was a huge fan of apple and i always thought the price on the macbook pros were too steep but i finally took the plunge and i ' m satisfied .\n->i always was a huge fan of apple and i always thought the price on the macbook pros were too steep but i finally took the plunge and i ' m satisfied .\n[{'aspect': 'macbook pros', 'opinion': 'satisfied', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: The pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria 's .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria 's .\n->", + "output": "{\"text\": \"The pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria 's .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozzarella', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'frozen', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'shredded', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is fast and friendly .\n->service is fast and friendly .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n->the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n[{'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: Its an excellent place to relax and the food is one of the best in the city of New York .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIts an excellent place to relax and the food is one of the best in the city of New York .\n->", + "output": "{\"text\": \"Its an excellent place to relax and the food is one of the best in the city of New York .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n->Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n[{'aspect': 'space', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this chromebook features a full size keyboard and is easier type on by a long shot .\n->this chromebook features a full size keyboard and is easier type on by a long shot .\n[{'aspect': 'keyboard', 'opinion': 'full size', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'easier', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: service is friendly , and never had a problem walking in and getting a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is friendly , and never had a problem walking in and getting a table .\n->", + "output": "{\"text\": \"service is friendly , and never had a problem walking in and getting a table .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'never had a problem', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very cozy and warm inside . . . . .\n->very cozy and warm inside . . . . .\n[{'aspect': 'NULL', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the battery life is great ( i get about 9 hours ) .\n->the battery life is great ( i get about 9 hours ) .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: The atmosphere was crowded but it was a great bistro-type vibe .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere was crowded but it was a great bistro-type vibe .\n->", + "output": "{\"text\": \"The atmosphere was crowded but it was a great bistro-type vibe .\", \"labels\": \"[{'aspect': 'bistro-type vibe', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cute place , nice wait staff but would never go there again .\n->Cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Sushi was n't anything spectacular for the price .\n->Sushi was n't anything spectacular for the price .\n[{'aspect': 'Sushi', 'opinion': 'spectacular', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: First off , the waitress was completely unattentive the 2 times we saw her ( odd in a restaurant with 6 tables ) and got our order wrong .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFirst off , the waitress was completely unattentive the 2 times we saw her ( odd in a restaurant with 6 tables ) and got our order wrong .\n->", + "output": "{\"text\": \"First off , the waitress was completely unattentive the 2 times we saw her ( odd in a restaurant with 6 tables ) and got our order wrong .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'unattentive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was bland oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was bland oily .\n->", + "output": "{\"text\": \"The food was bland oily .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'trendi', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: price is good\n->price is good\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: What is even better , is that the prices are very affordable as well , and the food is really good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhat is even better , is that the prices are very affordable as well , and the food is really good .\n->", + "output": "{\"text\": \"What is even better , is that the prices are very affordable as well , and the food is really good .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: took my mom for mother ' s day , and the maitre d ' was pretty rude .\n->took my mom for mother ' s day , and the maitre d ' was pretty rude .\n[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this little guy fits the bill perfectly .\n->this little guy fits the bill perfectly .\n[{'aspect': 'guy', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The fish is fresh but the variety of fish is nothing out of ordinary .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fish is fresh but the variety of fish is nothing out of ordinary .\n->", + "output": "{\"text\": \"The fish is fresh but the variety of fish is nothing out of ordinary .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'variety of fish', 'opinion': 'ordinary', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fast service .\n->Fast service .\n[{'aspect': 'service', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n->the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n[{'aspect': 'plastic', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keys', 'opinion': \"' t stick\", 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mousepad', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n->", + "output": "{\"text\": \"Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\", \"labels\": \"[{'aspect': 'pesto pizza', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house salad', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bottle of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ' ve tried before but it always packed and does n ' t take reservations .\n->we ' ve tried before but it always packed and does n ' t take reservations .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: it ' s great to go for a quick lunch either alone or with a friend .\n->it ' s great to go for a quick lunch either alone or with a friend .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Ambiance and music funky , which I enjoy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmbiance and music funky , which I enjoy .\n->", + "output": "{\"text\": \"Ambiance and music funky , which I enjoy .\", \"labels\": \"[{'aspect': 'Ambiance', 'opinion': 'funky', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'funky', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: go there once and oh yes . . . you will go back . . . you will . . .\n->go there once and oh yes . . . you will go back . . . you will . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i will be returning this item .\n->i will be returning this item .\n[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The food , drinks and service are clearly among the best in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food , drinks and service are clearly among the best in the city .\n->", + "output": "{\"text\": \"The food , drinks and service are clearly among the best in the city .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + the colour is a beautiful grey and not purple as reviewed by other users\n->+ the colour is a beautiful grey and not purple as reviewed by other users\n[{'aspect': 'colour', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: \u2022 occasionally hangs for 10 - 30 seconds with no response from keyboard or trackpad\n->\u2022 occasionally hangs for 10 - 30 seconds with no response from keyboard or trackpad\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGranted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n->", + "output": "{\"text\": \"Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\", \"labels\": \"[{'aspect': 'space', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: customer service told me it ' s faulty\n->customer service told me it ' s faulty\n[{'aspect': 'customer service', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: Decor leaves something to be desired .\n->Decor leaves something to be desired .\n[{'aspect': 'Decor', 'opinion': 'desired', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I love the atmorphere @ peep !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI love the atmorphere @ peep !\n->", + "output": "{\"text\": \"I love the atmorphere @ peep !\", \"labels\": \"[{'aspect': 'atmorphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: terrible would be a compliment !\n->terrible would be a compliment !\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: you can get a completely delish martini in a glass ( that ' s about 2 1 / 2 drinks ) for $ 8 . 50 ( i recommend the vanilla shanty , mmmm ! ) in a great homey setting with great music .\n->you can get a completely delish martini in a glass ( that ' s about 2 1 / 2 drinks ) for $ 8 . 50 ( i recommend the vanilla shanty , mmmm ! ) in a great homey setting with great music .\n[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}, {'aspect': 'vanilla shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The service was attentive and her suggestions of menu items was right on the mark .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was attentive and her suggestions of menu items was right on the mark .\n->", + "output": "{\"text\": \"The service was attentive and her suggestions of menu items was right on the mark .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu items', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great service with amazon on fulfilling my order .\n->great service with amazon on fulfilling my order .\n[{'aspect': 'service with amazon', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: i can live with the so - so touchpad since the rest of it feels solid / sturdy .\n->i can live with the so - so touchpad since the rest of it feels solid / sturdy .\n[{'aspect': 'touchpad', 'opinion': 'so - so', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'touchpad', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'touchpad', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n->", + "output": "{\"text\": \"The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\", \"labels\": \"[{'aspect': 'three course meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had the best ravioli ever .\n->i had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n->however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n[{'aspect': 'kimchee', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'slimy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'korean fair', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n->", + "output": "{\"text\": \"Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'Outstanding', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I do n't like Indian food too much and this was delicious , however you want to factor that into the equation .\n->I do n't like Indian food too much and this was delicious , however you want to factor that into the equation .\n[{'aspect': 'Indian food', 'opinion': \"do n't like\", 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is a dreadful little piece of machinery .\n->this is a dreadful little piece of machinery .\n[{'aspect': 'machinery', 'opinion': 'dreadful', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The environment is romantic , but the food is horrible , the service is pathetic , and gabriella lies about everything she could .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe environment is romantic , but the food is horrible , the service is pathetic , and gabriella lies about everything she could .\n->", + "output": "{\"text\": \"The environment is romantic , but the food is horrible , the service is pathetic , and gabriella lies about everything she could .\", \"labels\": \"[{'aspect': 'environment', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is exactly what i needed .\n->this is exactly what i needed .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: an unexpected benefit for me has been the iphone / mbp integration .\n->an unexpected benefit for me has been the iphone / mbp integration .\n[{'aspect': 'iphone / mbp integration', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: We had crawfish boiled and despite making a mess , it was a ton of fun and quite tasty as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had crawfish boiled and despite making a mess , it was a ton of fun and quite tasty as well .\n->", + "output": "{\"text\": \"We had crawfish boiled and despite making a mess , it was a ton of fun and quite tasty as well .\", \"labels\": \"[{'aspect': 'crawfish boiled', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crawfish boiled', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n->overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n[{'aspect': 'asus c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food is consistently wonderful - I 've been coming here for years , and the owner has always been accomodating and friendly .\n->The food is consistently wonderful - I 've been coming here for years , and the owner has always been accomodating and friendly .\n[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: What came to our table was burned beyond recognition and stringy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhat came to our table was burned beyond recognition and stringy .\n->", + "output": "{\"text\": \"What came to our table was burned beyond recognition and stringy .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'burned', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very junky\n->very junky\n[{'aspect': 'junky', 'opinion': 'junky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Atmosphere is nice and relaxed too ...\n->Atmosphere is nice and relaxed too ...\n[{'aspect': 'Atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I had the Pad Thai and the noodles were sticky .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had the Pad Thai and the noodles were sticky .\n->", + "output": "{\"text\": \"I had the Pad Thai and the noodles were sticky .\", \"labels\": \"[{'aspect': 'Pad Thai', 'opinion': 'sticky', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'noodles', 'opinion': 'sticky', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while finishing our meals which included a high - end bottle of wine , our son ' s fiance joined us for a glass of wine and dessert .\n->while finishing our meals which included a high - end bottle of wine , our son ' s fiance joined us for a glass of wine and dessert .\n[{'aspect': 'bottle of wine', 'opinion': 'high - end', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: also , chromeos does not allow color / temperature calibration of the display device .\n->also , chromeos does not allow color / temperature calibration of the display device .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\ntext: The hostess is rude to the point of being offensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe hostess is rude to the point of being offensive .\n->", + "output": "{\"text\": \"The hostess is rude to the point of being offensive .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the waitress was very patient with us and the food is phenomenal !\n->the waitress was very patient with us and the food is phenomenal !\n[{'aspect': 'waitress', 'opinion': 'patient', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n->food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork belly', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: If you 're craving for Haru 's great food , especially the House Roll , but ca n't stand the wait building outisde , head across the street to their Sake Bar !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you 're craving for Haru 's great food , especially the House Roll , but ca n't stand the wait building outisde , head across the street to their Sake Bar !\n->", + "output": "{\"text\": \"If you 're craving for Haru 's great food , especially the House Roll , but ca n't stand the wait building outisde , head across the street to their Sake Bar !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they have a delicious banana chocolate dessert , as well as a great green tea tempura .\n->they have a delicious banana chocolate dessert , as well as a great green tea tempura .\n[{'aspect': 'banana chocolate dessert', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'green tea tempura', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: But the staff was so horrible to us .\n->But the staff was so horrible to us .\n[{'aspect': 'staff', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: A narrow corridor leads to a tiny space where there are three tiny white tiled counters , a great deal of mess ( stacks of bottles , cans ) and a small counter holding 12-14 entrees .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA narrow corridor leads to a tiny space where there are three tiny white tiled counters , a great deal of mess ( stacks of bottles , cans ) and a small counter holding 12-14 entrees .\n->", + "output": "{\"text\": \"A narrow corridor leads to a tiny space where there are three tiny white tiled counters , a great deal of mess ( stacks of bottles , cans ) and a small counter holding 12-14 entrees .\", \"labels\": \"[{'aspect': 'corridor', 'opinion': 'narrow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'space', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'counters', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apple told me to upgrade it to just buy the new one when they release it .\n->apple told me to upgrade it to just buy the new one when they release it .\n[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: good food\n->good food\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is great .\n->", + "output": "{\"text\": \"The food is great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fingerprint reader is working well\n->- fingerprint reader is working well\n[{'aspect': 'fingerprint reader', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: - charges quickly\n->- charges quickly\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: All conveniently delivered right to the door .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll conveniently delivered right to the door .\n->", + "output": "{\"text\": \"All conveniently delivered right to the door .\", \"labels\": \"[{'aspect': 'delivered', 'opinion': 'conveniently', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .\n->very immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .\n[{'aspect': 'bartender', 'opinion': 'immature', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'slowwwww', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'not fresh or warm', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'waitresses', 'opinion': 'werent very attentive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - 4gb ram cap .\n->- 4gb ram cap .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: I also ordered the Change Mojito , which was out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI also ordered the Change Mojito , which was out of this world .\n->", + "output": "{\"text\": \"I also ordered the Change Mojito , which was out of this world .\", \"labels\": \"[{'aspect': 'Change Mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n->the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n->The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n[{'aspect': 'three course meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Okay service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOkay service .\n->", + "output": "{\"text\": \"Okay service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Okay', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n->i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: delivery guy sometimes get upset if you do n ' t tip more than 10 % .\n->delivery guy sometimes get upset if you do n ' t tip more than 10 % .\n[{'aspect': 'delivery guy', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The only thing more wonderful than the food ( which is exceptional ) is the service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only thing more wonderful than the food ( which is exceptional ) is the service .\n->", + "output": "{\"text\": \"The only thing more wonderful than the food ( which is exceptional ) is the service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also like the display .\n->i also like the display .\n[{'aspect': 'display', 'opinion': 'like', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: i got this laptop 2 days ago and it says plugged in , not charged .\n->i got this laptop 2 days ago and it says plugged in , not charged .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n->", + "output": "{\"text\": \"The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\", \"labels\": \"[{'aspect': 'anti-pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filling pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the body of the chromebook feels solid due to the aluminium body .\n->the body of the chromebook feels solid due to the aluminium body .\n[{'aspect': 'body of the chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n->but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The waitress suggested glasses of wine that went very well with the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waitress suggested glasses of wine that went very well with the food .\n->", + "output": "{\"text\": \"The waitress suggested glasses of wine that went very well with the food .\", \"labels\": \"[{'aspect': 'glasses of wine', 'opinion': 'went very well with the food', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The drinks are always well made and wine selection is fairly priced .\n->The drinks are always well made and wine selection is fairly priced .\n[{'aspect': 'drinks', 'opinion': 'well made', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine selection', 'opinion': 'fairly priced', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n->my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n[{'aspect': 'bagel with lox spread', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagles', 'opinion': 'unbeliavably good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: They might be all business at the counter when you give your order , but their food says I love you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey might be all business at the counter when you give your order , but their food says I love you .\n->", + "output": "{\"text\": \"They might be all business at the counter when you give your order , but their food says I love you .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'counter', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so one star off for the keyboard .\n->so one star off for the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: mac really knows how to build a good dev laptop\n->mac really knows how to build a good dev laptop\n[{'aspect': 'mac', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->", + "output": "{\"text\": \"We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\", \"labels\": \"[{'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the webcam sucks but i don ' t care about that .\n->- the webcam sucks but i don ' t care about that .\n[{'aspect': 'webcam', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: maggot in the food !\n->maggot in the food !\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Good luck getting a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood luck getting a table .\n->", + "output": "{\"text\": \"Good luck getting a table .\", \"labels\": \"[{'aspect': 'getting a table', 'opinion': 'Good luck', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lo and behold , i hadn ' t read quite all of the 1 * reviews : the worst was a teacher who had ordered so many for her several classes and said ` ` i think the problem is with the motherboard .\n->lo and behold , i hadn ' t read quite all of the 1 * reviews : the worst was a teacher who had ordered so many for her several classes and said ` ` i think the problem is with the motherboard .\n[{'aspect': 'motherboard', 'opinion': 'worst', 'polarity': 'negative', 'category': 'MOTHERBOARD#QUALITY'}]\nExample:\ntext: so the audio can easily be muffled .\n->so the audio can easily be muffled .\n[{'aspect': 'audio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: The takeout menu says to keep an eye out for an expanded menu offering more italian dishes , I ca n't wait !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe takeout menu says to keep an eye out for an expanded menu offering more italian dishes , I ca n't wait !\n->", + "output": "{\"text\": \"The takeout menu says to keep an eye out for an expanded menu offering more italian dishes , I ca n't wait !\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'expanded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'italian dishes', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The dining room is quietly elegant with no music to shout over -- how refreshing !\n->The dining room is quietly elegant with no music to shout over -- how refreshing !\n[{'aspect': 'dining room', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining room', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but overall a good laptop for productivity .\n->but overall a good laptop for productivity .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: The pizza is good though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza is good though .\n->", + "output": "{\"text\": \"The pizza is good though .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you 're craving for Haru 's great food , especially the House Roll , but ca n't stand the wait building outisde , head across the street to their Sake Bar !\n->If you 're craving for Haru 's great food , especially the House Roll , but ca n't stand the wait building outisde , head across the street to their Sake Bar !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The octopus eaters were floored by the Octopus salad .\n->The octopus eaters were floored by the Octopus salad .\n[{'aspect': 'Octopus salad', 'opinion': 'floored', 'polarity': 'positive', 'category': 'NULL'}]\ntext: bottles of wine are cheap and good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbottles of wine are cheap and good .\n->", + "output": "{\"text\": \"bottles of wine are cheap and good .\", \"labels\": \"[{'aspect': 'bottles of wine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bottles of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when i went .\n->food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when i went .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'view of the new york city skiline', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\nExample:\ntext: it is set far from the small street it ' s on , and there is no traffic noise .\n->it is set far from the small street it ' s on , and there is no traffic noise .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n->", + "output": "{\"text\": \"The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\", \"labels\": \"[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: horrible , purchased directly from google , dead pixel on arrival , note feature did not pick up stylus and bluetooth rarely worked .\n->horrible , purchased directly from google , dead pixel on arrival , note feature did not pick up stylus and bluetooth rarely worked .\n[{'aspect': 'NULL', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'pixel', 'opinion': 'dead', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: right out of the box , this computer is really slow , but two simple steps easily fix that issue .\n->right out of the box , this computer is really slow , but two simple steps easily fix that issue .\n[{'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n->", + "output": "{\"text\": \"You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\", \"labels\": \"[{'aspect': 'crabmeat lasagna', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n->it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'screen', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it is going on 3 hours now and is only 17 % done .\n->it is going on 3 hours now and is only 17 % done .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Very good service and very good prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nVery good service and very good prices .\n->", + "output": "{\"text\": \"Very good service and very good prices .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Average to good Thai food , but terrible delivery .\n->Average to good Thai food , but terrible delivery .\n[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it ' s a real treat .\n->it ' s a real treat .\n[{'aspect': 'NULL', 'opinion': 'treat', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n->", + "output": "{\"text\": \"The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it stopped working a week after i recieved it .\n->it stopped working a week after i recieved it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food looked very appetizing and delicious since it came on a variety of fancy plates .\n->The food looked very appetizing and delicious since it came on a variety of fancy plates .\n[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'plates', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Our food was great too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur food was great too !\n->", + "output": "{\"text\": \"Our food was great too !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n->this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n[{'aspect': 'chromebook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: those rolls were big , but not good and sashimi was n ' t fresh .\n->those rolls were big , but not good and sashimi was n ' t fresh .\n[{'aspect': 'rolls', 'opinion': 'big', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sashimi', 'opinion': \"was n ' t fresh\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The most annoying thing , though , is the fact that the servers seem to be trained to drive revenue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe most annoying thing , though , is the fact that the servers seem to be trained to drive revenue .\n->", + "output": "{\"text\": \"The most annoying thing , though , is the fact that the servers seem to be trained to drive revenue .\", \"labels\": \"[{'aspect': 'servers', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: go here for a romantic dinner but not for an all out wow dining experience .\n->go here for a romantic dinner but not for an all out wow dining experience .\n[{'aspect': 'NULL', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'wow', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the macbook was delivered soon and it is exactly as described\n->the macbook was delivered soon and it is exactly as described\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Please try the Filet Mignon , its just the most tender piece ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPlease try the Filet Mignon , its just the most tender piece ever .\n->", + "output": "{\"text\": \"Please try the Filet Mignon , its just the most tender piece ever .\", \"labels\": \"[{'aspect': 'Filet Mignon', 'opinion': 'tender', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is where it really really gets bad : the manager said , there is absolutely nothing we can do , it ' s a matter of taste that she did n ' t like it , and i can not comp it .\n->this is where it really really gets bad : the manager said , there is absolutely nothing we can do , it ' s a matter of taste that she did n ' t like it , and i can not comp it .\n[{'aspect': 'manager', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: do not purchase this laptop .\n->do not purchase this laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the drinks are amazing and half off till 8pm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe drinks are amazing and half off till 8pm .\n->", + "output": "{\"text\": \"the drinks are amazing and half off till 8pm .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keys are easy to type on and the laptop itself is thin yet feels solid and well constructed .\n->the keys are easy to type on and the laptop itself is thin yet feels solid and well constructed .\n[{'aspect': 'keys', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'well constructed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: lives up to the hype\n->lives up to the hype\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n->", + "output": "{\"text\": \"i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\", \"labels\": \"[{'aspect': 'beef cubes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop has handled everything i have thrown at it .\n->this laptop has handled everything i have thrown at it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: We were seated promptly as we had reservations , however after that the service was slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were seated promptly as we had reservations , however after that the service was slow .\n->", + "output": "{\"text\": \"We were seated promptly as we had reservations , however after that the service was slow .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is among the best .\n->this is among the best .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I 'm happy to have Nosh in the neighborhood and the food is very comforting .\n->I 'm happy to have Nosh in the neighborhood and the food is very comforting .\n[{'aspect': 'food', 'opinion': 'comforting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was pretty good , but a little flavorless and the portions very small , including dessert .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was pretty good , but a little flavorless and the portions very small , including dessert .\n->", + "output": "{\"text\": \"The food was pretty good , but a little flavorless and the portions very small , including dessert .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great spot , whether looking for a couple of drinks or quiet dinner .\n->Great spot , whether looking for a couple of drinks or quiet dinner .\n[{'aspect': 'spot', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: every app i have downloaded from the google app store has worked perfectly .\n->every app i have downloaded from the google app store has worked perfectly .\n[{'aspect': 'app', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Lived in Shanghai most of my life and thought the food was comparable to the flagship Green Bo restaurant there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLived in Shanghai most of my life and thought the food was comparable to the flagship Green Bo restaurant there .\n->", + "output": "{\"text\": \"Lived in Shanghai most of my life and thought the food was comparable to the flagship Green Bo restaurant there .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'comparable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a weakness is the chicken in the salads .\n->a weakness is the chicken in the salads .\n[{'aspect': 'chicken in the salads', 'opinion': 'weakness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n->", + "output": "{\"text\": \"I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: yes , the prices are high , but i felt it was worth it .\n->yes , the prices are high , but i felt it was worth it .\n[{'aspect': 'NULL', 'opinion': 'high', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: acer had no answer for that question .\n->acer had no answer for that question .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: All the NYU students love this place so it makes for a fun young atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll the NYU students love this place so it makes for a fun young atmosphere .\n->", + "output": "{\"text\": \"All the NYU students love this place so it makes for a fun young atmosphere .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'fun young', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: multiple system crashes .\n->multiple system crashes .\n[{'aspect': 'system', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: this place is not worth the prices .\n->this place is not worth the prices .\n[{'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food here does a great service to the name ( Cantonese that is ... ) .\n->", + "output": "{\"text\": \"The food here does a great service to the name ( Cantonese that is ... ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great computer .\n->this is a great computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: It 's a great place to pick up a cheap lunch or dinner .\n->It 's a great place to pick up a cheap lunch or dinner .\n[{'aspect': 'lunch', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\ntext: But , nothing stands out about the cooking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut , nothing stands out about the cooking .\n->", + "output": "{\"text\": \"But , nothing stands out about the cooking .\", \"labels\": \"[{'aspect': 'cooking', 'opinion': 'nothing stands out', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: really disappointing results with sound volume and volume consistency .\n->really disappointing results with sound volume and volume consistency .\n[{'aspect': 'sound volume', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'volume consistency', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: Salads are a delicious way to begin the meal .\n->Salads are a delicious way to begin the meal .\n[{'aspect': 'Salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nJoya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n->", + "output": "{\"text\": \"Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'colorful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also ordered the change mojito , which was out of this world .\n->i also ordered the change mojito , which was out of this world .\n[{'aspect': 'change mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n->i loved this chromebook but i had to return it bevause it had sound issues .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .\n->", + "output": "{\"text\": \"The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .\", \"labels\": \"[{'aspect': 'Prix Fixe menu', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do n ' t be fooled by crowds of people .\n->do n ' t be fooled by crowds of people .\n[{'aspect': 'NULL', 'opinion': 'fooled', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n->if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n[{'aspect': 'sushi', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I work near-by , and they have the BEST oatmeal in the neighborhood- not a packaged or quick-cooked item .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI work near-by , and they have the BEST oatmeal in the neighborhood- not a packaged or quick-cooked item .\n->", + "output": "{\"text\": \"I work near-by , and they have the BEST oatmeal in the neighborhood- not a packaged or quick-cooked item .\", \"labels\": \"[{'aspect': 'oatmeal', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best dining experience in the west village !\n->best dining experience in the west village !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i eventually returned it .\n->i eventually returned it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Too bad the food was n't of the same heritage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nToo bad the food was n't of the same heritage .\n->", + "output": "{\"text\": \"Too bad the food was n't of the same heritage .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but it lost the coil whine roulette - - badly .\n->but it lost the coil whine roulette - - badly .\n[{'aspect': 'NULL', 'opinion': 'badly', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: The only problem is that the manager is a complete incompetent .\n->The only problem is that the manager is a complete incompetent .\n[{'aspect': 'manager', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n->", + "output": "{\"text\": \"The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Chilean Sea Bass', 'opinion': 'except', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you just have to deal with a low battery and that ' s all\n->you just have to deal with a low battery and that ' s all\n[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: just that it seems that the hard drive doesn ' t work properly .\n->just that it seems that the hard drive doesn ' t work properly .\n[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSince it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n->", + "output": "{\"text\": \"Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Total hipster-wannabe attitude in an otherwise sweet spot .\n->Total hipster-wannabe attitude in an otherwise sweet spot .\n[{'aspect': 'spot', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the display is beautiful and the amount if software you get makes it worth the price !\n->the display is beautiful and the amount if software you get makes it worth the price !\n[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'software', 'opinion': 'worth', 'polarity': 'positive', 'category': 'SOFTWARE#PRICE'}]\ntext: When he finally did , he was unable to make a gin and tonic -- could n't find tonic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhen he finally did , he was unable to make a gin and tonic -- could n't find tonic .\n->", + "output": "{\"text\": \"When he finally did , he was unable to make a gin and tonic -- could n't find tonic .\", \"labels\": \"[{'aspect': 'gin and tonic', 'opinion': 'unable', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n->The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n[{'aspect': 'mussaman curry', 'opinion': 'thin', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fried tofu', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato', 'opinion': 'poorly cooked', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: a little pricey but it really hits the spot on a sunday morning !\n->a little pricey but it really hits the spot on a sunday morning !\n[{'aspect': 'NULL', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'hits', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The dim sum is ok but does n't taste that fresh , and the little dishes do n't look steamy hot as they should ( also note lack of Chinese here ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dim sum is ok but does n't taste that fresh , and the little dishes do n't look steamy hot as they should ( also note lack of Chinese here ) .\n->", + "output": "{\"text\": \"The dim sum is ok but does n't taste that fresh , and the little dishes do n't look steamy hot as they should ( also note lack of Chinese here ) .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'ok', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'little dishes', 'opinion': \"do n't look steamy hot\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the potato balls were not dry at all . . . in fact it was buttery .\n->the potato balls were not dry at all . . . in fact it was buttery .\n[{'aspect': 'potato balls', 'opinion': 'not dry', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'potato balls', 'opinion': 'buttery', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .\n->It may be a bit packed on weekends , but the vibe is good and it is the best French food you will find in the area .\n[{'aspect': 'vibe', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'French food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service is ok , some of the people did n't get what they asked for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is ok , some of the people did n't get what they asked for .\n->", + "output": "{\"text\": \"The service is ok , some of the people did n't get what they asked for .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what you are paying for is the environment and the name .\n->what you are paying for is the environment and the name .\n[{'aspect': 'environment', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: january 21 , 2016 update : i love this laptop even more than before .\n->january 21 , 2016 update : i love this laptop even more than before .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: If you want good authentic Thai this place is not the place to go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you want good authentic Thai this place is not the place to go .\n->", + "output": "{\"text\": \"If you want good authentic Thai this place is not the place to go .\", \"labels\": \"[{'aspect': 'Thai', 'opinion': 'good authentic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now i ca n ' t even get into my laptop because the startup is all jacked up .\n->now i ca n ' t even get into my laptop because the startup is all jacked up .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the fans did not turn on loudly if at all .\n->the fans did not turn on loudly if at all .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: I recommend getting a reservation even though we saw people seated without one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recommend getting a reservation even though we saw people seated without one .\n->", + "output": "{\"text\": \"I recommend getting a reservation even though we saw people seated without one .\", \"labels\": \"[{'aspect': 'reservation', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good price , good quality and good service in korea .\n->good price , good quality and good service in korea .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: Drinks way over priced .\n->Drinks way over priced .\n[{'aspect': 'Drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'over', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Staff is very accomodating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nStaff is very accomodating .\n->", + "output": "{\"text\": \"Staff is very accomodating .\", \"labels\": \"[{'aspect': 'Staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first chromebook , and i ' m absolutely loving it .\n->this is my first chromebook , and i ' m absolutely loving it .\n[{'aspect': 'this', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nVery popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n->", + "output": "{\"text\": \"Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'sake-friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the style , aluminum shell , 14 inches monitor , and decent resolution .\n->love the style , aluminum shell , 14 inches monitor , and decent resolution .\n[{'aspect': 'style', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'aluminum shell', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '14 inches monitor', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: this restaurant is vegetarian ; there are no meat dishes whatsoever .\n->this restaurant is vegetarian ; there are no meat dishes whatsoever .\n[{'aspect': 'meat dishes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The food was terrific and the service classy , attentive , without being overbearing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was terrific and the service classy , attentive , without being overbearing .\n->", + "output": "{\"text\": \"The food was terrific and the service classy , attentive , without being overbearing .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'classy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'without being overbearing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the overall device is slim and lightweight .\n->the overall device is slim and lightweight .\n[{'aspect': 'device', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n->as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: This is an amazing place to try some roti rolls .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is an amazing place to try some roti rolls .\n->", + "output": "{\"text\": \"This is an amazing place to try some roti rolls .\", \"labels\": \"[{'aspect': 'roti rolls', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I love the fact that the pizza tastes so good and is so cheap .\n->I love the fact that the pizza tastes so good and is so cheap .\n[{'aspect': 'pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but just at first glance , this thing is top quality .\n->but just at first glance , this thing is top quality .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: Fresh ingredients and everything is made to order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFresh ingredients and everything is made to order .\n->", + "output": "{\"text\": \"Fresh ingredients and everything is made to order .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'Fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the best !\n->the best !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: - no ethernet connection and firewire 800 port .\n->- no ethernet connection and firewire 800 port .\n[{'aspect': 'firewire 800 port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: We have never had any problems with charging the meal or the tip , and the food was delivered quickly , but we live only a few minutes walk from them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe have never had any problems with charging the meal or the tip , and the food was delivered quickly , but we live only a few minutes walk from them .\n->", + "output": "{\"text\": \"We have never had any problems with charging the meal or the tip , and the food was delivered quickly , but we live only a few minutes walk from them .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'never had any problems', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delivered quickly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tip', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the premium feel combined with the charming simplicity of chrome os really gives it a stunning experience .\n->the premium feel combined with the charming simplicity of chrome os really gives it a stunning experience .\n[{'aspect': 'NULL', 'opinion': 'premium', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'charming', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the screen quality is excellent , and i am fussy due to my interest in digital imagery .\n->the screen quality is excellent , and i am fussy due to my interest in digital imagery .\n[{'aspect': 'screen quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: Quick and friendly service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nQuick and friendly service .\n->", + "output": "{\"text\": \"Quick and friendly service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is one great place to eat pizza more out but not a good place for take-out pizza .\n->This is one great place to eat pizza more out but not a good place for take-out pizza .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'take-out pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: basically , it is great for work and media as long as you dont need other proprietary programs to do your work .\n->basically , it is great for work and media as long as you dont need other proprietary programs to do your work .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Try the hot dogs too , they 're snappy and delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the hot dogs too , they 're snappy and delicious .\n->", + "output": "{\"text\": \"Try the hot dogs too , they 're snappy and delicious .\", \"labels\": \"[{'aspect': 'hot dogs', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hot dogs', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hot dogs', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n->we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: I ate here a week ago and found most dishes average at best and too expensive .\n->I ate here a week ago and found most dishes average at best and too expensive .\n[{'aspect': 'dishes', 'opinion': 'average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'too expensive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The noise level was unbearable , conversation impossible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe noise level was unbearable , conversation impossible .\n->", + "output": "{\"text\": \"The noise level was unbearable , conversation impossible .\", \"labels\": \"[{'aspect': 'noise level', 'opinion': 'unbearable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They might be all business at the counter when you give your order , but their food says I love you .\n->They might be all business at the counter when you give your order , but their food says I love you .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'counter', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n->so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: Personal pans are the perfect size for those hungry nights .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPersonal pans are the perfect size for those hungry nights .\n->", + "output": "{\"text\": \"Personal pans are the perfect size for those hungry nights .\", \"labels\": \"[{'aspect': 'Personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this has got to be one of the most overrated restaurants in brooklyn .\n->this has got to be one of the most overrated restaurants in brooklyn .\n[{'aspect': 'NULL', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: And the food was fantastic .\n->And the food was fantastic .\n[{'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Highly recommended is the Spicy Fried Clam Rolls and Spider Rolls .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHighly recommended is the Spicy Fried Clam Rolls and Spider Rolls .\n->", + "output": "{\"text\": \"Highly recommended is the Spicy Fried Clam Rolls and Spider Rolls .\", \"labels\": \"[{'aspect': 'Spicy Fried Clam Rolls', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Spider Rolls', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place blew me away . . . by far my new favorite restaurant on the uppereast side .\n->this place blew me away . . . by far my new favorite restaurant on the uppereast side .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it ' s a real treat .\n->it ' s a real treat .\n[{'aspect': 'NULL', 'opinion': 'treat', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOne of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n->", + "output": "{\"text\": \"One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n->- although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen ratio', 'opinion': 'not optimal', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The menu choices are similar but the taste lacked more flavor than it looked .\n->The menu choices are similar but the taste lacked more flavor than it looked .\n[{'aspect': 'taste', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu choices', 'opinion': 'similar', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Odd for Ave B , not just odd , The place attracts an eclectic crowd to say the least .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOdd for Ave B , not just odd , The place attracts an eclectic crowd to say the least .\n->", + "output": "{\"text\": \"Odd for Ave B , not just odd , The place attracts an eclectic crowd to say the least .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'odd', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fantastic place .\n->Fantastic place .\n[{'aspect': 'place', 'opinion': 'Fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the battery life is outstanding .\n->the battery life is outstanding .\n[{'aspect': 'battery life', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: just got back from lunch at Tamarind and it was excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust got back from lunch at Tamarind and it was excellent .\n->", + "output": "{\"text\": \"just got back from lunch at Tamarind and it was excellent .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing i did not like about the design is the fact that the speakers are on the bottom of the unit .\n->the only thing i did not like about the design is the fact that the speakers are on the bottom of the unit .\n[{'aspect': 'speakers', 'opinion': 'not like', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n->You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n[{'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: head and shoulders above its neighboors on east 6 st , taj mahal is also very comparable , in food quality , to the much overpraised ( and underdeserving ) baluchi 's .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhead and shoulders above its neighboors on east 6 st , taj mahal is also very comparable , in food quality , to the much overpraised ( and underdeserving ) baluchi 's .\n->", + "output": "{\"text\": \"head and shoulders above its neighboors on east 6 st , taj mahal is also very comparable , in food quality , to the much overpraised ( and underdeserving ) baluchi 's .\", \"labels\": \"[{'aspect': 'food quality', 'opinion': 'comparable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food is excellent .\n->food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: but just at first glance , this thing is top quality .\n->but just at first glance , this thing is top quality .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: As we were sitting eating the subpar food the manager proceeded to berate a couple of his employees for putting out the wrong containers for condiments and explained to them how expensive these containers were .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs we were sitting eating the subpar food the manager proceeded to berate a couple of his employees for putting out the wrong containers for condiments and explained to them how expensive these containers were .\n->", + "output": "{\"text\": \"As we were sitting eating the subpar food the manager proceeded to berate a couple of his employees for putting out the wrong containers for condiments and explained to them how expensive these containers were .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'containers', 'opinion': 'expensive', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked great , no issues besides the mouse began to freeze when the computer was idled for too long .\n->it worked great , no issues besides the mouse began to freeze when the computer was idled for too long .\n[{'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: The dim sum however was very good .\n->The dim sum however was very good .\n[{'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great Indian food and the service is incredible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat Indian food and the service is incredible .\n->", + "output": "{\"text\": \"Great Indian food and the service is incredible .\", \"labels\": \"[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never got an explanation as to what was going on .\n->never got an explanation as to what was going on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Still , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...\n->Still , any quibbles about the bill were off-set by the pour-your-own measures of liquers which were courtesey of the house ...\n[{'aspect': 'measures of liquers', 'opinion': 'pour-your-own', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'measures of liquers', 'opinion': 'courtesey', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Decor is nice and minimalist , food simple yet very well presented and cooked , and the wine list matches the food very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDecor is nice and minimalist , food simple yet very well presented and cooked , and the wine list matches the food very well .\n->", + "output": "{\"text\": \"Decor is nice and minimalist , food simple yet very well presented and cooked , and the wine list matches the food very well .\", \"labels\": \"[{'aspect': 'Decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Decor', 'opinion': 'minimalist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'simple', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'well presented and cooked', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n->While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - battery life is pretty amazing at 10 - 11hrs\n->- battery life is pretty amazing at 10 - 11hrs\n[{'aspect': 'battery life', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: We ordered a tuna melt - it came with out cheese which just made it a tuna sandwich .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ordered a tuna melt - it came with out cheese which just made it a tuna sandwich .\n->", + "output": "{\"text\": \"We ordered a tuna melt - it came with out cheese which just made it a tuna sandwich .\", \"labels\": \"[{'aspect': 'tuna melt', 'opinion': 'with out', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'with out', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'tuna sandwich', 'opinion': 'with out', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n->all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n[{'aspect': 'web browsing', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: these are overpriced and you can get better just around the corner :\n->these are overpriced and you can get better just around the corner :\n[{'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The waiters were not attentive except that the bill turned up on the table before we were finished .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waiters were not attentive except that the bill turned up on the table before we were finished .\n->", + "output": "{\"text\": \"The waiters were not attentive except that the bill turned up on the table before we were finished .\", \"labels\": \"[{'aspect': 'waiters', 'opinion': 'attentive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n->i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n[{'aspect': 'touch pad', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: for starters they delivered us someone else ' s order .\n->for starters they delivered us someone else ' s order .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The sangria was pretty tasty and good on a hot muggy day .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sangria was pretty tasty and good on a hot muggy day .\n->", + "output": "{\"text\": \"The sangria was pretty tasty and good on a hot muggy day .\", \"labels\": \"[{'aspect': 'sangria', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sangria', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cute place , nice wait staff but would never go there again .\n->cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: but , when software compatibility began stacking up , it became a nogo for me .\n->but , when software compatibility began stacking up , it became a nogo for me .\n[{'aspect': 'software', 'opinion': 'nogo', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: For the people who want great food plus great service , Roxy is a place to AVOID !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor the people who want great food plus great service , Roxy is a place to AVOID !\n->", + "output": "{\"text\": \"For the people who want great food plus great service , Roxy is a place to AVOID !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: honestly , i ' m debating returning this laptop .\n->honestly , i ' m debating returning this laptop .\n[{'aspect': 'laptop', 'opinion': 'debating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s fast , the display looks like a macbook pro , as does the aluminum case .\n->it ' s fast , the display looks like a macbook pro , as does the aluminum case .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: I had the best ravioli ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had the best ravioli ever .\n->", + "output": "{\"text\": \"I had the best ravioli ever .\", \"labels\": \"[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the best shabu - shabu restaurant in the try - state area .\n->this is the best shabu - shabu restaurant in the try - state area .\n[{'aspect': 'shabu - shabu restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: beware this product seems to have no quality control .\n->beware this product seems to have no quality control .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: Ive been here a bunch of times now and the service is always outstanding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIve been here a bunch of times now and the service is always outstanding .\n->", + "output": "{\"text\": \"Ive been here a bunch of times now and the service is always outstanding .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love my new lenovo .\n->love my new lenovo .\n[{'aspect': 'lenovo', 'opinion': 'love', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n->downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n[{'aspect': 'appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Quite frankly , this is some of the worst sushi I have ever tried .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nQuite frankly , this is some of the worst sushi I have ever tried .\n->", + "output": "{\"text\": \"Quite frankly , this is some of the worst sushi I have ever tried .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: usb - c connectors on both sides can both charge , making the power cord location an option .\n->usb - c connectors on both sides can both charge , making the power cord location an option .\n[{'aspect': 'usb - c connectors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\nExample:\ntext: We ordered a tuna melt - it came with out cheese which just made it a tuna sandwich .\n->We ordered a tuna melt - it came with out cheese which just made it a tuna sandwich .\n[{'aspect': 'tuna melt', 'opinion': 'with out', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'with out', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'tuna sandwich', 'opinion': 'with out', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The food is great , service is ok .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is great , service is ok .\n->", + "output": "{\"text\": \"The food is great , service is ok .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: downloading is very fast over wifi .\n->downloading is very fast over wifi .\n[{'aspect': 'wifi', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n->this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n[{'aspect': 'computer', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Spice is great Thai food , love the inexpensive appetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSpice is great Thai food , love the inexpensive appetizers .\n->", + "output": "{\"text\": \"Spice is great Thai food , love the inexpensive appetizers .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizers', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizers', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall this chromebook is perfect for my current use - case and would recommend it to anyone in the chromebook market with 300 dollars to spare .\n->overall this chromebook is perfect for my current use - case and would recommend it to anyone in the chromebook market with 300 dollars to spare .\n[{'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i picked the grilled black cod as my entree , which i absolutely devoured while someone commented that the grilled salmon dish was better .\n->i picked the grilled black cod as my entree , which i absolutely devoured while someone commented that the grilled salmon dish was better .\n[{'aspect': 'grilled black cod', 'opinion': 'devoured', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled salmon dish', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The service was attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was attentive .\n->", + "output": "{\"text\": \"The service was attentive .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the perfect date spot for Williamsburg couples .\n->This is the perfect date spot for Williamsburg couples .\n[{'aspect': 'date spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the only real complain is the same one everyone else has with this model and that is the battery life could be better .\n->the only real complain is the same one everyone else has with this model and that is the battery life could be better .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: He not only makes his own homemade mozzarella , but every pie is ultra fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHe not only makes his own homemade mozzarella , but every pie is ultra fresh .\n->", + "output": "{\"text\": \"He not only makes his own homemade mozzarella , but every pie is ultra fresh .\", \"labels\": \"[{'aspect': 'mozzarella', 'opinion': 'homemade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'ultra fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the design and atmosphere is just as good .\n->the design and atmosphere is just as good .\n[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: love love love this laptop !\n->love love love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Fish was overdone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFish was overdone .\n->", + "output": "{\"text\": \"Fish was overdone .\", \"labels\": \"[{'aspect': 'Fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great customer service .\n->great customer service .\n[{'aspect': 'customer service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: great food and the prices are very reasonable .\n->great food and the prices are very reasonable .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhen you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\n->", + "output": "{\"text\": \"When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\", \"labels\": \"[{'aspect': 'main dining room', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceiling', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceiling', 'opinion': 'hand-painted high', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is outstanding and the service is quick , friendly and very professional .\n->The food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Terrible , terrible management - deserves to be shut-down .\n->Terrible , terrible management - deserves to be shut-down .\n[{'aspect': 'management', 'opinion': 'Terrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: We visited Bread Bar during January restaurant week and were so pleased with the menu selections and service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe visited Bread Bar during January restaurant week and were so pleased with the menu selections and service .\n->", + "output": "{\"text\": \"We visited Bread Bar during January restaurant week and were so pleased with the menu selections and service .\", \"labels\": \"[{'aspect': 'menu selections', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Do n't waste money on decor .\n->Do n't waste money on decor .\n[{'aspect': 'decor', 'opinion': 'waste', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: also make sure you pay attention to the music being piped in - quite a weird selection .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso make sure you pay attention to the music being piped in - quite a weird selection .\n->", + "output": "{\"text\": \"also make sure you pay attention to the music being piped in - quite a weird selection .\", \"labels\": \"[{'aspect': 'music', 'opinion': 'weird', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n->i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n[{'aspect': 'NULL', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Saul is the best restaurant on Smith Street and in Brooklyn .\n->Saul is the best restaurant on Smith Street and in Brooklyn .\n[{'aspect': 'Saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The establishment scores big points in presentation and style .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe establishment scores big points in presentation and style .\n->", + "output": "{\"text\": \"The establishment scores big points in presentation and style .\", \"labels\": \"[{'aspect': 'establishment', 'opinion': 'scores big points', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 2 ) slow start up and performance given\n->2 ) slow start up and performance given\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n->The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n[{'aspect': 'wait staff', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'knowledgable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'likeable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The wait staff is very friendly , if not overly efficient .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wait staff is very friendly , if not overly efficient .\n->", + "output": "{\"text\": \"The wait staff is very friendly , if not overly efficient .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'not overly efficient', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our food was great too !\n->our food was great too !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The waiters were not attentive except that the bill turned up on the table before we were finished .\n->The waiters were not attentive except that the bill turned up on the table before we were finished .\n[{'aspect': 'waiters', 'opinion': 'attentive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The dim sum however was very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dim sum however was very good .\n->", + "output": "{\"text\": \"The dim sum however was very good .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so what you really end up paying for is the restaurant not the food .\n->so what you really end up paying for is the restaurant not the food .\n[{'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: everything is amazing , love the look and everything about it until now .\n->everything is amazing , love the look and everything about it until now .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Service has always been friendly and efficient .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService has always been friendly and efficient .\n->", + "output": "{\"text\": \"Service has always been friendly and efficient .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it sounded clean and simple , exactly what we need .\n->it sounded clean and simple , exactly what we need .\n[{'aspect': 'NULL', 'opinion': 'clean', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: All we received was an apology as we left to see our show without dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll we received was an apology as we left to see our show without dinner .\n->", + "output": "{\"text\": \"All we received was an apology as we left to see our show without dinner .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'without', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we took advanatage of the half price sushi deal on saturday so it was well worth it .\n->we took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: pros - slim , lightweight laptop due to 8th gen core - i5 .\n->pros - slim , lightweight laptop due to 8th gen core - i5 .\n[{'aspect': 'laptop', 'opinion': 'pros', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '8th gen core - i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\ntext: The table next to us asked if he crushed the grapes himself when their long overdue bottle of wine finally arrived .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe table next to us asked if he crushed the grapes himself when their long overdue bottle of wine finally arrived .\n->", + "output": "{\"text\": \"The table next to us asked if he crushed the grapes himself when their long overdue bottle of wine finally arrived .\", \"labels\": \"[{'aspect': 'bottle of wine', 'opinion': 'long overdue', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I was very disappointed with this restaurant .\n->I was very disappointed with this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: And , atlhough tables opened up next to us and we ASKED for a slightly larger space , they left us awkardly seated .\n->And , atlhough tables opened up next to us and we ASKED for a slightly larger space , they left us awkardly seated .\n[{'aspect': 'space', 'opinion': 'larger', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Prices are very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPrices are very good .\n->", + "output": "{\"text\": \"Prices are very good .\", \"labels\": \"[{'aspect': 'Prices', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was mediocre , and the lack of air conditioning made for a less than comfortable meal .\n->The service was mediocre , and the lack of air conditioning made for a less than comfortable meal .\n[{'aspect': 'service', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'air conditioning', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'comfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the problem is that it never charged .\n->the problem is that it never charged .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The Dim Sum was so-so , but not spectacular .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Dim Sum was so-so , but not spectacular .\n->", + "output": "{\"text\": \"The Dim Sum was so-so , but not spectacular .\", \"labels\": \"[{'aspect': 'Dim Sum', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Dim Sum', 'opinion': 'not spectacular', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however i ' m happy to report that the keyboard is great and i ' ve already gotten use to it .\n->however i ' m happy to report that the keyboard is great and i ' ve already gotten use to it .\n[{'aspect': 'keyboard', 'opinion': 'happy', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: everyone was more then happy with his choices .\n->everyone was more then happy with his choices .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The food was so-so .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was so-so .\n->", + "output": "{\"text\": \"The food was so-so .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n->in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n[{'aspect': 'fingerprints', 'opinion': 'dislike', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}]\nExample:\ntext: screen turn black and won ' t turn on within a month rarely use .\n->screen turn black and won ' t turn on within a month rarely use .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: The place is larger than most and features adequate seating unlike most joints , and has a bar which deserves a mention .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is larger than most and features adequate seating unlike most joints , and has a bar which deserves a mention .\n->", + "output": "{\"text\": \"The place is larger than most and features adequate seating unlike most joints , and has a bar which deserves a mention .\", \"labels\": \"[{'aspect': 'seating', 'opinion': 'adequate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'deserves', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'larger', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything on the menu is great .\n->everything on the menu is great .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: All the NYU students love this place so it makes for a fun young atmosphere .\n->All the NYU students love this place so it makes for a fun young atmosphere .\n[{'aspect': 'atmosphere', 'opinion': 'fun young', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThrew my fiance 's surprise 30th birthday dinner here could n't be happier .\n->", + "output": "{\"text\": \"Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': \"could n't be happier\", 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is awful .\n->the service is awful .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: got a date ? go here !\n->got a date ? go here !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Food - awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood - awesome .\n->", + "output": "{\"text\": \"Food - awesome .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery lasts a solid 8 + hours unless you ' re playing games its more like 4 - 5 .\n->the battery lasts a solid 8 + hours unless you ' re playing games its more like 4 - 5 .\n[{'aspect': 'battery', 'opinion': 'solid', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: $ 20 for all you can eat sushi can not be beaten .\n->$ 20 for all you can eat sushi can not be beaten .\n[{'aspect': 'sushi', 'opinion': 'can not be beaten', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: May , the owner always has a smile on her and will warmly greet you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMay , the owner always has a smile on her and will warmly greet you .\n->", + "output": "{\"text\": \"May , the owner always has a smile on her and will warmly greet you .\", \"labels\": \"[{'aspect': 'owner', 'opinion': 'warmly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n->Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n[{'aspect': 'Ow Ley Soh', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ow Ley Soh', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n->it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'insane', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: For some reason , all the seafood on the menu was unavailable except for the Salmon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor some reason , all the seafood on the menu was unavailable except for the Salmon .\n->", + "output": "{\"text\": \"For some reason , all the seafood on the menu was unavailable except for the Salmon .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'unavailable', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'unavailable', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Salmon', 'opinion': 'except', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This big draw is the all you can sushi here for $ 19.95 !\n->This big draw is the all you can sushi here for $ 19.95 !\n[{'aspect': 'sushi', 'opinion': 'draw', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the laptop charger has sparked repeatedly .\n->the laptop charger has sparked repeatedly .\n[{'aspect': 'laptop charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: The beverages were excellent , and the dessert was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe beverages were excellent , and the dessert was good .\n->", + "output": "{\"text\": \"The beverages were excellent , and the dessert was good .\", \"labels\": \"[{'aspect': 'beverages', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: please note that the track pad is way better than most .\n->please note that the track pad is way better than most .\n[{'aspect': 'track pad', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n->my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n[{'aspect': 'Scallion Pancake', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Scallion Pancake', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shredded Squid Family Style', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shuizhu Fish', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNext time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n->", + "output": "{\"text\": \"Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\", \"labels\": \"[{'aspect': 'Asian appetizers', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n->thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n[{'aspect': 'windows 10', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The service was excellent , the food was excellent , but the entire experience was very cool .\n->The service was excellent , the food was excellent , but the entire experience was very cool .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Please if your thinking about it go , and stay the wait you wo n't be disappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPlease if your thinking about it go , and stay the wait you wo n't be disappointed .\n->", + "output": "{\"text\": \"Please if your thinking about it go , and stay the wait you wo n't be disappointed .\", \"labels\": \"[{'aspect': 'wait', 'opinion': \"wo n't be disappointed\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the m3 processor is pretty good ( decent speedometer score ) .\n->the m3 processor is pretty good ( decent speedometer score ) .\n[{'aspect': 'm3 processor', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'm3 processor', 'opinion': 'decent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\nExample:\ntext: I got the $ 10 10-piece dim sum combo , every bite of which was great .\n->I got the $ 10 10-piece dim sum combo , every bite of which was great .\n[{'aspect': 'dim sum combo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n->", + "output": "{\"text\": \"If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: on day one , i had been using it for maybe 2 hours and it randomly shut off .\n->on day one , i had been using it for maybe 2 hours and it randomly shut off .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: a little crowded but they move that line really fast !\n->a little crowded but they move that line really fast !\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: When going out for a nice dinner , I like a nice ambiance as well as very good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhen going out for a nice dinner , I like a nice ambiance as well as very good food .\n->", + "output": "{\"text\": \"When going out for a nice dinner , I like a nice ambiance as well as very good food .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely has issues with windows 10 .\n->definitely has issues with windows 10 .\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n->Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n[{'aspect': 'pesto pizza', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house salad', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bottle of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Highly recommend this as great value for excellent sushi and service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHighly recommend this as great value for excellent sushi and service .\n->", + "output": "{\"text\": \"Highly recommend this as great value for excellent sushi and service .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chrome book joins the group and is itself excellent and different .\n->this chrome book joins the group and is itself excellent and different .\n[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chrome book', 'opinion': 'different', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the wifi card recently died after 14 months .\n->the wifi card recently died after 14 months .\n[{'aspect': 'wifi card', 'opinion': 'died', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: This place would be so much better served by being run by a group that actually understands customer service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place would be so much better served by being run by a group that actually understands customer service .\n->", + "output": "{\"text\": \"This place would be so much better served by being run by a group that actually understands customer service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'would be so much better', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is so much fun .\n->This place is so much fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my only complaint is that the trackpad is just awful .\n->my only complaint is that the trackpad is just awful .\n[{'aspect': 'trackpad', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: A++ The service was good to excellent along with the attitude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA++ The service was good to excellent along with the attitude .\n->", + "output": "{\"text\": \"A++ The service was good to excellent along with the attitude .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'attitude', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this one is pretty , but obviously not sturdy .\n->this one is pretty , but obviously not sturdy .\n[{'aspect': 'NULL', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not sturdy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: 1 , the touchscreen stopped working after 6 months for finger presses .\n->1 , the touchscreen stopped working after 6 months for finger presses .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: The food is awesome - definitely try the striped bass .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is awesome - definitely try the striped bass .\n->", + "output": "{\"text\": \"The food is awesome - definitely try the striped bass .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'striped bass', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i returned this one as well .\n->i returned this one as well .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I can not imagine better Indian food in all of the city .\n->I can not imagine better Indian food in all of the city .\n[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: black white shakes came out good also .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nblack white shakes came out good also .\n->", + "output": "{\"text\": \"black white shakes came out good also .\", \"labels\": \"[{'aspect': 'black white shakes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n->everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n[{'aspect': 'zucchero pomodori', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: excellent product and experience with the purchase .\n->excellent product and experience with the purchase .\n[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: It 's simply the best meal in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's simply the best meal in NYC .\n->", + "output": "{\"text\": \"It 's simply the best meal in NYC .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n->the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n->google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n[{'aspect': \"google ' s own services\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: We go on Mondays for the prix fixe and our experience with the food has been comparable to Blue Ribbon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe go on Mondays for the prix fixe and our experience with the food has been comparable to Blue Ribbon .\n->", + "output": "{\"text\": \"We go on Mondays for the prix fixe and our experience with the food has been comparable to Blue Ribbon .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'comparable', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n->The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n[{'aspect': 'outdoor atmosphere', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the seafood dynamite is also otherworldly .\n->the seafood dynamite is also otherworldly .\n[{'aspect': 'seafood dynamite', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: However , they 've got the most amazing pastrami and the soups hit the spot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , they 've got the most amazing pastrami and the soups hit the spot .\n->", + "output": "{\"text\": \"However , they 've got the most amazing pastrami and the soups hit the spot .\", \"labels\": \"[{'aspect': 'pastrami', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soups', 'opinion': 'hit the spot', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: computer came in good condition and at a good price .\n->computer came in good condition and at a good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#PRICE'}]\nExample:\ntext: we contacted both acer and amazon , and they both informed us that it has to be sent back for repairs again .\n->we contacted both acer and amazon , and they both informed us that it has to be sent back for repairs again .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: Great bagels made the old-fashioned way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat bagels made the old-fashioned way .\n->", + "output": "{\"text\": \"Great bagels made the old-fashioned way .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n->in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great little computer .\n->great little computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: This is some really good , inexpensive sushi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is some really good , inexpensive sushi .\n->", + "output": "{\"text\": \"This is some really good , inexpensive sushi .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This restaurant was way overhyped .\n->This restaurant was way overhyped .\n[{'aspect': 'restaurant', 'opinion': 'overhyped', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: beware that stains in the coating of the display have been detected in all of the macbook retina editions .\n->beware that stains in the coating of the display have been detected in all of the macbook retina editions .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n->", + "output": "{\"text\": \"my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\", \"labels\": \"[{'aspect': 'bagel with lox spread', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagles', 'opinion': 'unbeliavably good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Cozy romantic atomosphere with only around 15 tables at most .\n->Cozy romantic atomosphere with only around 15 tables at most .\n[{'aspect': 'atomosphere', 'opinion': 'Cozy romantic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n->", + "output": "{\"text\": \"I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\", \"labels\": \"[{'aspect': 'quality of food', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been to roth ' s twice and both times were very disappointing .\n->i have been to roth ' s twice and both times were very disappointing .\n[{'aspect': \"roth ' s\", 'opinion': 'disappointing .', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i ' m still worried about the quality of capacitor and conductor inside this thing .\n->i ' m still worried about the quality of capacitor and conductor inside this thing .\n[{'aspect': 'capacitor', 'opinion': 'worried', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}, {'aspect': 'conductor', 'opinion': 'worried', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n->", + "output": "{\"text\": \"For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\", \"labels\": \"[{'aspect': 'lobby area', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: issue summary : frequent crashing\n->issue summary : frequent crashing\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - port minimalism .\n->- port minimalism .\n[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#PORTABILITY'}]\ntext: The pizza is delicious and the proprietor is one of the nicest in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza is delicious and the proprietor is one of the nicest in NYC .\n->", + "output": "{\"text\": \"The pizza is delicious and the proprietor is one of the nicest in NYC .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the style , aluminum shell , 14 inches monitor , and decent resolution .\n->love the style , aluminum shell , 14 inches monitor , and decent resolution .\n[{'aspect': 'style', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'aluminum shell', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '14 inches monitor', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: some apps don ' t play well yet , but should with time .\n->some apps don ' t play well yet , but should with time .\n[{'aspect': 'some apps', 'opinion': \"' t play well\", 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Also good for client lunch meetings , esp .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlso good for client lunch meetings , esp .\n->", + "output": "{\"text\": \"Also good for client lunch meetings , esp .\", \"labels\": \"[{'aspect': 'lunch meetings', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: hot / dead pixels on screen after 4 months use .\n->hot / dead pixels on screen after 4 months use .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n->", + "output": "{\"text\": \"The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'low', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"food 's presentation\", 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good , fast service .\n->Good , fast service .\n[{'aspect': 'service', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n->we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: The Waitstaff were very nice and suggested swordfish for my husband he enjoyed his meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Waitstaff were very nice and suggested swordfish for my husband he enjoyed his meal .\n->", + "output": "{\"text\": \"The Waitstaff were very nice and suggested swordfish for my husband he enjoyed his meal .\", \"labels\": \"[{'aspect': 'Waitstaff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'swordfish', 'opinion': 'suggested', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: running android apps themselves are a pretty ` ` meh ` ` experience .\n->running android apps themselves are a pretty ` ` meh ` ` experience .\n[{'aspect': 'android apps', 'opinion': 'meh', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: 8 + lbs , this one is right under 5 so it makes it nice and portable .\n->8 + lbs , this one is right under 5 so it makes it nice and portable .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: The setting is casual and romantic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe setting is casual and romantic .\n->", + "output": "{\"text\": \"The setting is casual and romantic .\", \"labels\": \"[{'aspect': 'setting', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: besides that it runs ok\n->besides that it runs ok\n[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: ( i had to check that the caps lock was off after typing that last word . )\n->( i had to check that the caps lock was off after typing that last word . )\n[{'aspect': 'caps lock', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Terrible , terrible management - deserves to be shut-down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTerrible , terrible management - deserves to be shut-down .\n->", + "output": "{\"text\": \"Terrible , terrible management - deserves to be shut-down .\", \"labels\": \"[{'aspect': 'management', 'opinion': 'Terrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was absolutely amazing ! !\n->The food was absolutely amazing ! !\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: try everything for that matter , it is all good .\n->try everything for that matter , it is all good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->", + "output": "{\"text\": \"Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing i ' m bummed about is the lack of google play .\n->the only thing i ' m bummed about is the lack of google play .\n[{'aspect': 'google play', 'opinion': 'lack', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: i opened the box after ordering on black friday , and the machine wouldn ' t charge .\n->i opened the box after ordering on black friday , and the machine wouldn ' t charge .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: The Italian food has flavor ( that can be sort of surprising on the UES ) , and the service turns from a nightmare to attentive , they sort of remind me of the NY Yankees of the late 90 's , no matter how bad it look , you knew that there was a rally just around the corner ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Italian food has flavor ( that can be sort of surprising on the UES ) , and the service turns from a nightmare to attentive , they sort of remind me of the NY Yankees of the late 90 's , no matter how bad it look , you knew that there was a rally just around the corner ...\n->", + "output": "{\"text\": \"The Italian food has flavor ( that can be sort of surprising on the UES ) , and the service turns from a nightmare to attentive , they sort of remind me of the NY Yankees of the late 90 's , no matter how bad it look , you knew that there was a rally just around the corner ...\", \"labels\": \"[{'aspect': 'Italian food', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the intel i3 processor simply flys .\n->the intel i3 processor simply flys .\n[{'aspect': 'intel i3 processor', 'opinion': 'flys', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: on the flip side , this asus is very fast with minimal bloatware that is easy to get rid of .\n->on the flip side , this asus is very fast with minimal bloatware that is easy to get rid of .\n[{'aspect': 'asus', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'asus', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Mizu is home to creative and unique rolls not to found anywhere else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMizu is home to creative and unique rolls not to found anywhere else .\n->", + "output": "{\"text\": \"Mizu is home to creative and unique rolls not to found anywhere else .\", \"labels\": \"[{'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the asus c302 is the best chromebook you can buy .\n->the asus c302 is the best chromebook you can buy .\n[{'aspect': 'asus c302', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n->The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n[{'aspect': 'thai food', 'opinion': 'better', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Baluchi 's has solid food and a nice decor at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBaluchi 's has solid food and a nice decor at reasonable prices .\n->", + "output": "{\"text\": \"Baluchi 's has solid food and a nice decor at reasonable prices .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with so many good restaurants on the uws , i do n ' t need overpriced food , absurdly arrogant wait - staff who do n ' t recognize they work at a glorified diner , clumsy service , and management that does n ' t care .\n->with so many good restaurants on the uws , i do n ' t need overpriced food , absurdly arrogant wait - staff who do n ' t recognize they work at a glorified diner , clumsy service , and management that does n ' t care .\n[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'wait - staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n->samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n[{'aspect': 'stylus', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'oem stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: Moules were excellent , lobster ravioli was VERY salty !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMoules were excellent , lobster ravioli was VERY salty !\n->", + "output": "{\"text\": \"Moules were excellent , lobster ravioli was VERY salty !\", \"labels\": \"[{'aspect': 'Moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great vibe , lots of people .\n->great vibe , lots of people .\n[{'aspect': 'vibe', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n->Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n[{'aspect': 'sushi place', 'opinion': 'Not the greatest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi place', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: A cheap eat for NYC , but not for dosa .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA cheap eat for NYC , but not for dosa .\n->", + "output": "{\"text\": \"A cheap eat for NYC , but not for dosa .\", \"labels\": \"[{'aspect': 'dosa', 'opinion': 'but', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'eat', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cute place , nice wait staff but would never go there again .\n->cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n->the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n[{'aspect': 'keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'back light', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n->", + "output": "{\"text\": \"The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'ever-changing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'great surprises', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not impressed with the food .\n->Not impressed with the food .\n[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n->we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n[{'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The food was just OK , at least for what food was available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was just OK , at least for what food was available .\n->", + "output": "{\"text\": \"The food was just OK , at least for what food was available .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'OK', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was not fresh , the sauces were bland and very oily .\n->The food was not fresh , the sauces were bland and very oily .\n[{'aspect': 'food', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n[{'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The waitress was very patient with us and the food is phenomenal !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waitress was very patient with us and the food is phenomenal !\n->", + "output": "{\"text\": \"The waitress was very patient with us and the food is phenomenal !\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'patient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nice atmosphere , the service was very pleasant and the desert was good .\n->Nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'Nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: cons hd display is n ' t the greatest\n->cons hd display is n ' t the greatest\n[{'aspect': 'hd display', 'opinion': 'cons', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'hd display', 'opinion': \"n ' t the greatest\", 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\ntext: The ambience was nice , but service was n't so great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ambience was nice , but service was n't so great .\n->", + "output": "{\"text\": \"The ambience was nice , but service was n't so great .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': \"was n't so great\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also can not just ` ` add ` ` memory ddms : only one socket means you must remove the 8gb and purchace a 16gb ( ~ $ 180 further investment ) ; battery not gon na last several hrs , looking at ~ 2 - 4 .\n->also can not just ` ` add ` ` memory ddms : only one socket means you must remove the 8gb and purchace a 16gb ( ~ $ 180 further investment ) ; battery not gon na last several hrs , looking at ~ 2 - 4 .\n[{'aspect': 'memory ddms', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n->small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Solid wine list , knowledgeable staff , friendly owners and an adventurous , ever-changing menu keep us coming back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSolid wine list , knowledgeable staff , friendly owners and an adventurous , ever-changing menu keep us coming back .\n->", + "output": "{\"text\": \"Solid wine list , knowledgeable staff , friendly owners and an adventurous , ever-changing menu keep us coming back .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'Solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'knowledgeable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owners', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'adventurous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'ever-changing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Much more reasonably priced too !\n->Much more reasonably priced too !\n[{'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe people that work there are always so friendly you forget you are in New York sometimes .\n->", + "output": "{\"text\": \"The people that work there are always so friendly you forget you are in New York sometimes .\", \"labels\": \"[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is good .\n->battery life is good .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: it look like a professional laptop but overrall get this laptop you wo n ' t regret it , no negativity about it\n->it look like a professional laptop but overrall get this laptop you wo n ' t regret it , no negativity about it\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The location and ambience is Ok but the food is what makes up for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe location and ambience is Ok but the food is what makes up for it .\n->", + "output": "{\"text\": \"The location and ambience is Ok but the food is what makes up for it .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'Ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'Ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'makes up', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n[{'aspect': 'appetizer selection', 'opinion': 'complaints', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i have been going there since it opened and i ca n ' t get enough .\n->i have been going there since it opened and i ca n ' t get enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: I love and I know gourmet food by excellence !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI love and I know gourmet food by excellence !\n->", + "output": "{\"text\": \"I love and I know gourmet food by excellence !\", \"labels\": \"[{'aspect': 'gourmet food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'gourmet food', 'opinion': 'excellence', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n->Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n[{'aspect': 'fruit of the oil', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'sweetness', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Always popular , always full , always a wait .\n->Always popular , always full , always a wait .\n[{'aspect': 'wait', 'opinion': 'always', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I plan to come here again and look forward to trying their assortment of bruschetta , panini 's ... ..\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI plan to come here again and look forward to trying their assortment of bruschetta , panini 's ... ..\n->", + "output": "{\"text\": \"I plan to come here again and look forward to trying their assortment of bruschetta , panini 's ... ..\", \"labels\": \"[{'aspect': 'bruschetta', 'opinion': 'look forward', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'panini', 'opinion': 'look forward', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far it ' s running smooth with no issues as if it was new .\n->so far it ' s running smooth with no issues as if it was new .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - the aluminum build is firm and solid without feeling cheap .\n->- the aluminum build is firm and solid without feeling cheap .\n[{'aspect': 'aluminum build', 'opinion': 'firm', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminum build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminum build', 'opinion': 'without feeling cheap', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTwo people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n->", + "output": "{\"text\": \"Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'regular', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n->the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: your friends will thank you for introducing them to this gem !\n->your friends will thank you for introducing them to this gem !\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: He offers subpar service and has no personality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHe offers subpar service and has no personality .\n->", + "output": "{\"text\": \"He offers subpar service and has no personality .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my boyfriend had prime rib it was good .\n->my boyfriend had prime rib it was good .\n[{'aspect': 'prime rib', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Great bagels , spreads and a good place to hang out in .\n->Great bagels , spreads and a good place to hang out in .\n[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spreads', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The waitress remembers me and is very friendly , she knows what my regular is and that 's the fried mini buns with the condensed milk and the assorted fruits on beancurd .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waitress remembers me and is very friendly , she knows what my regular is and that 's the fried mini buns with the condensed milk and the assorted fruits on beancurd .\n->", + "output": "{\"text\": \"The waitress remembers me and is very friendly , she knows what my regular is and that 's the fried mini buns with the condensed milk and the assorted fruits on beancurd .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried mini buns with the condensed milk and the assorted fruits on beancurd', 'opinion': 'regular', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice screen and keyboard , touch pad is great .\n->nice screen and keyboard , touch pad is great .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: The place was quiet and delightful .\n->The place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Diner food at bistro prices is a bummer ... .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDiner food at bistro prices is a bummer ... .\n->", + "output": "{\"text\": \"Diner food at bistro prices is a bummer ... .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is outstanding and the service is quick , friendly and very professional .\n->The food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food was great and tasty , but the sitting space was too small , i do n ' t like being cramp in a corner .\n->the food was great and tasty , but the sitting space was too small , i do n ' t like being cramp in a corner .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sitting space', 'opinion': 'too small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: While we thoroughly enjoyed the food , it was annoying to scream across the table for conversation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile we thoroughly enjoyed the food , it was annoying to scream across the table for conversation .\n->", + "output": "{\"text\": \"While we thoroughly enjoyed the food , it was annoying to scream across the table for conversation .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very good wine choices .\n->very good wine choices .\n[{'aspect': 'wine choices', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: anybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .\n->anybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .\n[{'aspect': 'NULL', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The portions are large and the servers always surprise us with a different starter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portions are large and the servers always surprise us with a different starter .\n->", + "output": "{\"text\": \"The portions are large and the servers always surprise us with a different starter .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'starter', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is great .\n->This place is great .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was wonderful ;\n->service was wonderful ;\n[{'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: delicious bagels , especially when right out of the oven .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicious bagels , especially when right out of the oven .\n->", + "output": "{\"text\": \"delicious bagels , especially when right out of the oven .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n->we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'gulab jamun ( dessert )', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: a fantastic product , with an aluminum frame , touchscreen , and high definition ; high resolution screen ; you can ' t beat this .\n->a fantastic product , with an aluminum frame , touchscreen , and high definition ; high resolution screen ; you can ' t beat this .\n[{'aspect': 'product', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'definition', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: This is one great place to eat pizza more out but not a good place for take-out pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is one great place to eat pizza more out but not a good place for take-out pizza .\n->", + "output": "{\"text\": \"This is one great place to eat pizza more out but not a good place for take-out pizza .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'take-out pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n->and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the battery life sucks as it starts to die after 3 - 4 hours of use ( no gaming ) .\n->the battery life sucks as it starts to die after 3 - 4 hours of use ( no gaming ) .\n[{'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Try green curry with vegetables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry green curry with vegetables .\n->", + "output": "{\"text\": \"Try green curry with vegetables .\", \"labels\": \"[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen looks great .\n->screen looks great .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n->and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n[{'aspect': 'system', 'opinion': 'not worry', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFirst of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->", + "output": "{\"text\": \"First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\", \"labels\": \"[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 'm partial to the Gnocchi .\n->I 'm partial to the Gnocchi .\n[{'aspect': 'Gnocchi', 'opinion': 'partial', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food is great and the environment is even better .\n->the food is great and the environment is even better .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'environment', 'opinion': 'better', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The second you walk through the heavy vault like door , with people anticipating your arrival you get the sense that you are going to have the dining ride of a lifetime .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe second you walk through the heavy vault like door , with people anticipating your arrival you get the sense that you are going to have the dining ride of a lifetime .\n->", + "output": "{\"text\": \"The second you walk through the heavy vault like door , with people anticipating your arrival you get the sense that you are going to have the dining ride of a lifetime .\", \"labels\": \"[{'aspect': 'door', 'opinion': 'heavy', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keys feel nice and responsive however the mouse pad is a little over responsive .\n->the keys feel nice and responsive however the mouse pad is a little over responsive .\n[{'aspect': 'keys', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keys', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'mouse pad', 'opinion': 'over responsive', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i eventually returned it .\n->i eventually returned it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Much more reasonably priced too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMuch more reasonably priced too !\n->", + "output": "{\"text\": \"Much more reasonably priced too !\", \"labels\": \"[{'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I/we will never go back to this place again .\n->I/we will never go back to this place again .\n[{'aspect': 'place', 'opinion': 'never go back', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: other than that i ' m pleased with the performance .\n->other than that i ' m pleased with the performance .\n[{'aspect': 'performance', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n->", + "output": "{\"text\": \"The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portioins', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen ' s color gamut is only 50 % ntsc , but as i don ' t perform image editing with it , this hasn ' t mattered .\n->the screen ' s color gamut is only 50 % ntsc , but as i don ' t perform image editing with it , this hasn ' t mattered .\n[{'aspect': \"screen ' s color gamut\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: mizu is home to creative and unique rolls not to found anywhere else .\n->mizu is home to creative and unique rolls not to found anywhere else .\n[{'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The Pad Thai is excellent here , as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Pad Thai is excellent here , as well .\n->", + "output": "{\"text\": \"The Pad Thai is excellent here , as well .\", \"labels\": \"[{'aspect': 'Pad Thai', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n->as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: screen looks good , it has a good battery life , keypad has some nice feedback , the works .\n->screen looks good , it has a good battery life , keypad has some nice feedback , the works .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: I do not recommend lunch specials just because it tasts the same with other regular chinese restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI do not recommend lunch specials just because it tasts the same with other regular chinese restaurant .\n->", + "output": "{\"text\": \"I do not recommend lunch specials just because it tasts the same with other regular chinese restaurant .\", \"labels\": \"[{'aspect': 'lunch specials', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n->Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n[{'aspect': 'food suggestions', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 2 ) the touchpad is way too wonky - asus needs to fix this asap .\n->2 ) the touchpad is way too wonky - asus needs to fix this asap .\n[{'aspect': 'touchpad', 'opinion': 'wonky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: We parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas .\n->", + "output": "{\"text\": \"We parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizzas', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing i moderately enjoyed was their grilled chicken special with edamame puree .\n->the only thing i moderately enjoyed was their grilled chicken special with edamame puree .\n[{'aspect': 'grilled chicken special with edamame puree', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The food is o.k. , but not any better than what you get at a good neighborhood restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is o.k. , but not any better than what you get at a good neighborhood restaurant .\n->", + "output": "{\"text\": \"The food is o.k. , but not any better than what you get at a good neighborhood restaurant .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'o.k. ,', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not any better', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , the problems are fairly minor and for the price i ' m happy with what i got .\n->overall , the problems are fairly minor and for the price i ' m happy with what i got .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n->in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'chrome os', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\ntext: We had the pot-stickers which were great and a tempura dish that was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had the pot-stickers which were great and a tempura dish that was great .\n->", + "output": "{\"text\": \"We had the pot-stickers which were great and a tempura dish that was great .\", \"labels\": \"[{'aspect': 'pot-stickers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tempura dish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n->Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n[{'aspect': 'wine', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: within two months , there were too many issues / bugs with the os .\n->within two months , there were too many issues / bugs with the os .\n[{'aspect': 'os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: I think the stuff was better than Disney .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI think the stuff was better than Disney .\n->", + "output": "{\"text\": \"I think the stuff was better than Disney .\", \"labels\": \"[{'aspect': 'stuff', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent packaging .\n->excellent packaging .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: Good food .\n->Good food .\n[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: While the $ 20 entree range is not overly expensive , in New York City , there is definitely better food in that range , and so Sapphire , despite it 's lovely atmosphere , will most likely not be a restaurant to which I will return .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the $ 20 entree range is not overly expensive , in New York City , there is definitely better food in that range , and so Sapphire , despite it 's lovely atmosphere , will most likely not be a restaurant to which I will return .\n->", + "output": "{\"text\": \"While the $ 20 entree range is not overly expensive , in New York City , there is definitely better food in that range , and so Sapphire , despite it 's lovely atmosphere , will most likely not be a restaurant to which I will return .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entree range', 'opinion': 'not overly expensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n->the pizza was delivered cold and the cheese was n ' t even fully melted !\n[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n->i recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n[{'aspect': 'jelly fish', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'drunken chicken', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'soupy dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'stir fry blue crab', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Orsay , is without a doubt one of the best values for authentic French food in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOrsay , is without a doubt one of the best values for authentic French food in NYC .\n->", + "output": "{\"text\": \"Orsay , is without a doubt one of the best values for authentic French food in NYC .\", \"labels\": \"[{'aspect': 'French food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n->my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n->one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n[{'aspect': 'chromeos', 'opinion': 'frustrate', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\ntext: Well , their deliveries take for ever and the food is usually cold .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWell , their deliveries take for ever and the food is usually cold .\n->", + "output": "{\"text\": \"Well , their deliveries take for ever and the food is usually cold .\", \"labels\": \"[{'aspect': 'deliveries', 'opinion': 'for ever', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the acer is similar but bigger and heavier .\n->the acer is similar but bigger and heavier .\n[{'aspect': 'acer', 'opinion': 'bigger', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'acer', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: if you like spicy food get the chicken vindaloo .\n->if you like spicy food get the chicken vindaloo .\n[{'aspect': 'chicken vindaloo', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: And really large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd really large portions .\n->", + "output": "{\"text\": \"And really large portions .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A very inviting restaurant , with friendly service .\n->A very inviting restaurant , with friendly service .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: well designed , nice fit and finish , and the build quality seems exceptional .\n->well designed , nice fit and finish , and the build quality seems exceptional .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'build quality', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: This is a great Thai restaurant with a very friendly staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a great Thai restaurant with a very friendly staff .\n->", + "output": "{\"text\": \"This is a great Thai restaurant with a very friendly staff .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The specials are usually quite good too .\n->The specials are usually quite good too .\n[{'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop has handled everything i have thrown at it .\n->this laptop has handled everything i have thrown at it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I ordered the smoked salmon and roe appetizer and it was off flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ordered the smoked salmon and roe appetizer and it was off flavor .\n->", + "output": "{\"text\": \"I ordered the smoked salmon and roe appetizer and it was off flavor .\", \"labels\": \"[{'aspect': 'smoked salmon and roe appetizer', 'opinion': 'off flavor', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the boot up is fast .\n->the boot up is fast .\n[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: had no flavor and the staff is rude and not attentive .\n->had no flavor and the staff is rude and not attentive .\n[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'not attentive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .\n->", + "output": "{\"text\": \"We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wonderful strawberry daiquiries as well !\n->wonderful strawberry daiquiries as well !\n[{'aspect': 'strawberry daiquiries', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: they loved them and said they worked perfectly .\n->they loved them and said they worked perfectly .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I recommend the garlic shrimp , okra ( bindi ) , and anything with lamb .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recommend the garlic shrimp , okra ( bindi ) , and anything with lamb .\n->", + "output": "{\"text\": \"I recommend the garlic shrimp , okra ( bindi ) , and anything with lamb .\", \"labels\": \"[{'aspect': 'garlic shrimp', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'okra ( bindi )', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The prices were CHEAP compared to the quality of service and food .\n->The prices were CHEAP compared to the quality of service and food .\n[{'aspect': 'prices', 'opinion': 'CHEAP', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n->i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n[{'aspect': 'apple support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: The food is great and authentic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is great and authentic .\n->", + "output": "{\"text\": \"The food is great and authentic .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n->they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: Everything was wonderful ; food , drinks , staff , mileau .\n->Everything was wonderful ; food , drinks , staff , mileau .\n[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mileau', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Good for casual dinner with jeans and sneakers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood for casual dinner with jeans and sneakers .\n->", + "output": "{\"text\": \"Good for casual dinner with jeans and sneakers .\", \"labels\": \"[{'aspect': 'casual dinner', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was good and food is wonderful .\n->Service was good and food is wonderful .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was terrific and the service classy , attentive , without being overbearing .\n->The food was terrific and the service classy , attentive , without being overbearing .\n[{'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'classy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'without being overbearing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Anyway , the food is good , the price is right and they have a decent wine list .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnyway , the food is good , the price is right and they have a decent wine list .\n->", + "output": "{\"text\": \"Anyway , the food is good , the price is right and they have a decent wine list .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n->i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n[{'aspect': 'keyboards', 'opinion': 'worst', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: This place has many different styles of pizza and they are all amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place has many different styles of pizza and they are all amazing .\n->", + "output": "{\"text\": \"This place has many different styles of pizza and they are all amazing .\", \"labels\": \"[{'aspect': 'styles of pizza', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'styles of pizza', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ( food was delivered by a busboy , not waiter ) We got no cheese offered for the pasta , our water and wine glasses remained EMPTY our entire meal , when we would have easily spent another $ 20 on wine .\n->( food was delivered by a busboy , not waiter ) We got no cheese offered for the pasta , our water and wine glasses remained EMPTY our entire meal , when we would have easily spent another $ 20 on wine .\n[{'aspect': 'water and wine glasses', 'opinion': 'EMPTY', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\n->The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'regular menu-fare', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'regular menu-fare', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was great .\n->", + "output": "{\"text\": \"The food was great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a little weird at first , knowing that the button isn ' t actually moving when you click down , but you definitely get used to it .\n->it ' s a little weird at first , knowing that the button isn ' t actually moving when you click down , but you definitely get used to it .\n[{'aspect': 'button', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i have to say that the keyboard is my favorite feature .\n->i have to say that the keyboard is my favorite feature .\n[{'aspect': 'keyboard', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: Pair you food with the excellent beers on tap or their well priced wine list .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPair you food with the excellent beers on tap or their well priced wine list .\n->", + "output": "{\"text\": \"Pair you food with the excellent beers on tap or their well priced wine list .\", \"labels\": \"[{'aspect': 'beers on tap', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Diner food at bistro prices is a bummer ... .\n->Diner food at bistro prices is a bummer ... .\n[{'aspect': 'food', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The staff is very attentive and we can almost always get a table .\n->The staff is very attentive and we can almost always get a table .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n->", + "output": "{\"text\": \"If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\", \"labels\": \"[{'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bottle minimun', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Horrible food and horrible service .\n->Horrible food and horrible service .\n[{'aspect': 'food', 'opinion': 'Horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Not only is the cuisine the best around , the service has always been attentive and charming .\n->Not only is the cuisine the best around , the service has always been attentive and charming .\n[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Metrazur has a beautiful spot overlooking the main terminal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMetrazur has a beautiful spot overlooking the main terminal .\n->", + "output": "{\"text\": \"Metrazur has a beautiful spot overlooking the main terminal .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: could have been a great computer if not for the terrible keyboard construction .\n->could have been a great computer if not for the terrible keyboard construction .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard construction', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: it is apparent the hard drive has failed yet again .\n->it is apparent the hard drive has failed yet again .\n[{'aspect': 'hard drive', 'opinion': 'failed', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: The restaurant is rather small but we were lucky to get a table quickly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe restaurant is rather small but we were lucky to get a table quickly .\n->", + "output": "{\"text\": \"The restaurant is rather small but we were lucky to get a table quickly .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * very weak wifi reception from the built - in antenna .\n->* very weak wifi reception from the built - in antenna .\n[{'aspect': 'wifi', 'opinion': 'weak', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: the salads are delicious , both refreshing and very spicy .\n->the salads are delicious , both refreshing and very spicy .\n[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nQuality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n->", + "output": "{\"text\": \"Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\", \"labels\": \"[{'aspect': 'Quality of food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is a great bargain .\n->This place is a great bargain .\n[{'aspect': 'place', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: And they have these home made potato chips at the bar that are the most delicious things in the world !\n->And they have these home made potato chips at the bar that are the most delicious things in the world !\n[{'aspect': 'potato chips', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n->", + "output": "{\"text\": \"The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\", \"labels\": \"[{'aspect': 'seating', 'opinion': 'drafty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'tight', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nha Trang , while being notorious for utter lack of comfort and decor , horribly slow wait staff and horribly quick meals , is one of the best vietnamese restaurants i 've ever been to . the pho is delicious and comes with very fresh vegtables .\n->Nha Trang , while being notorious for utter lack of comfort and decor , horribly slow wait staff and horribly quick meals , is one of the best vietnamese restaurants i 've ever been to . the pho is delicious and comes with very fresh vegtables .\n[{'aspect': 'comfort', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'horribly slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meals', 'opinion': 'horribly quick', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pho', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegtables', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The noise level was unbearable , conversation impossible .\n->The noise level was unbearable , conversation impossible .\n[{'aspect': 'noise level', 'opinion': 'unbearable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: But , they were too big for the bun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut , they were too big for the bun .\n->", + "output": "{\"text\": \"But , they were too big for the bun .\", \"labels\": \"[{'aspect': 'bun', 'opinion': 'too big', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n->but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the food was undercooked -the sauce watery , and the vegetables raw .\n->the food was undercooked -the sauce watery , and the vegetables raw .\n[{'aspect': 'food', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'raw', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The food is fresh , delicious , and reasonably priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is fresh , delicious , and reasonably priced .\n->", + "output": "{\"text\": \"The food is fresh , delicious , and reasonably priced .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , service was as plain as sesame crusted Salmon I had .\n->However , service was as plain as sesame crusted Salmon I had .\n[{'aspect': 'service', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'sesame crusted Salmon', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRaga stands out with an interesting fusion of French and Indian cooking .\n->", + "output": "{\"text\": \"Raga stands out with an interesting fusion of French and Indian cooking .\", \"labels\": \"[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cons hd display is n ' t the greatest\n->cons hd display is n ' t the greatest\n[{'aspect': 'hd display', 'opinion': 'cons', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'hd display', 'opinion': \"n ' t the greatest\", 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: Zero ambiance to boot .\n->Zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'Zero', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The bagels are fabulous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bagels are fabulous .\n->", + "output": "{\"text\": \"The bagels are fabulous .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i looove their eggplant pizza , as well as their pastas !\n->i looove their eggplant pizza , as well as their pastas !\n[{'aspect': 'eggplant pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I took my girlfriend there for her birthday last night and we had a relaxing , really good meal .\n->I took my girlfriend there for her birthday last night and we had a relaxing , really good meal .\n[{'aspect': 'meal', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n->", + "output": "{\"text\": \"Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\", \"labels\": \"[{'aspect': 'seating in the garden', 'opinion': 'lie', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'seats', 'opinion': 'not available', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - windows 10 ( do i really need to list the drawbacks of 10 ?\n->- windows 10 ( do i really need to list the drawbacks of 10 ?\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n->If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n[{'aspect': 'ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlthough they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n->", + "output": "{\"text\": \"Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'water', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not sure where the previous reviewer , lonk , dined , but saul is in a great neighborhood and has great food !\n->not sure where the previous reviewer , lonk , dined , but saul is in a great neighborhood and has great food !\n[{'aspect': 'neighborhood', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i bought this because it had a good price .\n->i bought this because it had a good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The food was very good , a great deal , and the place its self was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was very good , a great deal , and the place its self was great .\n->", + "output": "{\"text\": \"The food was very good , a great deal , and the place its self was great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i updated it to high sierra and it ' s running smoothly so far .\n->i updated it to high sierra and it ' s running smoothly so far .\n[{'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i bought this for a linux machine and it does that just great .\n->i bought this for a linux machine and it does that just great .\n[{'aspect': 'linux machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nProbably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n->", + "output": "{\"text\": \"Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'busier', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'seldom crowded', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n->The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n[{'aspect': 'thai food', 'opinion': 'better', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n->it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Their calzones are horrific , bad , vomit-inducing , YUCK .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir calzones are horrific , bad , vomit-inducing , YUCK .\n->", + "output": "{\"text\": \"Their calzones are horrific , bad , vomit-inducing , YUCK .\", \"labels\": \"[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'vomit-inducing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'YUCK', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n->even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n[{'aspect': 'touchpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food was good too .\n->the food was good too .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: It takes forever to get a drink and they almost always forget to bring something ( although they dont forget to charge you for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt takes forever to get a drink and they almost always forget to bring something ( although they dont forget to charge you for it .\n->", + "output": "{\"text\": \"It takes forever to get a drink and they almost always forget to bring something ( although they dont forget to charge you for it .\", \"labels\": \"[{'aspect': 'drink', 'opinion': 'forever', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Enjoyed a very nice Caesar Salad while my wife had arugula and goat cheese ... .both very tasty .\n->Enjoyed a very nice Caesar Salad while my wife had arugula and goat cheese ... .both very tasty .\n[{'aspect': 'Caesar Salad', 'opinion': 'Enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Caesar Salad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'arugula and goat cheese', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n->you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The place itself is beautiful the bar scene seems to be happening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place itself is beautiful the bar scene seems to be happening .\n->", + "output": "{\"text\": \"The place itself is beautiful the bar scene seems to be happening .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar scene', 'opinion': 'happening', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can even run time machine backups while the computer is sleeping now .\n->you can even run time machine backups while the computer is sleeping now .\n[{'aspect': 'machine backups', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n->although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Saturday , Nov. 6th I had a group from work come in with about 35 people and the staff was amazing to accomodate us .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSaturday , Nov. 6th I had a group from work come in with about 35 people and the staff was amazing to accomodate us .\n->", + "output": "{\"text\": \"Saturday , Nov. 6th I had a group from work come in with about 35 people and the staff was amazing to accomodate us .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n->they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: the only beverage we did receive was water in dirty glasses !\n->the only beverage we did receive was water in dirty glasses !\n[{'aspect': 'NULL', 'opinion': 'dirty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Good spreads , great beverage selections and bagels really tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood spreads , great beverage selections and bagels really tasty .\n->", + "output": "{\"text\": \"Good spreads , great beverage selections and bagels really tasty .\", \"labels\": \"[{'aspect': 'spreads', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beverage selections', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the machine is easy to use , snappy , and everything the reviewers say .\n->the machine is easy to use , snappy , and everything the reviewers say .\n[{'aspect': 'machine', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'machine', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Another plus is most of the entrees are approx .\n->Another plus is most of the entrees are approx .\n[{'aspect': 'entrees', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Also , top the meal with a delicious and perfect slice of tiramisu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlso , top the meal with a delicious and perfect slice of tiramisu .\n->", + "output": "{\"text\": \"Also , top the meal with a delicious and perfect slice of tiramisu .\", \"labels\": \"[{'aspect': 'tiramisu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tiramisu', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is delicious - from the specials to the regular menu - fare , the dishes are never a disappointment .\n->the food is delicious - from the specials to the regular menu - fare , the dishes are never a disappointment .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'specials', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'regular menu - fare', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n->Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n[{'aspect': 'Appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Check out the secret back room .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCheck out the secret back room .\n->", + "output": "{\"text\": \"Check out the secret back room .\", \"labels\": \"[{'aspect': 'secret back room', 'opinion': 'Check out', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there are no negatives to speak of .\n->there are no negatives to speak of .\n[{'aspect': 'NULL', 'opinion': 'no negatives', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i bought this for $ 389 on cyber monday 2017 .\n->i bought this for $ 389 on cyber monday 2017 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: It 's also attached to Angel 's Share , which is a cool , more romantic bar ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's also attached to Angel 's Share , which is a cool , more romantic bar ...\n->", + "output": "{\"text\": \"It 's also attached to Angel 's Share , which is a cool , more romantic bar ...\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is awful -- the last time I was there ( and I do mean the last time ) we were told that they needed our table so we would have to leave .\n->The service is awful -- the last time I was there ( and I do mean the last time ) we were told that they needed our table so we would have to leave .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Moules were excellent , lobster ravioli was VERY salty !\n->Moules were excellent , lobster ravioli was VERY salty !\n[{'aspect': 'Moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I am amazed by the poor reviews- I find this place to be standout Italian in an area flooded with Italian- great prices , great atmosphere , good service and a wonderful wine list .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI am amazed by the poor reviews- I find this place to be standout Italian in an area flooded with Italian- great prices , great atmosphere , good service and a wonderful wine list .\n->", + "output": "{\"text\": \"I am amazed by the poor reviews- I find this place to be standout Italian in an area flooded with Italian- great prices , great atmosphere , good service and a wonderful wine list .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is excellent , and i can get several days use before needing to plug in .\n->battery life is excellent , and i can get several days use before needing to plug in .\n[{'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keyboard is great , and it ' s backlit !\n->the keyboard is great , and it ' s backlit !\n[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: With the theater 2 blocks away we had a delicious meal in a beautiful room .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWith the theater 2 blocks away we had a delicious meal in a beautiful room .\n->", + "output": "{\"text\": \"With the theater 2 blocks away we had a delicious meal in a beautiful room .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'room', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i love the feel of a lighter os and can do many tasks using google / web based apps .\n->i love the feel of a lighter os and can do many tasks using google / web based apps .\n[{'aspect': 'os', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: The food is great and reasonably priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is great and reasonably priced .\n->", + "output": "{\"text\": \"The food is great and reasonably priced .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have never had a problem with service save a missing rice once .\n->have never had a problem with service save a missing rice once .\n[{'aspect': 'service', 'opinion': 'problem', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n->The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'not over-bearing or rushed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOver the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n->", + "output": "{\"text\": \"Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\", \"labels\": \"[{'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it stays very cool to the touch and the performance has really been amazing .\n->it stays very cool to the touch and the performance has really been amazing .\n[{'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: two stars because the current laptop i have works great .\n->two stars because the current laptop i have works great .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The restuarant itself is not large , but seems to have several round tables to accomodate large groups hoping to save a buck to eat authentic Taiwanese .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe restuarant itself is not large , but seems to have several round tables to accomodate large groups hoping to save a buck to eat authentic Taiwanese .\n->", + "output": "{\"text\": \"The restuarant itself is not large , but seems to have several round tables to accomodate large groups hoping to save a buck to eat authentic Taiwanese .\", \"labels\": \"[{'aspect': 'round tables', 'opinion': 'several', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Taiwanese', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n->all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n[{'aspect': 'desktop / application options', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n->even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n->", + "output": "{\"text\": \"My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\", \"labels\": \"[{'aspect': 'french fries', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But the thai is definitely not great -- bland and undistinguished .\n->But the thai is definitely not great -- bland and undistinguished .\n[{'aspect': 'thai', 'opinion': 'not great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'thai', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'thai', 'opinion': 'undistinguished', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n->it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: It is the type of place to run into old friends and have a late , raucous dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is the type of place to run into old friends and have a late , raucous dinner .\n->", + "output": "{\"text\": \"It is the type of place to run into old friends and have a late , raucous dinner .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'raucous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent price , i bought it for a beginner in art design field .\n->excellent price , i bought it for a beginner in art design field .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: - no audio in port .\n->- no audio in port .\n[{'aspect': 'audio in port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\n->", + "output": "{\"text\": \"The food is delicious - from the specials to the regular menu-fare , the dishes are never a disappointment .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'regular menu-fare', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'regular menu-fare', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n->Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Have eaten at Ginger House several times , and it 's always good .\n->Have eaten at Ginger House several times , and it 's always good .\n[{'aspect': 'Ginger House', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The combination of fresh tomato sauce , fresh mozz cheese , basil and the dough they make with imported flour , makes this is one of the better pizza 's in NY .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe combination of fresh tomato sauce , fresh mozz cheese , basil and the dough they make with imported flour , makes this is one of the better pizza 's in NY .\n->", + "output": "{\"text\": \"The combination of fresh tomato sauce , fresh mozz cheese , basil and the dough they make with imported flour , makes this is one of the better pizza 's in NY .\", \"labels\": \"[{'aspect': 'fresh tomato sauce', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozz cheese', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flour', 'opinion': 'imported', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard on this unit is actually quite nice .\n->the keyboard on this unit is actually quite nice .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: great laptop for a good price too !\n->great laptop for a good price too !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n->", + "output": "{\"text\": \"For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\", \"labels\": \"[{'aspect': 'Paneer Roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is spectacular , from the appitizers to the main course , and then of course the desserts , ( WOW ) you 'll need no more .\n->The food is spectacular , from the appitizers to the main course , and then of course the desserts , ( WOW ) you 'll need no more .\n[{'aspect': 'food', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appitizers', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'main course', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: If your favorite Chinese food is General Tao chicken , then this is NOT your place .\n->If your favorite Chinese food is General Tao chicken , then this is NOT your place .\n[{'aspect': 'General Tao chicken', 'opinion': 'favorite', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The food looked very appetizing and delicious since it came on a variety of fancy plates .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food looked very appetizing and delicious since it came on a variety of fancy plates .\n->", + "output": "{\"text\": \"The food looked very appetizing and delicious since it came on a variety of fancy plates .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'plates', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing you can do here is walk in and eat . . but planning an event , especially a small , intimate one , forget about it .\n->the only thing you can do here is walk in and eat . . but planning an event , especially a small , intimate one , forget about it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Great food , great decor , great service .\n->Great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBoth the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n->", + "output": "{\"text\": \"Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\", \"labels\": \"[{'aspect': 'fresh mozzerella slices', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozzerella slices', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Plain Cheese slice', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n->Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n[{'aspect': 'turnip soup with pureed basil', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I can not imagine better Indian food in all of the city .\n->I can not imagine better Indian food in all of the city .\n[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is terrible and overall , I would have to say avoid at all costs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is terrible and overall , I would have to say avoid at all costs .\n->", + "output": "{\"text\": \"The food is terrible and overall , I would have to say avoid at all costs .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place blew me away ... by far my new favorite restaurant on the uppereast side .\n->This place blew me away ... by far my new favorite restaurant on the uppereast side .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: highly impressed from the decor to the food to the hospitality to the great night i had !\n->highly impressed from the decor to the food to the hospitality to the great night i had !\n[{'aspect': 'decor', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'hospitality', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n->", + "output": "{\"text\": \"It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'surprisingly fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * decent selection of ports for its size\n->* decent selection of ports for its size\n[{'aspect': 'ports', 'opinion': 'decent', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\nExample:\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n->The people that work there are always so friendly you forget you are in New York sometimes .\n[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I went at 6:00 PM specifically for the pre-theater menu ( $ 19.95 for roasted tomato soup with chevre , steak frites , creme brulee ) and it was marvelous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI went at 6:00 PM specifically for the pre-theater menu ( $ 19.95 for roasted tomato soup with chevre , steak frites , creme brulee ) and it was marvelous .\n->", + "output": "{\"text\": \"I went at 6:00 PM specifically for the pre-theater menu ( $ 19.95 for roasted tomato soup with chevre , steak frites , creme brulee ) and it was marvelous .\", \"labels\": \"[{'aspect': 'pre-theater menu', 'opinion': 'marvelous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'roasted tomato soup with chevre', 'opinion': 'marvelous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak frites', 'opinion': 'marvelous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'creme brulee', 'opinion': 'marvelous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fish is so very fresh .\n->Fish is so very fresh .\n[{'aspect': 'Fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: touch screen is really responsive .\n->touch screen is really responsive .\n[{'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n->", + "output": "{\"text\": \"The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\", \"labels\": \"[{'aspect': 'waitstaff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'polite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we are very happy with the lenovo laptop .\n->we are very happy with the lenovo laptop .\n[{'aspect': 'lenovo laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n->update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n[{'aspect': 'laptop', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: - the bread at the beginning is super tasty and makes you want more - the pizza is delicious and comes in personal sizes , however be warned that the Peter 's Favourite pizza with prosciutto and baby arugula is actually a margarite pizza with cold prosciutto and baby arugula on top , like a salad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the bread at the beginning is super tasty and makes you want more - the pizza is delicious and comes in personal sizes , however be warned that the Peter 's Favourite pizza with prosciutto and baby arugula is actually a margarite pizza with cold prosciutto and baby arugula on top , like a salad .\n->", + "output": "{\"text\": \"- the bread at the beginning is super tasty and makes you want more - the pizza is delicious and comes in personal sizes , however be warned that the Peter 's Favourite pizza with prosciutto and baby arugula is actually a margarite pizza with cold prosciutto and baby arugula on top , like a salad .\", \"labels\": \"[{'aspect': 'bread', 'opinion': 'super tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is incredibly lightweight and slim ( ultimately won me over after years of lugging around a whopping 5lb levono ) .\n->this laptop is incredibly lightweight and slim ( ultimately won me over after years of lugging around a whopping 5lb levono ) .\n[{'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n->ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHave always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n->", + "output": "{\"text\": \"Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\", \"labels\": \"[{'aspect': 'waiters', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'busy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good food : my favorite is the seafood spaghetti .\n->good food : my favorite is the seafood spaghetti .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood spaghetti', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: While this can hardly be called a restaurant , it is possibly the best deal in Manhatten : $ 4 for a plate heaped with rice and 2-3 entrees .\n->While this can hardly be called a restaurant , it is possibly the best deal in Manhatten : $ 4 for a plate heaped with rice and 2-3 entrees .\n[{'aspect': 'rice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The owner truly caters to all your needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe owner truly caters to all your needs .\n->", + "output": "{\"text\": \"The owner truly caters to all your needs .\", \"labels\": \"[{'aspect': 'owner', 'opinion': 'caters', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my mom originally introduced me to this place , but even she ( being indian ) feels the food can be somewhat over the top spicy and far too oily .\n->my mom originally introduced me to this place , but even she ( being indian ) feels the food can be somewhat over the top spicy and far too oily .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: for the people who want great food plus great service , roxy is a place to avoid !\n->for the people who want great food plus great service , roxy is a place to avoid !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: He takes real pride in his food and his business .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHe takes real pride in his food and his business .\n->", + "output": "{\"text\": \"He takes real pride in his food and his business .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'pride', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good food .\n->Good food .\n[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this chromebook works like a dream .\n->this chromebook works like a dream .\n[{'aspect': 'chromebook', 'opinion': 'dream', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: So if you want a nice , enjoyable meal at Montparnasse , go early for the pre-theater prix-fixe .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSo if you want a nice , enjoyable meal at Montparnasse , go early for the pre-theater prix-fixe .\n->", + "output": "{\"text\": \"So if you want a nice , enjoyable meal at Montparnasse , go early for the pre-theater prix-fixe .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pre-theater prix-fixe', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pre-theater prix-fixe', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nothing on it feels cheap at all .\n->nothing on it feels cheap at all .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: very ` ` normal indian food ' ' , but done really well .\n->very ` ` normal indian food ' ' , but done really well .\n[{'aspect': 'indian food', 'opinion': 'normal', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'indian food', 'opinion': 'well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Over the years , it has always provided a pleasurable dining experience with quality food and wine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOver the years , it has always provided a pleasurable dining experience with quality food and wine .\n->", + "output": "{\"text\": \"Over the years , it has always provided a pleasurable dining experience with quality food and wine .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining', 'opinion': 'pleasurable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love it .\n->i love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: This place is always packed .\n->This place is always packed .\n[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The photobook menu was a cute touch , certainly helped my group and I pick the fried chicken , pork chop , and noodle dishes that we all ordered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe photobook menu was a cute touch , certainly helped my group and I pick the fried chicken , pork chop , and noodle dishes that we all ordered .\n->", + "output": "{\"text\": \"The photobook menu was a cute touch , certainly helped my group and I pick the fried chicken , pork chop , and noodle dishes that we all ordered .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s great other than the weak speakers and the touchpad .\n->it ' s great other than the weak speakers and the touchpad .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n->The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n[{'aspect': 'ambience', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I can not imagine better Indian food in all of the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI can not imagine better Indian food in all of the city .\n->", + "output": "{\"text\": \"I can not imagine better Indian food in all of the city .\", \"labels\": \"[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cons hd display is n ' t the greatest\n->cons hd display is n ' t the greatest\n[{'aspect': 'hd display', 'opinion': 'cons', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'hd display', 'opinion': \"n ' t the greatest\", 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: Chance is a small cozy restaurant , with a romantic feel to it , the decor is great .\n->Chance is a small cozy restaurant , with a romantic feel to it , the decor is great .\n[{'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very good breads as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good breads as well .\n->", + "output": "{\"text\": \"very good breads as well .\", \"labels\": \"[{'aspect': 'breads', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n->i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: suan is a great place that i often take my friends ( classmates ) too .\n->suan is a great place that i often take my friends ( classmates ) too .\n[{'aspect': 'suan', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Pizza is terrific , as is homemade pasta .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza is terrific , as is homemade pasta .\n->", + "output": "{\"text\": \"Pizza is terrific , as is homemade pasta .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n->i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n[{'aspect': 'beef cubes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is excellent .\n->the screen is excellent .\n[{'aspect': 'screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: This is a wonderful place on all stand points especially value ofr money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a wonderful place on all stand points especially value ofr money .\n->", + "output": "{\"text\": \"This is a wonderful place on all stand points especially value ofr money .\", \"labels\": \"[{'aspect': 'value ofr money', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best reuben sandwich ever !\n->best reuben sandwich ever !\n[{'aspect': 'reuben sandwich', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it has served me very well ever since .\n->it has served me very well ever since .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrom beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n->", + "output": "{\"text\": \"From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\", \"labels\": \"[{'aspect': 'beginning appetizers', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'scallops', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chocolate souffle with rasberry mint sorbet', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'taste', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after running through the setup wizard , the laptop failed to boot .\n->after running through the setup wizard , the laptop failed to boot .\n[{'aspect': 'laptop', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great Indian food and the service is incredible .\n->Great Indian food and the service is incredible .\n[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My goodness , everything from the fish to the rice to the seaweed was absolutely amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy goodness , everything from the fish to the rice to the seaweed was absolutely amazing .\n->", + "output": "{\"text\": \"My goodness , everything from the fish to the rice to the seaweed was absolutely amazing .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seaweed', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard and touchpad experience are pivotal for a device like this and what asus delivers is very satisfying .\n->the keyboard and touchpad experience are pivotal for a device like this and what asus delivers is very satisfying .\n[{'aspect': 'asus', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n->Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n[{'aspect': 'Chow fun', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pork shu mai', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The food is reliable and the price is moderate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is reliable and the price is moderate .\n->", + "output": "{\"text\": \"The food is reliable and the price is moderate .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the flip and touchscreen aspects work fine , no problems .\n->the flip and touchscreen aspects work fine , no problems .\n[{'aspect': 'flip', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Thius is a must for anyone who loves Shabu-Shabu .\n->Thius is a must for anyone who loves Shabu-Shabu .\n[{'aspect': 'Shabu-Shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'NULL'}]\ntext: While the ambiance and atmosphere were great , the food and service could have been a lot better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the ambiance and atmosphere were great , the food and service could have been a lot better .\n->", + "output": "{\"text\": \"While the ambiance and atmosphere were great , the food and service could have been a lot better .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i attended a holiday dinner at the restaurant , and the food was majorly disappointing .\n->i attended a holiday dinner at the restaurant , and the food was majorly disappointing .\n[{'aspect': 'food', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: over the years the host , vittorio , and his crew , have always treated me as family - - although with all the business this not - so - little gem does , it amazing he ' s even able to remember a consistent but not - so - frequent visitor .\n->over the years the host , vittorio , and his crew , have always treated me as family - - although with all the business this not - so - little gem does , it amazing he ' s even able to remember a consistent but not - so - frequent visitor .\n[{'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The server was really cool and served us our food and drinks with a smile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe server was really cool and served us our food and drinks with a smile .\n->", + "output": "{\"text\": \"The server was really cool and served us our food and drinks with a smile .\", \"labels\": \"[{'aspect': 'server', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this one !\n->i love this one !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: of course , i have only had this acer for a week .\n->of course , i have only had this acer for a week .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPlanet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n->", + "output": "{\"text\": \"Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n->The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n[{'aspect': 'miso soup', 'opinion': 'lacked flavor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'unfortunately', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n->its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The sauce on the pizza is sooo good with garlic and fresh tomatoes and they do n't skimp .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sauce on the pizza is sooo good with garlic and fresh tomatoes and they do n't skimp .\n->", + "output": "{\"text\": \"The sauce on the pizza is sooo good with garlic and fresh tomatoes and they do n't skimp .\", \"labels\": \"[{'aspect': 'fresh tomatoes', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sauce on the pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n->", + "output": "{\"text\": \"We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\", \"labels\": \"[{'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is uniformly exceptional , with a very capable kitchen which will proudly whip up whatever you feel like eating , whether it 's on the menu or not .\n->The food is uniformly exceptional , with a very capable kitchen which will proudly whip up whatever you feel like eating , whether it 's on the menu or not .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kitchen', 'opinion': 'capable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: staff is very accomodating .\n->staff is very accomodating .\n[{'aspect': 'staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n->", + "output": "{\"text\": \"My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\", \"labels\": \"[{'aspect': 'mesclun', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ice cream', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'courses', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s an easy one for me : the 302 offers a better experience overall and it ' s not close .\n->it ' s an easy one for me : the 302 offers a better experience overall and it ' s not close .\n[{'aspect': '302', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': '302', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n->My suggestion is to eat family style because you 'll want to try the other dishes .\n[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is usually good but it certainly is n't a relaxing place to go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is usually good but it certainly is n't a relaxing place to go .\n->", + "output": "{\"text\": \"The food is usually good but it certainly is n't a relaxing place to go .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': \"is n't a relaxing\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Decor is nice and minimalist , food simple yet very well presented and cooked , and the wine list matches the food very well .\n->Decor is nice and minimalist , food simple yet very well presented and cooked , and the wine list matches the food very well .\n[{'aspect': 'Decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Decor', 'opinion': 'minimalist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'simple', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'well presented and cooked', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what more could you want ?\n->what more could you want ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Super friendly and knowledgable staff , fabulous bistro fare and a wonderful jazz brunch with great live jazz ( the chilaquiles were awesome !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSuper friendly and knowledgable staff , fabulous bistro fare and a wonderful jazz brunch with great live jazz ( the chilaquiles were awesome !\n->", + "output": "{\"text\": \"Super friendly and knowledgable staff , fabulous bistro fare and a wonderful jazz brunch with great live jazz ( the chilaquiles were awesome !\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bistro fare', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chilaquiles', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'jazz brunch', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'live jazz', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but overall a good laptop for productivity .\n->but overall a good laptop for productivity .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i use my chromebook a lot in the dark and it ' s a real treat .\n->i use my chromebook a lot in the dark and it ' s a real treat .\n[{'aspect': 'chromebook', 'opinion': 'treat', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: The prices were fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe prices were fantastic .\n->", + "output": "{\"text\": \"The prices were fantastic .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Only drawback - they wo n't toast your bagel , and they do n't make eggs for the bagel .\n->Only drawback - they wo n't toast your bagel , and they do n't make eggs for the bagel .\n[{'aspect': 'bagel', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i love the way it looks and feels the only thing i can complain about is the fact that none of these electronics come with printed manuels anymore also dont know anything about the camera only see me .\n->i love the way it looks and feels the only thing i can complain about is the fact that none of these electronics come with printed manuels anymore also dont know anything about the camera only see me .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'camera', 'opinion': 'complain', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#USABILITY'}]\ntext: The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n->", + "output": "{\"text\": \"The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'busy', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I love and I know gourmet food by excellence !\n->I love and I know gourmet food by excellence !\n[{'aspect': 'gourmet food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'gourmet food', 'opinion': 'excellence', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n->my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: It 's not mind-blowing , but to me , thai food never is and never will be .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's not mind-blowing , but to me , thai food never is and never will be .\n->", + "output": "{\"text\": \"It 's not mind-blowing , but to me , thai food never is and never will be .\", \"labels\": \"[{'aspect': 'thai food', 'opinion': 'mind-blowing', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n->having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n[{'aspect': 'win 8', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: looks brand new and the battery life lasts a long time ( see photos )\n->looks brand new and the battery life lasts a long time ( see photos )\n[{'aspect': 'NULL', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n->", + "output": "{\"text\": \"Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork belly', 'opinion': 'melt-in-your-mouth', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fat', 'opinion': 'longer', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i go and eat out at many different restaurants and this is one place you have go and try .\n->i go and eat out at many different restaurants and this is one place you have go and try .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: screen color is excellent .\n->screen color is excellent .\n[{'aspect': 'screen color', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: I love the fact that the pizza tastes so good and is so cheap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI love the fact that the pizza tastes so good and is so cheap .\n->", + "output": "{\"text\": \"I love the fact that the pizza tastes so good and is so cheap .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great sushi experience .\n->Great sushi experience .\n[{'aspect': 'sushi', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n->however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n[{'aspect': 'kimchee', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'slimy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'korean fair', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Service was prompt , friendly and great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was prompt , friendly and great .\n->", + "output": "{\"text\": \"Service was prompt , friendly and great .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Drinks got screwed up , she acted put upon .\n->Drinks got screwed up , she acted put upon .\n[{'aspect': 'Drinks', 'opinion': 'screwed up', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: I can not imagine a friendlier staff working in a restaurant .\n->I can not imagine a friendlier staff working in a restaurant .\n[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n->", + "output": "{\"text\": \"I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\", \"labels\": \"[{'aspect': 'lamb chop', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n->the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n[{'aspect': 'ambience', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: i really love the laptop !\n->i really love the laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The sushi was awful !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sushi was awful !\n->", + "output": "{\"text\": \"The sushi was awful !\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Please if your thinking about it go , and stay the wait you wo n't be disappointed .\n->Please if your thinking about it go , and stay the wait you wo n't be disappointed .\n[{'aspect': 'wait', 'opinion': \"wo n't be disappointed\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The portions are large and the servers always surprise us with a different starter .\n->The portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: You can get an excellent meal at most of the many Indian restaurants on nearby Lexington Avenue for the cost of one the dainty dishes here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou can get an excellent meal at most of the many Indian restaurants on nearby Lexington Avenue for the cost of one the dainty dishes here .\n->", + "output": "{\"text\": \"You can get an excellent meal at most of the many Indian restaurants on nearby Lexington Avenue for the cost of one the dainty dishes here .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'dainty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambiance -- relaxed and stylish .\n->Ambiance -- relaxed and stylish .\n[{'aspect': 'Ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: package arrived faster than the estimated arrival .\n->package arrived faster than the estimated arrival .\n[{'aspect': 'package', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\ntext: The plain pizza was soggy and the creative wild mushroom ( third generation-Fornini ) pizza we had was drenched with truffle oil in the middle ( again making it soggy ) and nothingon the rest .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe plain pizza was soggy and the creative wild mushroom ( third generation-Fornini ) pizza we had was drenched with truffle oil in the middle ( again making it soggy ) and nothingon the rest .\n->", + "output": "{\"text\": \"The plain pizza was soggy and the creative wild mushroom ( third generation-Fornini ) pizza we had was drenched with truffle oil in the middle ( again making it soggy ) and nothingon the rest .\", \"labels\": \"[{'aspect': 'plain pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'truffle oil', 'opinion': 'drenched', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n->my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n[{'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: he ' s very happy with it .\n->he ' s very happy with it .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Late nite omelletes are not good here , there is no variety !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLate nite omelletes are not good here , there is no variety !\n->", + "output": "{\"text\": \"Late nite omelletes are not good here , there is no variety !\", \"labels\": \"[{'aspect': 'omelletes', 'opinion': 'not good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n->Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n[{'aspect': 'dishes', 'opinion': 'sake-friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: cpu and gpu are good , ram is good and i like the keyboard .\n->cpu and gpu are good , ram is good and i like the keyboard .\n[{'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'gpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'GRAPHICS#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: After all that , they complained to me about the small tip .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAfter all that , they complained to me about the small tip .\n->", + "output": "{\"text\": \"After all that , they complained to me about the small tip .\", \"labels\": \"[{'aspect': 'tip', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the flavors are amazing and the value is phenomenal .\n->the flavors are amazing and the value is phenomenal .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: would not go back .\n->would not go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: It 's a great place to pick up a cheap lunch or dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's a great place to pick up a cheap lunch or dinner .\n->", + "output": "{\"text\": \"It 's a great place to pick up a cheap lunch or dinner .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all around , a very lacking laptop .\n->all around , a very lacking laptop .\n[{'aspect': 'laptop', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: although lightweight and less expensive , the lack of sensitivity of the touchpad makes using this with ease kind of frustrating .\n->although lightweight and less expensive , the lack of sensitivity of the touchpad makes using this with ease kind of frustrating .\n[{'aspect': 'touchpad', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'touchpad', 'opinion': 'less expensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'touchpad', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n->", + "output": "{\"text\": \"Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is a great laptop !\n->it is a great laptop !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n->However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Our waitress was sweet and accomodating , not overbearing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur waitress was sweet and accomodating , not overbearing .\n->", + "output": "{\"text\": \"Our waitress was sweet and accomodating , not overbearing .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The beverages were excellent , and the dessert was good .\n->The beverages were excellent , and the dessert was good .\n[{'aspect': 'beverages', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The hostess is rude to the point of being offensive .\n->The hostess is rude to the point of being offensive .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I was really disappointed ant wanted to tell everyone not to go eat or even take out food from there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI was really disappointed ant wanted to tell everyone not to go eat or even take out food from there .\n->", + "output": "{\"text\": \"I was really disappointed ant wanted to tell everyone not to go eat or even take out food from there .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i generally like this place .\n->i generally like this place .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: yet , compare with other flip - style of chromebooks , the screen is sufficient for me to do my code .\n->yet , compare with other flip - style of chromebooks , the screen is sufficient for me to do my code .\n[{'aspect': 'screen', 'opinion': 'sufficient', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDespite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n->", + "output": "{\"text\": \"Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food art', 'opinion': 'ultra fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our experience did not ever get any better .\n->our experience did not ever get any better .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n->for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\ntext: The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n->", + "output": "{\"text\": \"The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\", \"labels\": \"[{'aspect': 'outdoor atmosphere', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this does exactly what i need , writing on google docs .\n->this does exactly what i need , writing on google docs .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: but just at first glance , this thing is top quality .\n->but just at first glance , this thing is top quality .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: brick oven gallery is My pick for best pizza restaurant anywhere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbrick oven gallery is My pick for best pizza restaurant anywhere .\n->", + "output": "{\"text\": \"brick oven gallery is My pick for best pizza restaurant anywhere .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The atmosphere is unheralded , the service impeccable , and the food magnificant .\n->The atmosphere is unheralded , the service impeccable , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the side dishes were passable , and i did get a refill upon request .\n->the side dishes were passable , and i did get a refill upon request .\n[{'aspect': 'side dishes', 'opinion': 'passable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAdd to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n->", + "output": "{\"text\": \"Add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice little computer for the price .\n->nice little computer for the price .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: do your research , learn how to optimize your experience , and you ' ll love it !\n->do your research , learn how to optimize your experience , and you ' ll love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n->", + "output": "{\"text\": \"For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: loses wifi connection every hour .\n->loses wifi connection every hour .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: both forward and rear facing cameras would be nice too .\n->both forward and rear facing cameras would be nice too .\n[{'aspect': 'cameras', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: Oh , do n't even let me start with how expensive the bills were !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOh , do n't even let me start with how expensive the bills were !\n->", + "output": "{\"text\": \"Oh , do n't even let me start with how expensive the bills were !\", \"labels\": \"[{'aspect': 'bills', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen is bright , doesn ' t feel heavy .\n->screen is bright , doesn ' t feel heavy .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': \"' t feel heavy\", 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Should you happen to be impressed by the cuisine definitely try it .\n->Should you happen to be impressed by the cuisine definitely try it .\n[{'aspect': 'cuisine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n->", + "output": "{\"text\": \"All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\", \"labels\": \"[{'aspect': 'appetizers', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'watering', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the perfect date spot for Williamsburg couples .\n->This is the perfect date spot for Williamsburg couples .\n[{'aspect': 'date spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This is the best sushi in new york city - hands down .\n->This is the best sushi in new york city - hands down .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Besides having the table we had been promised given to other restaurant patrons twice before we were actually seated , we were served dishes we had n't ordered three times , received one of our orders 20 minutes after the rest of the table had been served ( and that order was undercooked ) , and charged $ 45 more than we should have been on our bill .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBesides having the table we had been promised given to other restaurant patrons twice before we were actually seated , we were served dishes we had n't ordered three times , received one of our orders 20 minutes after the rest of the table had been served ( and that order was undercooked ) , and charged $ 45 more than we should have been on our bill .\n->", + "output": "{\"text\": \"Besides having the table we had been promised given to other restaurant patrons twice before we were actually seated , we were served dishes we had n't ordered three times , received one of our orders 20 minutes after the rest of the table had been served ( and that order was undercooked ) , and charged $ 45 more than we should have been on our bill .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n->but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n[{'aspect': 'battery', 'opinion': 'erroneous', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'way too sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'cover / lid', 'opinion': 'cheaply', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the actual laptop is very much darker and blue .\n->the actual laptop is very much darker and blue .\n[{'aspect': 'actual laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Service is top notch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is top notch .\n->", + "output": "{\"text\": \"Service is top notch .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nothing fancy but really good food with pretty reasonable price .\n->Nothing fancy but really good food with pretty reasonable price .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i can plug it in and put it back to sleep and it will charge , but if i haven ' t woken it up it won ' t begin to charge .\n->i can plug it in and put it back to sleep and it will charge , but if i haven ' t woken it up it won ' t begin to charge .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The food now is inconsistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food now is inconsistent .\n->", + "output": "{\"text\": \"The food now is inconsistent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n->We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: They might be all business at the counter when you give your order , but their food says I love you .\n->They might be all business at the counter when you give your order , but their food says I love you .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'counter', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}]\ntext: BE CAREFUL before you request extra spice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBE CAREFUL before you request extra spice .\n->", + "output": "{\"text\": \"BE CAREFUL before you request extra spice .\", \"labels\": \"[{'aspect': 'spice', 'opinion': 'CAREFUL', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really loved the different and inovated touch that ' s the cheff gives to the food .\n->i really loved the different and inovated touch that ' s the cheff gives to the food .\n[{'aspect': 'cheff', 'opinion': 'loved', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheff', 'opinion': 'inovated', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: and it was quick which is very important .\n->and it was quick which is very important .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'important', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The octopus eaters were floored by the Octopus salad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe octopus eaters were floored by the Octopus salad .\n->", + "output": "{\"text\": \"The octopus eaters were floored by the Octopus salad .\", \"labels\": \"[{'aspect': 'Octopus salad', 'opinion': 'floored', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n->Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n[{'aspect': 'Wait staff', 'opinion': 'unappreciative', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the surface texture of the case is a satiny paper - like touch , which is both clean and easy to wipe down , but also not so smooth that you have to be concerned with it slipping out of your hand when carrying it .\n->the surface texture of the case is a satiny paper - like touch , which is both clean and easy to wipe down , but also not so smooth that you have to be concerned with it slipping out of your hand when carrying it .\n[{'aspect': 'surface texture of the case', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'surface texture of the case', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: We thought that this place is using too much of MSG cooking in the foods .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe thought that this place is using too much of MSG cooking in the foods .\n->", + "output": "{\"text\": \"We thought that this place is using too much of MSG cooking in the foods .\", \"labels\": \"[{'aspect': 'MSG cooking', 'opinion': 'too much', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the flip and touchscreen aspects work fine , no problems .\n->the flip and touchscreen aspects work fine , no problems .\n[{'aspect': 'flip', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is usually good but it certainly is n't a relaxing place to go .\n->The food is usually good but it certainly is n't a relaxing place to go .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': \"is n't a relaxing\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: And the Tom Kha soup was pathetic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd the Tom Kha soup was pathetic .\n->", + "output": "{\"text\": \"And the Tom Kha soup was pathetic .\", \"labels\": \"[{'aspect': 'Tom Kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ended up returning it .\n->i ended up returning it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: stop working the macbook pro\n->stop working the macbook pro\n[{'aspect': 'macbook pro', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: I started out with a Bombay beer which was big enough for two .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI started out with a Bombay beer which was big enough for two .\n->", + "output": "{\"text\": \"I started out with a Bombay beer which was big enough for two .\", \"labels\": \"[{'aspect': 'Bombay beer', 'opinion': 'big', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Metrazur has a beautiful spot overlooking the main terminal .\n->Metrazur has a beautiful spot overlooking the main terminal .\n[{'aspect': 'spot', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this was exactly what i needed and it performs as new which is exactly what i expected as well .\n->this was exactly what i needed and it performs as new which is exactly what i expected as well .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n->", + "output": "{\"text\": \"The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining hall', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first , it is hard to run more than 10 tabs open at any given time .\n->first , it is hard to run more than 10 tabs open at any given time .\n[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food here does a great service to the name ( cantonese that is . . . ) .\n->the food here does a great service to the name ( cantonese that is . . . ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The sweet lassi was excellent as was the lamb chettinad and the garlic naan but the rasamalai was forgettable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sweet lassi was excellent as was the lamb chettinad and the garlic naan but the rasamalai was forgettable .\n->", + "output": "{\"text\": \"The sweet lassi was excellent as was the lamb chettinad and the garlic naan but the rasamalai was forgettable .\", \"labels\": \"[{'aspect': 'sweet lassi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rasamalai', 'opinion': 'forgettable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing i don ' t like is that the power button sits beside the delete key .\n->the only thing i don ' t like is that the power button sits beside the delete key .\n[{'aspect': 'power button', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: amazing laptop !\n->amazing laptop !\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: We 've been following chef Lyle 's food around New York for 15 years and while remaining classic , his innovations with bistro fare have made us return and return .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe 've been following chef Lyle 's food around New York for 15 years and while remaining classic , his innovations with bistro fare have made us return and return .\n->", + "output": "{\"text\": \"We 've been following chef Lyle 's food around New York for 15 years and while remaining classic , his innovations with bistro fare have made us return and return .\", \"labels\": \"[{'aspect': 'bistro fare', 'opinion': 'innovations', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'classic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Orsay , is a very pleasant throw back to traditional French food , and French service as well .\n->Orsay , is a very pleasant throw back to traditional French food , and French service as well .\n[{'aspect': 'French food', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'French food', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was slow had to wait to order and get food although not crowded .\n->service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The best thing I tasted were the lambchops .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe best thing I tasted were the lambchops .\n->", + "output": "{\"text\": \"The best thing I tasted were the lambchops .\", \"labels\": \"[{'aspect': 'lambchops', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n->the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n[{'aspect': 'touchpad', 'opinion': 'sensitive', 'polarity': 'neutral', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: they are clearly working with more than one person at a time , and not effective multi - taskers .\n->they are clearly working with more than one person at a time , and not effective multi - taskers .\n[{'aspect': 'NULL', 'opinion': 'not effective', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: In addition , the food is very good and the prices are reasonable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn addition , the food is very good and the prices are reasonable .\n->", + "output": "{\"text\": \"In addition , the food is very good and the prices are reasonable .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a real dissapointment .\n->a real dissapointment .\n[{'aspect': 'NULL', 'opinion': 'dissapointment', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: so the audio can easily be muffled .\n->so the audio can easily be muffled .\n[{'aspect': 'audio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: Upon entering , we were greeted by the owners , Steven and Frederick , who went out of their way to be more than gracious hosts .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nUpon entering , we were greeted by the owners , Steven and Frederick , who went out of their way to be more than gracious hosts .\n->", + "output": "{\"text\": \"Upon entering , we were greeted by the owners , Steven and Frederick , who went out of their way to be more than gracious hosts .\", \"labels\": \"[{'aspect': 'hosts', 'opinion': 'gracious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fish was overdone .\n->fish was overdone .\n[{'aspect': 'fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n->this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n[{'aspect': 'runner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: Been going here since it opened have seen the quality value decrease considerably .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBeen going here since it opened have seen the quality value decrease considerably .\n->", + "output": "{\"text\": \"Been going here since it opened have seen the quality value decrease considerably .\", \"labels\": \"[{'aspect': 'quality value', 'opinion': 'decrease', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a thai restaurant out of rice during dinner ?\n->a thai restaurant out of rice during dinner ?\n[{'aspect': 'thai restaurant', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the sandwiches are dry , tasteless and way overpriced .\n->the sandwiches are dry , tasteless and way overpriced .\n[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: they bring service up a notch by offerng complementary amuse bouche to all tables and gave us a small dessert for our celebration .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey bring service up a notch by offerng complementary amuse bouche to all tables and gave us a small dessert for our celebration .\n->", + "output": "{\"text\": \"they bring service up a notch by offerng complementary amuse bouche to all tables and gave us a small dessert for our celebration .\", \"labels\": \"[{'aspect': 'amuse bouche', 'opinion': 'complementary', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer completely shut down after 2 months .\n->this computer completely shut down after 2 months .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: however , the 1 - star review is due to the advertising with the computer .\n->however , the 1 - star review is due to the advertising with the computer .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Try the crunchy tuna , it is to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the crunchy tuna , it is to die for .\n->", + "output": "{\"text\": \"Try the crunchy tuna , it is to die for .\", \"labels\": \"[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try ordering from the regular menu , then you would not regret !\n->Try ordering from the regular menu , then you would not regret !\n[{'aspect': 'menu', 'opinion': 'regret', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: conveniently located too , being right on bedford ave .\n->conveniently located too , being right on bedford ave .\n[{'aspect': 'NULL', 'opinion': 'conveniently', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: All in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n->", + "output": "{\"text\": \"All in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food now is inconsistent .\n->the food now is inconsistent .\n[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: their tuna tartar appetizer is to die for .\n->their tuna tartar appetizer is to die for .\n[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: It is a lot of fun with live entertainment and all kinds of Disney type special effects .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is a lot of fun with live entertainment and all kinds of Disney type special effects .\n->", + "output": "{\"text\": \"It is a lot of fun with live entertainment and all kinds of Disney type special effects .\", \"labels\": \"[{'aspect': 'live entertainment', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'special effects', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All the NYU students love this place so it makes for a fun young atmosphere .\n->All the NYU students love this place so it makes for a fun young atmosphere .\n[{'aspect': 'atmosphere', 'opinion': 'fun young', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was exceptional .\n->The food was exceptional .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n->", + "output": "{\"text\": \"if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n->the hot dogs are top notch , and they ' re slamwich is amazing !\n[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: touch pad is a + + .\n->touch pad is a + + .\n[{'aspect': 'touch pad', 'opinion': 'a +', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: Be careful of portions - they 're HUGE .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBe careful of portions - they 're HUGE .\n->", + "output": "{\"text\": \"Be careful of portions - they 're HUGE .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i never write on these sites but this restaurant is def worth commending !\n->i never write on these sites but this restaurant is def worth commending !\n[{'aspect': 'restaurant', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Have eaten at Ginger House several times , and it 's always good .\n->Have eaten at Ginger House several times , and it 's always good .\n[{'aspect': 'Ginger House', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Steak Tartare is a great bet , they fix it for you at the table .\n->", + "output": "{\"text\": \"The Steak Tartare is a great bet , they fix it for you at the table .\", \"labels\": \"[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n->calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'room', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'clerks', 'opinion': 'unhelpful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - the speakers are to the sides and not underneath so the sound isn ' t muffled when it ' s resting on something other than a flat surface\n->- the speakers are to the sides and not underneath so the sound isn ' t muffled when it ' s resting on something other than a flat surface\n[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\ntext: Great for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening .\n->", + "output": "{\"text\": \"Great for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'SUPER HAPPY', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n->a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n[{'aspect': 'chrome os devices', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: works great !\n->works great !\n[{'aspect': 'works', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Plus , on Wednesday nights the house wine is unlimited !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPlus , on Wednesday nights the house wine is unlimited !\n->", + "output": "{\"text\": \"Plus , on Wednesday nights the house wine is unlimited !\", \"labels\": \"[{'aspect': 'house wine', 'opinion': 'unlimited', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagels are also reasonably priced for NYC .\n->The bagels are also reasonably priced for NYC .\n[{'aspect': 'bagels', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\n->Their bagels are fine , but they are a little overcooked , and not really a 'special ' bagel experience .\n[{'aspect': 'bagels', 'opinion': 'fine', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n->", + "output": "{\"text\": \"However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\", \"labels\": \"[{'aspect': 'management', 'opinion': 'changed', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'door', 'opinion': 'great big', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was looking for banana tempura for dessert and they dont have .\n->i was looking for banana tempura for dessert and they dont have .\n[{'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: while the chromebook pro does have a nice high - resolution 3 : 2 screen and the s - pen ( which is truly a gimmick anyway ) , it has half the storage , shorter battery life , and no backlit keyboard .\n->while the chromebook pro does have a nice high - resolution 3 : 2 screen and the s - pen ( which is truly a gimmick anyway ) , it has half the storage , shorter battery life , and no backlit keyboard .\n[{'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: We wo n't go to this place again for a good meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe wo n't go to this place again for a good meal .\n->", + "output": "{\"text\": \"We wo n't go to this place again for a good meal .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only problem is that there is no caps lock button on the keyboard .\n->the only problem is that there is no caps lock button on the keyboard .\n[{'aspect': 'caps lock button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: battery lasts for quite sometime .\n->battery lasts for quite sometime .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBarbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n->", + "output": "{\"text\": \"Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\", \"labels\": \"[{'aspect': 'Barbecued codfish', 'opinion': 'moist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seasoning', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spice rub', 'opinion': 'overwhelmed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'herb mix', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will say , however , that the screen is kind of awful .\n->i will say , however , that the screen is kind of awful .\n[{'aspect': 'screen', 'opinion': 'awful', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: The service is ok , some of the people did n't get what they asked for .\n->The service is ok , some of the people did n't get what they asked for .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: After dinner , take your date to the HUGE dance floor , probably one of the biggest you 'll see in NY .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAfter dinner , take your date to the HUGE dance floor , probably one of the biggest you 'll see in NY .\n->", + "output": "{\"text\": \"After dinner , take your date to the HUGE dance floor , probably one of the biggest you 'll see in NY .\", \"labels\": \"[{'aspect': 'dance floor', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dance floor', 'opinion': 'biggest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the e15 has a bright , 1080p screen - text is extremely sharp .\n->the e15 has a bright , 1080p screen - text is extremely sharp .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Also , do n't plan on asking for your favorite roll , if it 's not on the menu , you ca n't have it .\n->Also , do n't plan on asking for your favorite roll , if it 's not on the menu , you ca n't have it .\n[{'aspect': 'roll', 'opinion': 'favorite', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Taj Mahal offeres gret value and great food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTaj Mahal offeres gret value and great food .\n->", + "output": "{\"text\": \"Taj Mahal offeres gret value and great food .\", \"labels\": \"[{'aspect': 'value', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i predict based on tests performed by other users on youtube with battlefield 4 ( frostbyte 3 engine ) that this machine will perform well with the impending starwars battlefront ( also frostbyte 3 ) .\n->i predict based on tests performed by other users on youtube with battlefield 4 ( frostbyte 3 engine ) that this machine will perform well with the impending starwars battlefront ( also frostbyte 3 ) .\n[{'aspect': 'machine', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne . . . got a little moody afterwards cause was stuffed . . . lol\n->the pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne . . . got a little moody afterwards cause was stuffed . . . lol\n[{'aspect': 'pasta penne', 'opinion': 'buttery', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pasta penne', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n->", + "output": "{\"text\": \"My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keep up the good work .\n->keep up the good work .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Outstanding Bagels , but you get what you pay for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOutstanding Bagels , but you get what you pay for .\n->", + "output": "{\"text\": \"Outstanding Bagels , but you get what you pay for .\", \"labels\": \"[{'aspect': 'Bagels', 'opinion': 'Outstanding', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is a lot of fun .\n->The place is a lot of fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we did arrive late for our reservation so i can not complain too much about the wait for a table .\n->we did arrive late for our reservation so i can not complain too much about the wait for a table .\n[{'aspect': 'wait', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->", + "output": "{\"text\": \"Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\", \"labels\": \"[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'freshly baked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n->The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n->Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n[{'aspect': 'dim sum', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: I found it on a cold night , the perfect spot to warm up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI found it on a cold night , the perfect spot to warm up .\n->", + "output": "{\"text\": \"I found it on a cold night , the perfect spot to warm up .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: skip this restaurant , it ' s a big disappointment .\n->skip this restaurant , it ' s a big disappointment .\n[{'aspect': 'restaurant', 'opinion': 'skip', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it does have an ips screen , battery life is going strong , and no touch pad issues .\n->it does have an ips screen , battery life is going strong , and no touch pad issues .\n[{'aspect': 'ips screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'strong', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: Service was warm and attentive , beef carpaachio was exellent ( huge portion ) and pasta was fresh and well-prepared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was warm and attentive , beef carpaachio was exellent ( huge portion ) and pasta was fresh and well-prepared .\n->", + "output": "{\"text\": \"Service was warm and attentive , beef carpaachio was exellent ( huge portion ) and pasta was fresh and well-prepared .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beef carpaachio', 'opinion': 'exellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'well-prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: typing is responsive , the touchescreen is a joy and it ' s fast .\n->typing is responsive , the touchescreen is a joy and it ' s fast .\n[{'aspect': 'typing', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchescreen', 'opinion': 'joy', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the laptop itself seemed fine at first .\n->the laptop itself seemed fine at first .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Best Italian food I ever had ( and being Italian , that means alot ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest Italian food I ever had ( and being Italian , that means alot ) .\n->", + "output": "{\"text\": \"Best Italian food I ever had ( and being Italian , that means alot ) .\", \"labels\": \"[{'aspect': 'Italian food', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then suddenly it needs a software update which made my laptop crash .\n->then suddenly it needs a software update which made my laptop crash .\n[{'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: Prices too high for this cramped and unappealing resturant .\n->Prices too high for this cramped and unappealing resturant .\n[{'aspect': 'Prices', 'opinion': 'high', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I will recommend Scopa to all of my friends for a place to go for wonderful Italian food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI will recommend Scopa to all of my friends for a place to go for wonderful Italian food .\n->", + "output": "{\"text\": \"I will recommend Scopa to all of my friends for a place to go for wonderful Italian food .\", \"labels\": \"[{'aspect': 'Italian food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the lobster teriyaki and the rose special roll .\n->try the lobster teriyaki and the rose special roll .\n[{'aspect': 'lobster teriyaki', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rose special roll', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: app game ( simpsons tapped out ) ( i know don ' t judge ) lags at every moment .\n->app game ( simpsons tapped out ) ( i know don ' t judge ) lags at every moment .\n[{'aspect': 'app game', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Great bar , most gorgeous bartenders you 've ever seen ( specifically the blond lady ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat bar , most gorgeous bartenders you 've ever seen ( specifically the blond lady ) .\n->", + "output": "{\"text\": \"Great bar , most gorgeous bartenders you 've ever seen ( specifically the blond lady ) .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bartenders', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the hinges are just perfect .\n->- the hinges are just perfect .\n[{'aspect': 'hinges', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n->i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I really loved the different and inovated touch that 's the cheff gives to the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI really loved the different and inovated touch that 's the cheff gives to the food .\n->", + "output": "{\"text\": \"I really loved the different and inovated touch that 's the cheff gives to the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'inovated', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: personal pans are the perfect size for those hungry nights .\n->personal pans are the perfect size for those hungry nights .\n[{'aspect': 'personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n->i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n[{'aspect': '2012 chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: They came out over cooked and the cheese was almost non existant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey came out over cooked and the cheese was almost non existant .\n->", + "output": "{\"text\": \"They came out over cooked and the cheese was almost non existant .\", \"labels\": \"[{'aspect': 'cheese', 'opinion': 'non existant', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: monitor went out 35 days after receiving .\n->monitor went out 35 days after receiving .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: there were challenges with the chromebook specific apps and the google play store apps worked sometimes , but most of them had the size issue where they are only the size of a phone screen .\n->there were challenges with the chromebook specific apps and the google play store apps worked sometimes , but most of them had the size issue where they are only the size of a phone screen .\n[{'aspect': 'chromebook specific apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}, {'aspect': 'google play store apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n->", + "output": "{\"text\": \"I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'nice', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is one great place to eat pizza more out but not a good place for take-out pizza .\n->This is one great place to eat pizza more out but not a good place for take-out pizza .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'take-out pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Make sure you try this place as often as you can .\n->Make sure you try this place as often as you can .\n[{'aspect': 'place', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->", + "output": "{\"text\": \"The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\", \"labels\": \"[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n->the menu looked great , and the waiter was very nice , but when the food came , it was average .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the screen display is absolutely amazing and totally blows me away .\n->the screen display is absolutely amazing and totally blows me away .\n[{'aspect': 'screen display', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: The food is good , especially their more basic dishes , and the drinks are delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is good , especially their more basic dishes , and the drinks are delicious .\n->", + "output": "{\"text\": \"The food is good , especially their more basic dishes , and the drinks are delicious .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was also very good .\n->service was also very good .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: But the main hit was the whole grilled fish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the main hit was the whole grilled fish .\n->", + "output": "{\"text\": \"But the main hit was the whole grilled fish .\", \"labels\": \"[{'aspect': 'whole grilled fish', 'opinion': 'hit', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: whenever you need a sushi fix , mizu will be there with quality fish and great service .\n->whenever you need a sushi fix , mizu will be there with quality fish and great service .\n[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i can no longer return it and have wasted $ 850 , the support refuses to get back in touch or provide any form of civility .\n->i can no longer return it and have wasted $ 850 , the support refuses to get back in touch or provide any form of civility .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: On a hot day it was fabulous to stop in and enjoy lunch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOn a hot day it was fabulous to stop in and enjoy lunch .\n->", + "output": "{\"text\": \"On a hot day it was fabulous to stop in and enjoy lunch .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: awesome computer .\n->awesome computer .\n[{'aspect': 'computer', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the core m3 allows this system to get fast and to stay quiet .\n->the core m3 allows this system to get fast and to stay quiet .\n[{'aspect': 'core m3', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'core m3', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: The food is delicious and beautifully prepared along with the friendly and personable service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is delicious and beautifully prepared along with the friendly and personable service .\n->", + "output": "{\"text\": \"The food is delicious and beautifully prepared along with the friendly and personable service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'beautifully prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'personable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n->Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Why do people rave about the ambience .\n->Why do people rave about the ambience .\n[{'aspect': 'ambience', 'opinion': 'rave', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Pizza here is consistently good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza here is consistently good .\n->", + "output": "{\"text\": \"Pizza here is consistently good .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n->The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: - horrible customer service .\n->- horrible customer service .\n[{'aspect': 'customer service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: Would n't recomend it for dinner !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWould n't recomend it for dinner !\n->", + "output": "{\"text\": \"Would n't recomend it for dinner !\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'recomend', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the mediterranean salad , it is a true experience for your taste buds ! !\n->Try the mediterranean salad , it is a true experience for your taste buds ! !\n[{'aspect': 'mediterranean salad', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is good .\n->the keyboard is good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: Sauce was watery and the food did n't have much flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSauce was watery and the food did n't have much flavor .\n->", + "output": "{\"text\": \"Sauce was watery and the food did n't have much flavor .\", \"labels\": \"[{'aspect': 'Sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"did n't have much flavor\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is can became on e of the ny italian food fare institutions .\n->this is can became on e of the ny italian food fare institutions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i did n ' t complain , i liked the atmosphere so much .\n->i did n ' t complain , i liked the atmosphere so much .\n[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: My husband and I enjoy Sangria .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy husband and I enjoy Sangria .\n->", + "output": "{\"text\": \"My husband and I enjoy Sangria .\", \"labels\": \"[{'aspect': 'Sangria', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had this for about 3 weeks , and i ' m loving it .\n->i ' ve had this for about 3 weeks , and i ' m loving it .\n[{'aspect': 'NULL', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: It 's a rather cramped and busy restaurant and it closes early .\n->It 's a rather cramped and busy restaurant and it closes early .\n[{'aspect': 'restaurant', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: We both opted for a pasta dish and they were served timely and fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe both opted for a pasta dish and they were served timely and fresh .\n->", + "output": "{\"text\": \"We both opted for a pasta dish and they were served timely and fresh .\", \"labels\": \"[{'aspect': 'pasta dish', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'timely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would recommend Roxy 's for that , but not for their food .\n->I would recommend Roxy 's for that , but not for their food .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: great indian food and the service is incredible .\n->great indian food and the service is incredible .\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: First the wrong bread came out with the appetizer , then when i tried to order a second glass of wine for my main course ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFirst the wrong bread came out with the appetizer , then when i tried to order a second glass of wine for my main course ...\n->", + "output": "{\"text\": \"First the wrong bread came out with the appetizer , then when i tried to order a second glass of wine for my main course ...\", \"labels\": \"[{'aspect': 'bread', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'glass of wine', 'opinion': 'second', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n->it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: and finally today , 4 months after purchasing it , it has completely crashed .\n->and finally today , 4 months after purchasing it , it has completely crashed .\n[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: In an area sadly lacking in decent Thai food , this is one of the best spots .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn an area sadly lacking in decent Thai food , this is one of the best spots .\n->", + "output": "{\"text\": \"In an area sadly lacking in decent Thai food , this is one of the best spots .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: otherwise computer seems okay .\n->otherwise computer seems okay .\n[{'aspect': 'computer', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: would never go back\n->would never go back\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: Make reservations but expect to be delayed 15-20 minutes as the hosting staff was having difficulty seating guests who arrived with a reservation because they probably had a lot of walk ins being so close to Time Square .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMake reservations but expect to be delayed 15-20 minutes as the hosting staff was having difficulty seating guests who arrived with a reservation because they probably had a lot of walk ins being so close to Time Square .\n->", + "output": "{\"text\": \"Make reservations but expect to be delayed 15-20 minutes as the hosting staff was having difficulty seating guests who arrived with a reservation because they probably had a lot of walk ins being so close to Time Square .\", \"labels\": \"[{'aspect': 'reservations', 'opinion': 'delayed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'difficulty', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had the laptop for a full day now and i can say it is quite impressive .\n->i ' ve had the laptop for a full day now and i can say it is quite impressive .\n[{'aspect': 'laptop', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n->i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n[{'aspect': 'item', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'item', 'opinion': 'perfect', 'polarity': 'negative', 'category': 'SHIPPING#QUALITY'}]\ntext: The food was average or above including some surprising tasty dishes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was average or above including some surprising tasty dishes .\n->", + "output": "{\"text\": \"The food was average or above including some surprising tasty dishes .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The prices and ambience are especially great considering it 's in the West Village .\n->The prices and ambience are especially great considering it 's in the West Village .\n[{'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: touch pad is a + + .\n->touch pad is a + + .\n[{'aspect': 'touch pad', 'opinion': 'a +', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: They do n't seem to place an emphasis on specials or fresh ingredients which to me is necessary for good thai .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey do n't seem to place an emphasis on specials or fresh ingredients which to me is necessary for good thai .\n->", + "output": "{\"text\": \"They do n't seem to place an emphasis on specials or fresh ingredients which to me is necessary for good thai .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'thai', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is crisp and bright .\n->the screen is crisp and bright .\n[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: The food is very good for it 's price , better than most fried dumplings I 've had .\n->The food is very good for it 's price , better than most fried dumplings I 've had .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried dumplings', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The crust is thin , the ingredients are fresh and the staff is friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe crust is thin , the ingredients are fresh and the staff is friendly .\n->", + "output": "{\"text\": \"The crust is thin , the ingredients are fresh and the staff is friendly .\", \"labels\": \"[{'aspect': 'crust', 'opinion': 'thin', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this has been a really good computer for the money .\n->this has been a really good computer for the money .\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: All the NYU students love this place so it makes for a fun young atmosphere .\n->All the NYU students love this place so it makes for a fun young atmosphere .\n[{'aspect': 'atmosphere', 'opinion': 'fun young', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Also a little more expensive than your average bagel place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlso a little more expensive than your average bagel place .\n->", + "output": "{\"text\": \"Also a little more expensive than your average bagel place .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'expensive', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m sure i ' ll return for a final judgement tho .\n->i ' m sure i ' ll return for a final judgement tho .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we ordered this chromebook for my son to use for school .\n->we ordered this chromebook for my son to use for school .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: the food was undercooked -the sauce watery , and the vegetables raw .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was undercooked -the sauce watery , and the vegetables raw .\n->", + "output": "{\"text\": \"the food was undercooked -the sauce watery , and the vegetables raw .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'raw', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have reservations about the all you can eat deal , however - - the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n->i have reservations about the all you can eat deal , however - - the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n[{'aspect': 'all you can eat deal', 'opinion': 'limited', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'all you can eat deal', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: apple should be embarrassed .\n->apple should be embarrassed .\n[{'aspect': 'apple', 'opinion': 'embarrassed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: And the fried clams had just enough kick to them to make 'em worth eating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd the fried clams had just enough kick to them to make 'em worth eating .\n->", + "output": "{\"text\": \"And the fried clams had just enough kick to them to make 'em worth eating .\", \"labels\": \"[{'aspect': 'fried clams', 'opinion': 'enough', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very satisfied with simplicity of use , the streamlining of the google products , and the considerable battery life .\n->very satisfied with simplicity of use , the streamlining of the google products , and the considerable battery life .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'google products', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'considerable', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n->You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Vanilla Shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The sauces used are also not that exciting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sauces used are also not that exciting .\n->", + "output": "{\"text\": \"The sauces used are also not that exciting .\", \"labels\": \"[{'aspect': 'sauces', 'opinion': 'not that exciting', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s fine as a computer , but the lack of a real guest account made it not workable as a family room media machine .\n->it ' s fine as a computer , but the lack of a real guest account made it not workable as a family room media machine .\n[{'aspect': 'guest account', 'opinion': 'not workable', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: The food is great .\n->The food is great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The secret is the lunch menu which offers a complimentary appetizer with every entree ordered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe secret is the lunch menu which offers a complimentary appetizer with every entree ordered .\n->", + "output": "{\"text\": \"The secret is the lunch menu which offers a complimentary appetizer with every entree ordered .\", \"labels\": \"[{'aspect': 'lunch menu', 'opinion': 'secret', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizer', 'opinion': 'complimentary', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entree', 'opinion': 'complimentary', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I will recommend Scopa to all of my friends for a place to go for wonderful Italian food .\n->I will recommend Scopa to all of my friends for a place to go for wonderful Italian food .\n[{'aspect': 'Italian food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: its a good computer for the price but it needs to work awhile without crashing within 6 months .\n->its a good computer for the price but it needs to work awhile without crashing within 6 months .\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The shrimp scampi was excellent and the antipasti were plentiful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe shrimp scampi was excellent and the antipasti were plentiful .\n->", + "output": "{\"text\": \"The shrimp scampi was excellent and the antipasti were plentiful .\", \"labels\": \"[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'lot', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I got the $ 10 10-piece dim sum combo , every bite of which was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI got the $ 10 10-piece dim sum combo , every bite of which was great .\n->", + "output": "{\"text\": \"I got the $ 10 10-piece dim sum combo , every bite of which was great .\", \"labels\": \"[{'aspect': 'dim sum combo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this guy refused to seat her and she left , followed shortly by the four of us , but not before i told him that in my 40 years of world travel , including paris , that i had never seen such a display of bad behavior by a frontman in a restaurant .\n->this guy refused to seat her and she left , followed shortly by the four of us , but not before i told him that in my 40 years of world travel , including paris , that i had never seen such a display of bad behavior by a frontman in a restaurant .\n[{'aspect': 'frontman', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great purchase , quick shipping .\n->great purchase , quick shipping .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'shipping', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: There is also very limited seating and there can be a substantial wait in getting food at peak times .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere is also very limited seating and there can be a substantial wait in getting food at peak times .\n->", + "output": "{\"text\": \"There is also very limited seating and there can be a substantial wait in getting food at peak times .\", \"labels\": \"[{'aspect': 'seating', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'substantial', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best dish is nori-wrapped tuna .\n->Best dish is nori-wrapped tuna .\n[{'aspect': 'nori-wrapped tuna', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the location is perfect .\n->the location is perfect .\n[{'aspect': 'location', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: If it 's just a quick martini at the bar ( which I recommend Jeffery 's ) or a mind blowing Roast Chicken , go to Village !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf it 's just a quick martini at the bar ( which I recommend Jeffery 's ) or a mind blowing Roast Chicken , go to Village !\n->", + "output": "{\"text\": \"If it 's just a quick martini at the bar ( which I recommend Jeffery 's ) or a mind blowing Roast Chicken , go to Village !\", \"labels\": \"[{'aspect': 'martini', 'opinion': 'quick', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Roast Chicken', 'opinion': 'mind blowing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good selection of wines ranging from affordable to high end .\n->good selection of wines ranging from affordable to high end .\n[{'aspect': 'selection of wines', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: noisy hdr , better with ssd ) works quickly .\n->noisy hdr , better with ssd ) works quickly .\n[{'aspect': 'hdr', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}, {'aspect': 'ssd', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\ntext: There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n->", + "output": "{\"text\": \"There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'long wait', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'insde table', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is not worth the prices .\n->this place is not worth the prices .\n[{'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: laptop runs really well but fans get a bit loud when gaming\n->laptop runs really well but fans get a bit loud when gaming\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: The prices are wonderfully low .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe prices are wonderfully low .\n->", + "output": "{\"text\": \"The prices are wonderfully low .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'wonderfully low', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Turned out there was full service upstairs and sat down .\n->Turned out there was full service upstairs and sat down .\n[{'aspect': 'service', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m beyond satisfied with this chromebook , it is stunning in every way .\n->i ' m beyond satisfied with this chromebook , it is stunning in every way .\n[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I have never been disappointed but their true strength lays in their amazingly delicious and cheap lunch specials .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have never been disappointed but their true strength lays in their amazingly delicious and cheap lunch specials .\n->", + "output": "{\"text\": \"I have never been disappointed but their true strength lays in their amazingly delicious and cheap lunch specials .\", \"labels\": \"[{'aspect': 'lunch specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch specials', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was authentic .\n->The food was authentic .\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is the most well priced laptop for its spec\n->this is the most well priced laptop for its spec\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'spec', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: So much more than the usual bar food , go there to enjoy the menu while sampling one of their hand-crafted beers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSo much more than the usual bar food , go there to enjoy the menu while sampling one of their hand-crafted beers .\n->", + "output": "{\"text\": \"So much more than the usual bar food , go there to enjoy the menu while sampling one of their hand-crafted beers .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hand-crafted beers', 'opinion': 'hand-crafted', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n->The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n[{'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n->i loved this chromebook but i had to return it bevause it had sound issues .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: No free drink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNo free drink .\n->", + "output": "{\"text\": \"No free drink .\", \"labels\": \"[{'aspect': 'drink', 'opinion': 'No free', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delete and power button too close to each other and looks the same , i accidentally pressed the power button couple of times .\n->delete and power button too close to each other and looks the same , i accidentally pressed the power button couple of times .\n[{'aspect': 'delete and power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: it printed easily to our wireless printer too !\n->it printed easily to our wireless printer too !\n[{'aspect': 'NULL', 'opinion': 'easily', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: This is my first time writing a review for a restaurant because the food and service was excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is my first time writing a review for a restaurant because the food and service was excellent .\n->", + "output": "{\"text\": \"This is my first time writing a review for a restaurant because the food and service was excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the best secret place in midtown ' , I heard that from the bartender , after having brilliant food ( try steak with portobello mushrooms ) and drinks on the bar last Tuesday .\n->this is the best secret place in midtown ' , I heard that from the bartender , after having brilliant food ( try steak with portobello mushrooms ) and drinks on the bar last Tuesday .\n[{'aspect': 'food', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak with portobello mushrooms', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak with portobello mushrooms', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: one of the things that drew me to the c302 was the convertible form - factor .\n->one of the things that drew me to the c302 was the convertible form - factor .\n[{'aspect': 'c302', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Do n't waste money on decor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDo n't waste money on decor .\n->", + "output": "{\"text\": \"Do n't waste money on decor .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'waste', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was pleasantly suprised .\n->i was pleasantly suprised .\n[{'aspect': 'NULL', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'suprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n->i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n[{'aspect': 'chromebook', 'opinion': 'enthusiast', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: They also have a back garden open in the summer - cute and French with outdoor seating - what more could you ask for ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey also have a back garden open in the summer - cute and French with outdoor seating - what more could you ask for ?\n->", + "output": "{\"text\": \"They also have a back garden open in the summer - cute and French with outdoor seating - what more could you ask for ?\", \"labels\": \"[{'aspect': 'back garden', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back garden', 'opinion': 'French', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this unit is a great compromise between powerful cpu and gpu with good battery life .\n->this unit is a great compromise between powerful cpu and gpu with good battery life .\n[{'aspect': 'cpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'gpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}, {'aspect': 'unit', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I have never before eaten 40 pieces of relatively good nigiri .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have never before eaten 40 pieces of relatively good nigiri .\n->", + "output": "{\"text\": \"I have never before eaten 40 pieces of relatively good nigiri .\", \"labels\": \"[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n->ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: service is not what you are coming here for . . .\n->service is not what you are coming here for . . .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Given the incredible architecture surrounding it , this place has no character .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGiven the incredible architecture surrounding it , this place has no character .\n->", + "output": "{\"text\": \"Given the incredible architecture surrounding it , this place has no character .\", \"labels\": \"[{'aspect': 'architecture', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'no character', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n->i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we ' ve got android nougat on beta running pretty well now .\n->we ' ve got android nougat on beta running pretty well now .\n[{'aspect': 'android nougat', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n->", + "output": "{\"text\": \"While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but the service was a bit slow .\n->but the service was a bit slow .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Their eggplant is so delicate , sweet tender !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir eggplant is so delicate , sweet tender !\n->", + "output": "{\"text\": \"Their eggplant is so delicate , sweet tender !\", \"labels\": \"[{'aspect': 'eggplant', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'eggplant', 'opinion': 'sweet tender', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n->apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#OPERATION_PERFORMANCE'}]\nExample:\ntext: but it lost the coil whine roulette - - badly .\n->but it lost the coil whine roulette - - badly .\n[{'aspect': 'NULL', 'opinion': 'badly', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Each bite of food at Kai was indeed delicious , fresh , and elegant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEach bite of food at Kai was indeed delicious , fresh , and elegant .\n->", + "output": "{\"text\": \"Each bite of food at Kai was indeed delicious , fresh , and elegant .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m seriously considering returning it !\n->i ' m seriously considering returning it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: His drinks are very inventive , delicious and classy .\n->His drinks are very inventive , delicious and classy .\n[{'aspect': 'drinks', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'classy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: all the food was excellent - considering the quality of food in most moderately priced restaurants is mediocre this was slightly more pricey and well worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall the food was excellent - considering the quality of food in most moderately priced restaurants is mediocre this was slightly more pricey and well worth it .\n->", + "output": "{\"text\": \"all the food was excellent - considering the quality of food in most moderately priced restaurants is mediocre this was slightly more pricey and well worth it .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality of food', 'opinion': 'mediocre', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'moderately', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only downside . . . they only take cash which is ok if you know about it ahead of time .\n->the only downside . . . they only take cash which is ok if you know about it ahead of time .\n[{'aspect': 'NULL', 'opinion': 'downside', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: however , it charges insanely quickly : you can get a full charge in under an hour .\n->however , it charges insanely quickly : you can get a full charge in under an hour .\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: I had the cod with paella ( spicy and very filling , I 'm a big eater and could only eat half ) while my boyfriend had the classic fish and chips ( again , a big serving - at least 5 pieces of fish and a basketful of fries ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had the cod with paella ( spicy and very filling , I 'm a big eater and could only eat half ) while my boyfriend had the classic fish and chips ( again , a big serving - at least 5 pieces of fish and a basketful of fries ) .\n->", + "output": "{\"text\": \"I had the cod with paella ( spicy and very filling , I 'm a big eater and could only eat half ) while my boyfriend had the classic fish and chips ( again , a big serving - at least 5 pieces of fish and a basketful of fries ) .\", \"labels\": \"[{'aspect': 'cod with paella', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cod with paella', 'opinion': 'filling', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'classic', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'big', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'serving', 'opinion': 'big', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: how is this palce still open ?\n->how is this palce still open ?\n[{'aspect': 'palce', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: They have it all -- great price , food , and service .\n->They have it all -- great price , food , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: It 's a shame that a nice , convenient place like the Pink Pony can be so ruined by lousy service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's a shame that a nice , convenient place like the Pink Pony can be so ruined by lousy service .\n->", + "output": "{\"text\": \"It 's a shame that a nice , convenient place like the Pink Pony can be so ruined by lousy service .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While there are plenty of places to go for a good corned beef sandwich , Katz 's has a charm about it .\n->While there are plenty of places to go for a good corned beef sandwich , Katz 's has a charm about it .\n[{'aspect': 'corned beef sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n->Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n[{'aspect': 'waiting area', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seat', 'opinion': 'all taken', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Try the spicy wontons and the salt pepper shrimps .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the spicy wontons and the salt pepper shrimps .\n->", + "output": "{\"text\": \"Try the spicy wontons and the salt pepper shrimps .\", \"labels\": \"[{'aspect': 'spicy wontons', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salt pepper shrimps', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Rao 's has the best service and atmosphere in NYC .\n->Rao 's has the best service and atmosphere in NYC .\n[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I got the $ 10 10-piece dim sum combo , every bite of which was great .\n->I got the $ 10 10-piece dim sum combo , every bite of which was great .\n[{'aspect': 'dim sum combo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The fries are yummy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fries are yummy .\n->", + "output": "{\"text\": \"The fries are yummy .\", \"labels\": \"[{'aspect': 'fries', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i do not recommend .\n->i do not recommend .\n[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: Friendly and informative staff , very attentive and prompt raw bar service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFriendly and informative staff , very attentive and prompt raw bar service .\n->", + "output": "{\"text\": \"Friendly and informative staff , very attentive and prompt raw bar service .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'informative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar service', 'opinion': 'raw', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can ' t use it to edit a word document .\n->you can ' t use it to edit a word document .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\nExample:\ntext: cons : the speakers make a loud muffled white noise while playing music on occasion .\n->cons : the speakers make a loud muffled white noise while playing music on occasion .\n[{'aspect': 'speakers', 'opinion': 'cons', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: Best Taiwanese food in NY !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest Taiwanese food in NY !\n->", + "output": "{\"text\": \"Best Taiwanese food in NY !\", \"labels\": \"[{'aspect': 'Taiwanese food', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great laptop .\n->this is a great laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I am amazed by the poor reviews- I find this place to be standout Italian in an area flooded with Italian- great prices , great atmosphere , good service and a wonderful wine list .\n->I am amazed by the poor reviews- I find this place to be standout Italian in an area flooded with Italian- great prices , great atmosphere , good service and a wonderful wine list .\n[{'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place is a great stop for great food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is a great stop for great food .\n->", + "output": "{\"text\": \"This place is a great stop for great food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is cheap .\n->the build quality is cheap .\n[{'aspect': 'build quality', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The food now is inconsistent .\n->The food now is inconsistent .\n[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n->", + "output": "{\"text\": \"All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\", \"labels\": \"[{'aspect': 'pastas', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade lasagna', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love love love this laptop !\n->love love love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: wine list selection is good and wine - by - the - glass was generously filled to the top .\n->wine list selection is good and wine - by - the - glass was generously filled to the top .\n[{'aspect': 'wine list selection', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine - by - the - glass', 'opinion': 'generously filled', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: And the prices were way to high for what you get .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd the prices were way to high for what you get .\n->", + "output": "{\"text\": \"And the prices were way to high for what you get .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'high', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: customer service told me it ' s faulty\n->customer service told me it ' s faulty\n[{'aspect': 'customer service', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n->i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The staff ignored my friends and I the entire time we were there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff ignored my friends and I the entire time we were there .\n->", + "output": "{\"text\": \"The staff ignored my friends and I the entire time we were there .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice computer for the price .\n->nice computer for the price .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n->The people that work there are always so friendly you forget you are in New York sometimes .\n[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n->", + "output": "{\"text\": \"Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very unsatisfied with warranty service .\n->very unsatisfied with warranty service .\n[{'aspect': 'warranty service', 'opinion': 'unsatisfied', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\nExample:\ntext: one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n->one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n[{'aspect': 'chromeos', 'opinion': 'frustrate', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\ntext: The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n->", + "output": "{\"text\": \"The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\", \"labels\": \"[{'aspect': 'in-house lady DJ', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good for casual dinner with jeans and sneakers .\n->Good for casual dinner with jeans and sneakers .\n[{'aspect': 'casual dinner', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: now there are pixels on the screen not working , and they are multiplying .\n->now there are pixels on the screen not working , and they are multiplying .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: Great wine list , reasonably priced . -- Sara\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat wine list , reasonably priced . -- Sara\n->", + "output": "{\"text\": \"Great wine list , reasonably priced . -- Sara\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Late nite omelletes are not good here , there is no variety !\n->Late nite omelletes are not good here , there is no variety !\n[{'aspect': 'omelletes', 'opinion': 'not good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The food is fresh , delicious , and reasonably priced .\n->The food is fresh , delicious , and reasonably priced .\n[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The hot dogs were cold in the middle and the buns were stale .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe hot dogs were cold in the middle and the buns were stale .\n->", + "output": "{\"text\": \"The hot dogs were cold in the middle and the buns were stale .\", \"labels\": \"[{'aspect': 'hot dogs', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'buns', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n[{'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n->certain apps ( especially flash based apps ) will get the machine very hot .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n->", + "output": "{\"text\": \"While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mussles and calamari were superb saturday evening .\n->mussles and calamari were superb saturday evening .\n[{'aspect': 'mussles', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'calamari', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i absolutely loved this place .\n->i absolutely loved this place .\n[{'aspect': 'place', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Beef noodle soup is good as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBeef noodle soup is good as well .\n->", + "output": "{\"text\": \"Beef noodle soup is good as well .\", \"labels\": \"[{'aspect': 'Beef noodle soup', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Of course the reason its so packed is because the food is so delicious !\n->Of course the reason its so packed is because the food is so delicious !\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i don ' t understand this kind of design .\n->i don ' t understand this kind of design .\n[{'aspect': 'design', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Delivery service is great too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDelivery service is great too .\n->", + "output": "{\"text\": \"Delivery service is great too .\", \"labels\": \"[{'aspect': 'Delivery service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place has got to be the best japanese restaurant in the new york area .\n->This place has got to be the best japanese restaurant in the new york area .\n[{'aspect': 'place', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n->On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food 's dazzling flavors overwhelm the palate , truly embracing the beauty of authentic Thai cuisine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food 's dazzling flavors overwhelm the palate , truly embracing the beauty of authentic Thai cuisine .\n->", + "output": "{\"text\": \"The food 's dazzling flavors overwhelm the palate , truly embracing the beauty of authentic Thai cuisine .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'overwhelm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai cuisine', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavors', 'opinion': 'overwhelm', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: that is just incredible .\n->that is just incredible .\n[{'aspect': 'NULL', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: everything lagged and the screen flickered .\n->everything lagged and the screen flickered .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: I recieved prompt service with a smile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recieved prompt service with a smile .\n->", + "output": "{\"text\": \"I recieved prompt service with a smile .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pixels are currently stuck .\n->the pixels are currently stuck .\n[{'aspect': 'pixels', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n->My suggestion is to eat family style because you 'll want to try the other dishes .\n[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\ntext: They pray to their Food Gods to make them into a good pizza like VT 's .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey pray to their Food Gods to make them into a good pizza like VT 's .\n->", + "output": "{\"text\": \"They pray to their Food Gods to make them into a good pizza like VT 's .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were not dissappointed in the least bit by this little gem .\n->we were not dissappointed in the least bit by this little gem .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: suddenly the laptop goes to sleep and doesn ' t wake up .\n->suddenly the laptop goes to sleep and doesn ' t wake up .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The place was quiet and delightful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place was quiet and delightful .\n->", + "output": "{\"text\": \"The place was quiet and delightful .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is great and reasonably priced .\n->the food is great and reasonably priced .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i started out with a bombay beer which was big enough for two .\n->i started out with a bombay beer which was big enough for two .\n[{'aspect': 'bombay beer', 'opinion': 'big', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->", + "output": "{\"text\": \"The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'homemade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'herbs', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Scalina Fedeli reminded me why service is so integral to fine dining .\n->Scalina Fedeli reminded me why service is so integral to fine dining .\n[{'aspect': 'service', 'opinion': 'integral', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Decor is charming .\n->Decor is charming .\n[{'aspect': 'Decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\ntext: As much as I like the food there , I ca n't bring myself to go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs much as I like the food there , I ca n't bring myself to go back .\n->", + "output": "{\"text\": \"As much as I like the food there , I ca n't bring myself to go back .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - timeout on keyboard backlight not adjustable .\n->- timeout on keyboard backlight not adjustable .\n[{'aspect': 'keyboard', 'opinion': 'not adjustable', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: good spreads , great beverage selections and bagels really tasty .\n->good spreads , great beverage selections and bagels really tasty .\n[{'aspect': 'spreads', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beverage selections', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'bagels', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The prices and ambience are especially great considering it 's in the West Village .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe prices and ambience are especially great considering it 's in the West Village .\n->", + "output": "{\"text\": \"The prices and ambience are especially great considering it 's in the West Village .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chrome os is intuitive and easy to use .\n->chrome os is intuitive and easy to use .\n[{'aspect': 'chrome os', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'chrome os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\nExample:\ntext: You can not go wrong with this place .\n->You can not go wrong with this place .\n[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The soup is pretty good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe soup is pretty good too .\n->", + "output": "{\"text\": \"The soup is pretty good too .\", \"labels\": \"[{'aspect': 'soup', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n->what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n[{'aspect': 'NULL', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fail', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: It is so easy to get a reservation at a top place in NYC with a week 's notice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is so easy to get a reservation at a top place in NYC with a week 's notice .\n->", + "output": "{\"text\": \"It is so easy to get a reservation at a top place in NYC with a week 's notice .\", \"labels\": \"[{'aspect': 'reservation', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice chromebook .\n->nice chromebook .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: somehow working the italian charm with constant mille grazie does not constitute proper service .\n->somehow working the italian charm with constant mille grazie does not constitute proper service .\n[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n->", + "output": "{\"text\": \"not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the portions are small but being that the food was so good makes up for that .\n->the portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n->Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n[{'aspect': 'salmon', 'opinion': 'wasnt impressed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servings', 'opinion': 'Small', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I always get the Shabu-Shabu dinner and the beef is always fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI always get the Shabu-Shabu dinner and the beef is always fresh .\n->", + "output": "{\"text\": \"I always get the Shabu-Shabu dinner and the beef is always fresh .\", \"labels\": \"[{'aspect': 'beef', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n->i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n[{'aspect': 'NULL', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n->my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The pizza was delivered cold and the cheese was n't even fully melted !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza was delivered cold and the cheese was n't even fully melted !\n->", + "output": "{\"text\": \"The pizza was delivered cold and the cheese was n't even fully melted !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': \"was n't even fully melted\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little computer is awesome and it was so inexpensive for what you get !\n->this little computer is awesome and it was so inexpensive for what you get !\n[{'aspect': 'computer', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: my main complaint is the battery life , i see many positive reviews about the battery life .\n->my main complaint is the battery life , i see many positive reviews about the battery life .\n[{'aspect': 'battery life', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: We were very surprised by how good the food was on our first visit here on a Sunday night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were very surprised by how good the food was on our first visit here on a Sunday night .\n->", + "output": "{\"text\": \"We were very surprised by how good the food was on our first visit here on a Sunday night .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n->Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork belly', 'opinion': 'melt-in-your-mouth', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fat', 'opinion': 'longer', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n->My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n[{'aspect': 'food', 'opinion': 'opposite', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Nha Trang , while being notorious for utter lack of comfort and decor , horribly slow wait staff and horribly quick meals , is one of the best vietnamese restaurants i 've ever been to . the pho is delicious and comes with very fresh vegtables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNha Trang , while being notorious for utter lack of comfort and decor , horribly slow wait staff and horribly quick meals , is one of the best vietnamese restaurants i 've ever been to . the pho is delicious and comes with very fresh vegtables .\n->", + "output": "{\"text\": \"Nha Trang , while being notorious for utter lack of comfort and decor , horribly slow wait staff and horribly quick meals , is one of the best vietnamese restaurants i 've ever been to . the pho is delicious and comes with very fresh vegtables .\", \"labels\": \"[{'aspect': 'comfort', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'horribly slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meals', 'opinion': 'horribly quick', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pho', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegtables', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We were worried we would have trouble getting in , but somehow managed to have a short wait .\n->We were worried we would have trouble getting in , but somehow managed to have a short wait .\n[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I think the stuff was better than Disney .\n->I think the stuff was better than Disney .\n[{'aspect': 'stuff', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: On the other hand , if you are not fooled easily , you will find hundreds of restaurants that will give you service and ambiance that is on par with Alain Ducasse , and food that will outshine in presentaion , taste , choice , quality and quantity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOn the other hand , if you are not fooled easily , you will find hundreds of restaurants that will give you service and ambiance that is on par with Alain Ducasse , and food that will outshine in presentaion , taste , choice , quality and quantity .\n->", + "output": "{\"text\": \"On the other hand , if you are not fooled easily , you will find hundreds of restaurants that will give you service and ambiance that is on par with Alain Ducasse , and food that will outshine in presentaion , taste , choice , quality and quantity .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'on par', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'on par', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'outshine', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i first got it in the mail and i opened it up and turned it on for the first time , i was a little speechless because the retina display looks so good !\n->when i first got it in the mail and i opened it up and turned it on for the first time , i was a little speechless because the retina display looks so good !\n[{'aspect': 'retina display', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: i called acer tech support but nothing worked .\n->i called acer tech support but nothing worked .\n[{'aspect': 'acer tech support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: If I could rate the people this place would be off the charts - unfortunately - the pizza , sorry - not the best in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf I could rate the people this place would be off the charts - unfortunately - the pizza , sorry - not the best in NYC .\n->", + "output": "{\"text\": \"If I could rate the people this place would be off the charts - unfortunately - the pizza , sorry - not the best in NYC .\", \"labels\": \"[{'aspect': 'people', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'best', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n->i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s great other than the weak speakers and the touchpad .\n->it ' s great other than the weak speakers and the touchpad .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: Having not been home in the last 2 years may skew this reviewer a bit , but the food was tasty and spicy sans the oil that comes floating along at similar venues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHaving not been home in the last 2 years may skew this reviewer a bit , but the food was tasty and spicy sans the oil that comes floating along at similar venues .\n->", + "output": "{\"text\": \"Having not been home in the last 2 years may skew this reviewer a bit , but the food was tasty and spicy sans the oil that comes floating along at similar venues .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen ' s color gamut is only 50 % ntsc , but as i don ' t perform image editing with it , this hasn ' t mattered .\n->the screen ' s color gamut is only 50 % ntsc , but as i don ' t perform image editing with it , this hasn ' t mattered .\n[{'aspect': \"screen ' s color gamut\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Not only is the cuisine the best around , the service has always been attentive and charming .\n->Not only is the cuisine the best around , the service has always been attentive and charming .\n[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The portions are now very small , the sauces are overly ambitious usually inedible while the service is still good , the restaurant , due to its popularity , seems frantic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portions are now very small , the sauces are overly ambitious usually inedible while the service is still good , the restaurant , due to its popularity , seems frantic .\n->", + "output": "{\"text\": \"The portions are now very small , the sauces are overly ambitious usually inedible while the service is still good , the restaurant , due to its popularity , seems frantic .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'ambitious', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: had an awful experience at casa la femme on a saturday dinner .\n->had an awful experience at casa la femme on a saturday dinner .\n[{'aspect': 'casa la femme', 'opinion': 'awful', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: screen backlight stopped working after just one month of light use .\n->screen backlight stopped working after just one month of light use .\n[{'aspect': 'screen backlight', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe owner is very friendly and a great guy , go try his pizza , you 'll like it !\n->", + "output": "{\"text\": \"The owner is very friendly and a great guy , go try his pizza , you 'll like it !\", \"labels\": \"[{'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they even scoop it out nice ( for those on a diet ) not too much not to little .\n->they even scoop it out nice ( for those on a diet ) not too much not to little .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n->Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n[{'aspect': 'Wait staff', 'opinion': 'unappreciative', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The atmosphere is great ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is great ! ! !\n->", + "output": "{\"text\": \"The atmosphere is great ! ! !\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: typing is responsive , the touchescreen is a joy and it ' s fast .\n->typing is responsive , the touchescreen is a joy and it ' s fast .\n[{'aspect': 'typing', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchescreen', 'opinion': 'joy', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: if this guy is in your price range - just buy it and get it over with .\n->if this guy is in your price range - just buy it and get it over with .\n[{'aspect': 'guy', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWith so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n->", + "output": "{\"text\": \"With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait-staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': \"does n't care\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'diner', 'opinion': 'glorified', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keys and mouse pad are responsive and comfortable to use .\n->the keys and mouse pad are responsive and comfortable to use .\n[{'aspect': 'keys', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keys', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: the mussles were the fishiest things i ' ve ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w / chicken had bones in it . . . it was disgusting .\n->the mussles were the fishiest things i ' ve ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w / chicken had bones in it . . . it was disgusting .\n[{'aspect': 'mussles', 'opinion': 'fishiest', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'seabass', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'goat cheese salad', 'opinion': 'missing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'penne w / chicken', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .\n->", + "output": "{\"text\": \"I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .\", \"labels\": \"[{'aspect': 'sea urchin', 'opinion': 'heavenly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thin , light , cool are what i feel when holding it and carry around .\n->thin , light , cool are what i feel when holding it and carry around .\n[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: The dim sum however was very good .\n->The dim sum however was very good .\n[{'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service is a little scatty at times but all is forgiven when the food arrives .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is a little scatty at times but all is forgiven when the food arrives .\n->", + "output": "{\"text\": \"The service is a little scatty at times but all is forgiven when the food arrives .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'scatty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'forgiven', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mem , hdd , screed , dvd drive are all easily accessible and removable .\n->mem , hdd , screed , dvd drive are all easily accessible and removable .\n[{'aspect': 'mem', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'mem', 'opinion': 'removable', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'hdd', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'hdd', 'opinion': 'removable', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'screed', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screed', 'opinion': 'removable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'removable', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}]\nExample:\ntext: was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n->was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n[{'aspect': 'chef', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n->", + "output": "{\"text\": \"The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\", \"labels\": \"[{'aspect': 'bruscetta', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mix of greens', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n->If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n[{'aspect': 'ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n->i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n[{'aspect': 'item', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'item', 'opinion': 'perfect', 'polarity': 'negative', 'category': 'SHIPPING#QUALITY'}]\ntext: The atmosphere is n't the greatest , but I suppose that 's how they keep the prices down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is n't the greatest , but I suppose that 's how they keep the prices down .\n->", + "output": "{\"text\": \"The atmosphere is n't the greatest , but I suppose that 's how they keep the prices down .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': \"is n't the greatest\", 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'down', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I loved everythig about it-especially the shows and actors .\n->I loved everythig about it-especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this bios is horrible .\n->this bios is horrible .\n[{'aspect': 'bios', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: The pickles were great addition .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pickles were great addition .\n->", + "output": "{\"text\": \"The pickles were great addition .\", \"labels\": \"[{'aspect': 'pickles', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price i think it ' s just fine .\n->for the price i think it ' s just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: love it so far very nice product and it do what it is set out to do\n->love it so far very nice product and it do what it is set out to do\n[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: If your favorite Chinese food is General Tao chicken , then this is NOT your place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf your favorite Chinese food is General Tao chicken , then this is NOT your place .\n->", + "output": "{\"text\": \"If your favorite Chinese food is General Tao chicken , then this is NOT your place .\", \"labels\": \"[{'aspect': 'General Tao chicken', 'opinion': 'favorite', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you need a decent computer that runs quality this is it , especially if you are starting out .\n->if you need a decent computer that runs quality this is it , especially if you are starting out .\n[{'aspect': 'computer', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: during the course of the past 3 months , the chef and staff changed and it was not for the better .\n->during the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: You must try the shrimp appetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou must try the shrimp appetizers .\n->", + "output": "{\"text\": \"You must try the shrimp appetizers .\", \"labels\": \"[{'aspect': 'shrimp appetizers', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .\n->I will be out with friends and all of a sudden I am hungry and I only crave one thing ... their Pizza .\n[{'aspect': 'Pizza', 'opinion': 'crave', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: sorry not a fan of windows 10 .\n->sorry not a fan of windows 10 .\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\ntext: Not only is the cuisine the best around , the service has always been attentive and charming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot only is the cuisine the best around , the service has always been attentive and charming .\n->", + "output": "{\"text\": \"Not only is the cuisine the best around , the service has always been attentive and charming .\", \"labels\": \"[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: on that scale , it ' s a world - beater .\n->on that scale , it ' s a world - beater .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it is terrific , as is the value .\n->it is terrific , as is the value .\n[{'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: The production is a symphony , alot of fun to experience.The food sublime for the most part .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe production is a symphony , alot of fun to experience.The food sublime for the most part .\n->", + "output": "{\"text\": \"The production is a symphony , alot of fun to experience.The food sublime for the most part .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'sublime', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i feel like this will look good in any decade .\n->i feel like this will look good in any decade .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: all in all it is a great machine .\n->all in all it is a great machine .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Waiters tend to forget drinks completely , food portions are so tiny , two people have trouble sharing one entree .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWaiters tend to forget drinks completely , food portions are so tiny , two people have trouble sharing one entree .\n->", + "output": "{\"text\": \"Waiters tend to forget drinks completely , food portions are so tiny , two people have trouble sharing one entree .\", \"labels\": \"[{'aspect': 'Waiters', 'opinion': 'forget', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'forget', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'entree', 'opinion': 'trouble', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would highly recommend .\n->i would highly recommend .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n->Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n[{'aspect': 'Barbecued codfish', 'opinion': 'moist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seasoning', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spice rub', 'opinion': 'overwhelmed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'herb mix', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}]\ntext: But the thing that my wife and I hated was it was so loud and it felt like ' bar ' or ' pub ' .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the thing that my wife and I hated was it was so loud and it felt like ' bar ' or ' pub ' .\n->", + "output": "{\"text\": \"But the thing that my wife and I hated was it was so loud and it felt like ' bar ' or ' pub ' .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pub', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had Pam 's special fried fish and it was amazing .\n->We had Pam 's special fried fish and it was amazing .\n[{'aspect': \"Pam 's special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: While there are plenty of places to go for a good corned beef sandwich , Katz 's has a charm about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile there are plenty of places to go for a good corned beef sandwich , Katz 's has a charm about it .\n->", + "output": "{\"text\": \"While there are plenty of places to go for a good corned beef sandwich , Katz 's has a charm about it .\", \"labels\": \"[{'aspect': 'corned beef sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mbp does not run as hot as my old white mb did .\n->the mbp does not run as hot as my old white mb did .\n[{'aspect': 'mbp', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n->", + "output": "{\"text\": \"However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has a weird smell that ' s why i ' m giving it 3 stars .\n->it has a weird smell that ' s why i ' m giving it 3 stars .\n[{'aspect': 'NULL', 'opinion': 'weird', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the computer ' s hardware is decent , but the materials are poor .\n->the computer ' s hardware is decent , but the materials are poor .\n[{'aspect': \"computer ' s hardware\", 'opinion': 'decent', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'materials', 'opinion': 'poor', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: Priced at upper intermediate range .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPriced at upper intermediate range .\n->", + "output": "{\"text\": \"Priced at upper intermediate range .\", \"labels\": \"[{'aspect': 'Priced', 'opinion': 'upper intermediate', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Despite the fact that the space is large , they 've overcrowded the floor with tables .\n->Despite the fact that the space is large , they 've overcrowded the floor with tables .\n[{'aspect': 'space', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'overcrowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it is fast and lightweight .\n->it is fast and lightweight .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'it', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: If you live in new york city , you 'll find better food at small restaurants outside of time square and spend half the amount .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you live in new york city , you 'll find better food at small restaurants outside of time square and spend half the amount .\n->", + "output": "{\"text\": \"If you live in new york city , you 'll find better food at small restaurants outside of time square and spend half the amount .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff is very good .\n->the staff is very good .\n[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: noodles with shrimp and chicken and coconut juice is the MUST !\n->noodles with shrimp and chicken and coconut juice is the MUST !\n[{'aspect': 'noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they did n't disappoint , service from the second i arrived at the door was extremely pleasant and attentive with almost one server per table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey did n't disappoint , service from the second i arrived at the door was extremely pleasant and attentive with almost one server per table .\n->", + "output": "{\"text\": \"they did n't disappoint , service from the second i arrived at the door was extremely pleasant and attentive with almost one server per table .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was pretty inexpensive too .\n->it was pretty inexpensive too .\n[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: it has been great for everything i ' ve done .\n->it has been great for everything i ' ve done .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I do n't know who they think they are but they have no respect for the residents of the neighborhood ever since they opened their cabaret next door and blasts loud music till three in the morning every weekend during the summer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI do n't know who they think they are but they have no respect for the residents of the neighborhood ever since they opened their cabaret next door and blasts loud music till three in the morning every weekend during the summer .\n->", + "output": "{\"text\": \"I do n't know who they think they are but they have no respect for the residents of the neighborhood ever since they opened their cabaret next door and blasts loud music till three in the morning every weekend during the summer .\", \"labels\": \"[{'aspect': 'music', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i absolutely love this chromebook !\n->i absolutely love this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: ca n ' t go wrong with an asus !\n->ca n ' t go wrong with an asus !\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only positive was the wait staff , which was prompt , knowledgable , and likeable .\n->", + "output": "{\"text\": \"The only positive was the wait staff , which was prompt , knowledgable , and likeable .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'knowledgable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'likeable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place would be so much better served by being run by a group that actually understands customer service .\n->This place would be so much better served by being run by a group that actually understands customer service .\n[{'aspect': 'service', 'opinion': 'would be so much better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it feels amazing and the travel is perfect .\n->it feels amazing and the travel is perfect .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'travel', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#PORTABILITY'}]\ntext: I am not a vegetarian but , almost all the dishes were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI am not a vegetarian but , almost all the dishes were great .\n->", + "output": "{\"text\": \"I am not a vegetarian but , almost all the dishes were great .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best . sushi . ever .\n->best . sushi . ever .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: honestly , i ' m debating returning this laptop .\n->honestly , i ' m debating returning this laptop .\n[{'aspect': 'laptop', 'opinion': 'debating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: I was very impressed by this low-key upper eastsider and their authentically thai cuisine ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI was very impressed by this low-key upper eastsider and their authentically thai cuisine ! ! !\n->", + "output": "{\"text\": \"I was very impressed by this low-key upper eastsider and their authentically thai cuisine ! ! !\", \"labels\": \"[{'aspect': 'thai cuisine', 'opinion': 'authentically', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apps have to close and be reopened to get them to a somewhat decent size .\n->apps have to close and be reopened to get them to a somewhat decent size .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: we were drawn into the belly dancing show that captivated the crowd .\n->we were drawn into the belly dancing show that captivated the crowd .\n[{'aspect': 'belly dancing show', 'opinion': 'captivated', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: For the location , the prices are very reasonable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor the location , the prices are very reasonable .\n->", + "output": "{\"text\": \"For the location , the prices are very reasonable .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'location', 'opinion': 'reasonable', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This big draw is the all you can sushi here for $ 19.95 !\n->This big draw is the all you can sushi here for $ 19.95 !\n[{'aspect': 'sushi', 'opinion': 'draw', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s been about 3 weeks since my purchase of my lenova laptop and figure it ' s time to give my all important review .\n->it ' s been about 3 weeks since my purchase of my lenova laptop and figure it ' s time to give my all important review .\n[{'aspect': 'lenova laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: The food is okay and the prices here are mediocre .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is okay and the prices here are mediocre .\n->", + "output": "{\"text\": \"The food is okay and the prices here are mediocre .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n->sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n[{'aspect': 'c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this guy refused to seat her and she left , followed shortly by the four of us , but not before i told him that in my 40 years of world travel , including paris , that i had never seen such a display of bad behavior by a frontman in a restaurant .\n->this guy refused to seat her and she left , followed shortly by the four of us , but not before i told him that in my 40 years of world travel , including paris , that i had never seen such a display of bad behavior by a frontman in a restaurant .\n[{'aspect': 'frontman', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n->", + "output": "{\"text\": \"My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'opposite', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Bagels are ok , but be sure not to make any special requests !\n->Bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: On a hot day it was fabulous to stop in and enjoy lunch .\n->On a hot day it was fabulous to stop in and enjoy lunch .\n[{'aspect': 'lunch', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We have been to this place many times , and always have great food , wine , and service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe have been to this place many times , and always have great food , wine , and service .\n->", + "output": "{\"text\": \"We have been to this place many times , and always have great food , wine , and service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n->( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n[{'aspect': 'it', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n->The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n[{'aspect': 'hanger steak', 'opinion': 'rubber', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tuna', 'opinion': 'flavorless', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Not a typical pizza joint , but good for a low key and fairly cheap nice sit down dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot a typical pizza joint , but good for a low key and fairly cheap nice sit down dinner .\n->", + "output": "{\"text\": \"Not a typical pizza joint , but good for a low key and fairly cheap nice sit down dinner .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'cheap nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .\n->the waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The fish was really , really fresh .\n->The fish was really , really fresh .\n[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The corned beef was tender and melted in my mouth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe corned beef was tender and melted in my mouth .\n->", + "output": "{\"text\": \"The corned beef was tender and melted in my mouth .\", \"labels\": \"[{'aspect': 'corned beef', 'opinion': 'tender', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'corned beef', 'opinion': 'melted', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n->most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n[{'aspect': 'apps', 'opinion': 'ok', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: There is also very limited seating and there can be a substantial wait in getting food at peak times .\n->There is also very limited seating and there can be a substantial wait in getting food at peak times .\n[{'aspect': 'seating', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'substantial', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The best Chicken pad tai , I 've ever had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe best Chicken pad tai , I 've ever had .\n->", + "output": "{\"text\": \"The best Chicken pad tai , I 've ever had .\", \"labels\": \"[{'aspect': 'Chicken pad tai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n->overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n[{'aspect': 'computer', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: needless to say , android / play rollout is being managed very , very poorly .\n->needless to say , android / play rollout is being managed very , very poorly .\n[{'aspect': 'android / play rollout', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: $ 20 for all you can eat sushi can not be beaten .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n$ 20 for all you can eat sushi can not be beaten .\n->", + "output": "{\"text\": \"$ 20 for all you can eat sushi can not be beaten .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'can not be beaten', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at this price range it is a fine screen .\n->at this price range it is a fine screen .\n[{'aspect': 'screen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\nExample:\ntext: Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\n->Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\n[{'aspect': 'Cosette', 'opinion': 'off-the-beaten', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Haru serves very fresh fish , has a trendy , modern ambiance , prime location on Park Avenue South and friendly service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHaru serves very fresh fish , has a trendy , modern ambiance , prime location on Park Avenue South and friendly service .\n->", + "output": "{\"text\": \"Haru serves very fresh fish , has a trendy , modern ambiance , prime location on Park Avenue South and friendly service .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'trendy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'location', 'opinion': 'prime', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazon is 2 - day shipping me a replacement .\n->amazon is 2 - day shipping me a replacement .\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n->your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n[{'aspect': 'retina screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: Warm and friendly in the winter and terrific outdoor seating in the warmer months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWarm and friendly in the winter and terrific outdoor seating in the warmer months .\n->", + "output": "{\"text\": \"Warm and friendly in the winter and terrific outdoor seating in the warmer months .\", \"labels\": \"[{'aspect': 'outdoor seating', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n->My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n[{'aspect': 'food', 'opinion': 'ranting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'raving', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - track pad is accurate\n->- track pad is accurate\n[{'aspect': 'track pad', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nVery romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n->", + "output": "{\"text\": \"Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n->not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n[{'aspect': 'wait staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We had the pot-stickers which were great and a tempura dish that was great .\n->We had the pot-stickers which were great and a tempura dish that was great .\n[{'aspect': 'pot-stickers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tempura dish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We got a little tipsy from the sake but is n't that what Saturday nights with the girlfriends are all about ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe got a little tipsy from the sake but is n't that what Saturday nights with the girlfriends are all about ?\n->", + "output": "{\"text\": \"We got a little tipsy from the sake but is n't that what Saturday nights with the girlfriends are all about ?\", \"labels\": \"[{'aspect': 'sake', 'opinion': 'tipsy', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n->the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the customer support rep at amazon was of no help , though he did try .\n->the customer support rep at amazon was of no help , though he did try .\n[{'aspect': 'customer support rep', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: My turkey burger was not cooked at all , my friends salmon was completely raw .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy turkey burger was not cooked at all , my friends salmon was completely raw .\n->", + "output": "{\"text\": \"My turkey burger was not cooked at all , my friends salmon was completely raw .\", \"labels\": \"[{'aspect': 'turkey burger', 'opinion': 'not cooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'salmon', 'opinion': 'raw', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n->product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i love asus but this one is super slow !\n->i love asus but this one is super slow !\n[{'aspect': 'asus', 'opinion': 'love', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: His drinks are very inventive , delicious and classy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHis drinks are very inventive , delicious and classy .\n->", + "output": "{\"text\": \"His drinks are very inventive , delicious and classy .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'classy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n->you must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n[{'aspect': 'crabmeat lasagna', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chocolate bread pudding', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: decor is charming .\n->decor is charming .\n[{'aspect': 'decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: Great food , great prices , great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food , great prices , great service .\n->", + "output": "{\"text\": \"Great food , great prices , great service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pasta was well cooked , did n ' t have enough sauce though or flavor .\n->the pasta was well cooked , did n ' t have enough sauce though or flavor .\n[{'aspect': 'pasta', 'opinion': 'well cooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: so a for the computer , f for the charger , and a d for the customer support .\n->so a for the computer , f for the charger , and a d for the customer support .\n[{'aspect': 'computer', 'opinion': 'a', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'charger', 'opinion': 'f', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'customer support', 'opinion': 'd', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: Awesome Pizza especially the Margheritta slice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAwesome Pizza especially the Margheritta slice .\n->", + "output": "{\"text\": \"Awesome Pizza especially the Margheritta slice .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'Awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Margheritta', 'opinion': 'Awesome', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would recommend roxy ' s for that , but not for their food .\n->i would recommend roxy ' s for that , but not for their food .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: - battery life is pretty amazing at 10 - 11hrs\n->- battery life is pretty amazing at 10 - 11hrs\n[{'aspect': 'battery life', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: This place , however , has a lot less pretension than Joya and the Thai food is still above average .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place , however , has a lot less pretension than Joya and the Thai food is still above average .\n->", + "output": "{\"text\": \"This place , however , has a lot less pretension than Joya and the Thai food is still above average .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is a little smaller , but it ' s touch and even higher resolution .\n->the screen is a little smaller , but it ' s touch and even higher resolution .\n[{'aspect': 'screen', 'opinion': 'smaller', 'polarity': 'neutral', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'touch', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'higher', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: thought all apple products are over priced , i wouldn ' t have anything else .\n->thought all apple products are over priced , i wouldn ' t have anything else .\n[{'aspect': 'apple products', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'COMPANY#PRICE'}]\ntext: Try ordering from the regular menu , then you would not regret !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry ordering from the regular menu , then you would not regret !\n->", + "output": "{\"text\": \"Try ordering from the regular menu , then you would not regret !\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'regret', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the worst computer i have ever owned .\n->this is the worst computer i have ever owned .\n[{'aspect': 'computer', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - battery life is great .\n->- battery life is great .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n->", + "output": "{\"text\": \"We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\", \"labels\": \"[{'aspect': 'lox', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: third time in 5 months that the touchpad became unresponsive .\n->third time in 5 months that the touchpad became unresponsive .\n[{'aspect': 'touchpad', 'opinion': 'unresponsive', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: We have been to this place many times , and always have great food , wine , and service .\n->We have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is great .\n->", + "output": "{\"text\": \"Food is great .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: those rolls were big , but not good and sashimi was n't fresh .\n->those rolls were big , but not good and sashimi was n't fresh .\n[{'aspect': 'sashimi', 'opinion': \"was n't fresh\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this laptop exceeds my expectations for a mid - price laptop .\n->this laptop exceeds my expectations for a mid - price laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I recommend the meatballs and caprese salad and the beans on toast were a wonderful start to the meal !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recommend the meatballs and caprese salad and the beans on toast were a wonderful start to the meal !\n->", + "output": "{\"text\": \"I recommend the meatballs and caprese salad and the beans on toast were a wonderful start to the meal !\", \"labels\": \"[{'aspect': 'meatballs', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caprese salad', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beans on toast', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'wonderful', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one , the display is gorgeous ; watching video is a treat .\n->one , the display is gorgeous ; watching video is a treat .\n[{'aspect': 'display', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'treat', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n->All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n[{'aspect': 'appetizers', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'watering', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place is incredibly tiny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is incredibly tiny .\n->", + "output": "{\"text\": \"This place is incredibly tiny .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hold on before you decide to pay $ 2500 for this laptop .\n->hold on before you decide to pay $ 2500 for this laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i bought this chromebook on the prime day deal , but even without $ 60 off it would still have been worth the money .\n->i bought this chromebook on the prime day deal , but even without $ 60 off it would still have been worth the money .\n[{'aspect': 'chromebook', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: I did n't complain , I liked the atmosphere so much .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI did n't complain , I liked the atmosphere so much .\n->", + "output": "{\"text\": \"I did n't complain , I liked the atmosphere so much .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer ' s customer service is by far the worst .\n->acer ' s customer service is by far the worst .\n[{'aspect': \"acer ' s customer service\", 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n->in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'chrome os', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\ntext: The wine is always good , the tapas are always yummy , especially with the warm pita bread .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine is always good , the tapas are always yummy , especially with the warm pita bread .\n->", + "output": "{\"text\": \"The wine is always good , the tapas are always yummy , especially with the warm pita bread .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tapas', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pita bread', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good looking laptop but hardware has several major problems .\n->good looking laptop but hardware has several major problems .\n[{'aspect': 'laptop', 'opinion': 'good looking', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'hardware', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n->On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: But when you are seated the waitresses are great , they explain everything on the menu , and the price of the food is really cheap for the service you get .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut when you are seated the waitresses are great , they explain everything on the menu , and the price of the food is really cheap for the service you get .\n->", + "output": "{\"text\": \"But when you are seated the waitresses are great , they explain everything on the menu , and the price of the food is really cheap for the service you get .\", \"labels\": \"[{'aspect': 'waitresses', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n->The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n[{'aspect': 'waitstaff', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: It is definitely a good spot for snacks and chat .\n->It is definitely a good spot for snacks and chat .\n[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I 've been to several places for Dim Sum and this has got to be the WORST .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've been to several places for Dim Sum and this has got to be the WORST .\n->", + "output": "{\"text\": \"I 've been to several places for Dim Sum and this has got to be the WORST .\", \"labels\": \"[{'aspect': 'Dim Sum', 'opinion': 'WORST .', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchscreen is great though and feels very intuitive .\n->the touchscreen is great though and feels very intuitive .\n[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: Cheese plate is a varied delight and great bargain at $ 10 .\n->Cheese plate is a varied delight and great bargain at $ 10 .\n[{'aspect': 'Cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The freshest , best variety , and the fastest delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe freshest , best variety , and the fastest delivery .\n->", + "output": "{\"text\": \"The freshest , best variety , and the fastest delivery .\", \"labels\": \"[{'aspect': 'variety', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Bartender was unable to tear himself away from friends at bar .\n->Bartender was unable to tear himself away from friends at bar .\n[{'aspect': 'Bartender', 'opinion': 'unable to tear', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: I recently tried Suan and I thought that it was great .\n->I recently tried Suan and I thought that it was great .\n[{'aspect': 'Suan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Go to Volare for 1st class service and terrific food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGo to Volare for 1st class service and terrific food .\n->", + "output": "{\"text\": \"Go to Volare for 1st class service and terrific food .\", \"labels\": \"[{'aspect': 'service', 'opinion': '1st class', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: track pad is passable .\n->track pad is passable .\n[{'aspect': 'track pad', 'opinion': 'passable', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: that is awesome .\n->that is awesome .\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\ntext: The sandwiches are dry , tasteless and way overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sandwiches are dry , tasteless and way overpriced .\n->", + "output": "{\"text\": \"The sandwiches are dry , tasteless and way overpriced .\", \"labels\": \"[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try green curry with vegetables .\n->Try green curry with vegetables .\n[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n->- although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen ratio', 'opinion': 'not optimal', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: The fried rice is really good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fried rice is really good too .\n->", + "output": "{\"text\": \"The fried rice is really good too .\", \"labels\": \"[{'aspect': 'fried rice', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The burger was great , also .\n->The burger was great , also .\n[{'aspect': 'burger', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is bright and beautiful .\n->the screen is bright and beautiful .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: We were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections .\n->", + "output": "{\"text\": \"We were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'enthusiastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'manager', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n->i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n[{'aspect': 'device', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: 2 stars taken for horrible sound quality\n->2 stars taken for horrible sound quality\n[{'aspect': 'sound quality', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: Made my dining experience uncomfortable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMade my dining experience uncomfortable .\n->", + "output": "{\"text\": \"Made my dining experience uncomfortable .\", \"labels\": \"[{'aspect': 'dining experience', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: be prepared to wait , because the place is pretty tiny .\n->be prepared to wait , because the place is pretty tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: after the 4th time i asked again and the waiter than said after our dinner .\n->after the 4th time i asked again and the waiter than said after our dinner .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: A very inviting restaurant , with friendly service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA very inviting restaurant , with friendly service .\n->", + "output": "{\"text\": \"A very inviting restaurant , with friendly service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: buyer beware - computer is complete trash .\n->buyer beware - computer is complete trash .\n[{'aspect': 'computer', 'opinion': 'trash', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: good spreads , great beverage selections and bagels really tasty .\n->good spreads , great beverage selections and bagels really tasty .\n[{'aspect': 'spreads', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beverage selections', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'bagels', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: We also had shared a house salad that was fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe also had shared a house salad that was fresh .\n->", + "output": "{\"text\": \"We also had shared a house salad that was fresh .\", \"labels\": \"[{'aspect': 'house salad', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n->it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n[{'aspect': 'chrome os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'glossy', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n->this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The ceiling is amazing !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ceiling is amazing !\n->", + "output": "{\"text\": \"The ceiling is amazing !\", \"labels\": \"[{'aspect': 'ceiling', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - trackpad is too finicky and not my favorite\n->- trackpad is too finicky and not my favorite\n[{'aspect': 'trackpad', 'opinion': 'finicky', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n->Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch buffet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Not a small feat for good french food in the area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot a small feat for good french food in the area .\n->", + "output": "{\"text\": \"Not a small feat for good french food in the area .\", \"labels\": \"[{'aspect': 'french food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when it does run it runs great .\n->when it does run it runs great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: every time i have a special occasion with my boyfriend i have a hard time going anywhere else .\n->every time i have a special occasion with my boyfriend i have a hard time going anywhere else .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: The space is a bit too small for live music , so on jazz nights , it can be loud and cramped .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe space is a bit too small for live music , so on jazz nights , it can be loud and cramped .\n->", + "output": "{\"text\": \"The space is a bit too small for live music , so on jazz nights , it can be loud and cramped .\", \"labels\": \"[{'aspect': 'space', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'jazz nights', 'opinion': 'loud', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'jazz nights', 'opinion': 'cramped', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n->they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: The only fallback on this restaurant is the prices .\n->The only fallback on this restaurant is the prices .\n[{'aspect': 'prices', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Big Wong gets big Ups for a fine establishment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBig Wong gets big Ups for a fine establishment .\n->", + "output": "{\"text\": \"Big Wong gets big Ups for a fine establishment .\", \"labels\": \"[{'aspect': 'establishment', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: decor is charming .\n->decor is charming .\n[{'aspect': 'decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: superb value for money and powerful performance from this quad core computer .\n->superb value for money and powerful performance from this quad core computer .\n[{'aspect': 'quad core computer', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'quad core computer', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: The entree was bland and small , dessert was not inspired .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe entree was bland and small , dessert was not inspired .\n->", + "output": "{\"text\": \"The entree was bland and small , dessert was not inspired .\", \"labels\": \"[{'aspect': 'entree', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'entree', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'not inspired', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n->The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'not over-bearing or rushed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff was the friendliest that have seen in New York .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff was the friendliest that have seen in New York .\n->", + "output": "{\"text\": \"The staff was the friendliest that have seen in New York .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great and reasonably priced .\n->The food is great and reasonably priced .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it was fully charged and i only turned it on a total of 3 times before the screen went blank .\n->it was fully charged and i only turned it on a total of 3 times before the screen went blank .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'the screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWith the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->", + "output": "{\"text\": \"With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'pepper', 'opinion': 'much', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cheese plate is a varied delight and great bargain at $ 10 .\n->Cheese plate is a varied delight and great bargain at $ 10 .\n[{'aspect': 'Cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - for such a premium price , this is definitely the worst purchase quality wise that i have made .\n->- for such a premium price , this is definitely the worst purchase quality wise that i have made .\n[{'aspect': 'NULL', 'opinion': 'premium', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n->", + "output": "{\"text\": \"The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\", \"labels\": \"[{'aspect': 'thai food', 'opinion': 'better', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i find this chrome book is so much easy to use , it start up fast it is light weight ready to carry when traveling .\n->i find this chrome book is so much easy to use , it start up fast it is light weight ready to carry when traveling .\n[{'aspect': 'chrome book', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'chrome book', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chrome book', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: - this chromebook has access to the android beta channel for android apps\n->- this chromebook has access to the android beta channel for android apps\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Delicious food at a great price but do not go here on a cold day and sit by the front door .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDelicious food at a great price but do not go here on a cold day and sit by the front door .\n->", + "output": "{\"text\": \"Delicious food at a great price but do not go here on a cold day and sit by the front door .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'front door', 'opinion': 'cold', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service was friendly and the atmosphere was casual .\n->the service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it just is missing a lot of other features that i wanted in a notebook\n->it just is missing a lot of other features that i wanted in a notebook\n[{'aspect': 'notebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOverall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n->", + "output": "{\"text\": \"Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + nice , large screen\n->+ nice , large screen\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: this is a great laptop .\n->this is a great laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Pizza was a little soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPizza was a little soggy .\n->", + "output": "{\"text\": \"Pizza was a little soggy .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: an unexpected benefit for me has been the iphone / mbp integration .\n->an unexpected benefit for me has been the iphone / mbp integration .\n[{'aspect': 'iphone / mbp integration', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: lobster was good , nothing spectacular .\n->lobster was good , nothing spectacular .\n[{'aspect': 'lobster', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'nothing spectacular', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n->", + "output": "{\"text\": \"The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\", \"labels\": \"[{'aspect': 'bhelpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sevpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'samosa chaats', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bombay style chaat', 'opinion': 'famous scrumptious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n->5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n[{'aspect': 'emmc storage', 'opinion': 'slower', 'polarity': 'negative', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The all-Italian staff is warm and engaging from the start .\n->The all-Italian staff is warm and engaging from the start .\n[{'aspect': 'staff', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'engaging', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The only problem is that the manager is a complete incompetent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only problem is that the manager is a complete incompetent .\n->", + "output": "{\"text\": \"The only problem is that the manager is a complete incompetent .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s not real fast and it doesn ' t have a lot of storage .\n->it ' s not real fast and it doesn ' t have a lot of storage .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: We were worried we would have trouble getting in , but somehow managed to have a short wait .\n->We were worried we would have trouble getting in , but somehow managed to have a short wait .\n[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The wine list is interesting and has many good values .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is interesting and has many good values .\n->", + "output": "{\"text\": \"The wine list is interesting and has many good values .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'good values', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The noise level was unbearable , conversation impossible .\n->The noise level was unbearable , conversation impossible .\n[{'aspect': 'noise level', 'opinion': 'unbearable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the price very reasonable .\n->the price very reasonable .\n[{'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: Definite go if you 're used to good Indian restaurant food from abroad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDefinite go if you 're used to good Indian restaurant food from abroad .\n->", + "output": "{\"text\": \"Definite go if you 're used to good Indian restaurant food from abroad .\", \"labels\": \"[{'aspect': 'Indian restaurant food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: priced at upper intermediate range .\n->priced at upper intermediate range .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n->my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: By far , the best pizza in Manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBy far , the best pizza in Manhattan .\n->", + "output": "{\"text\": \"By far , the best pizza in Manhattan .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the body of the chromebook feels solid due to the aluminium body .\n->the body of the chromebook feels solid due to the aluminium body .\n[{'aspect': 'body of the chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: thin , light , cool are what i feel when holding it and carry around .\n->thin , light , cool are what i feel when holding it and carry around .\n[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDecent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n->", + "output": "{\"text\": \"Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - battery life is a bit short after some gaming\n->- battery life is a bit short after some gaming\n[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: very let down by the reliability of this machine .\n->very let down by the reliability of this machine .\n[{'aspect': 'machine', 'opinion': 'let down', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: The food is mostly made from scratch , fresh and well prepared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is mostly made from scratch , fresh and well prepared .\n->", + "output": "{\"text\": \"The food is mostly made from scratch , fresh and well prepared .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n->- i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i just do n ' t understand all the hype . . .\n->i just do n ' t understand all the hype . . .\n[{'aspect': 'NULL', 'opinion': 'hype', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: NO more reservations , expensive tips and annoying stuff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNO more reservations , expensive tips and annoying stuff .\n->", + "output": "{\"text\": \"NO more reservations , expensive tips and annoying stuff .\", \"labels\": \"[{'aspect': 'reservations', 'opinion': 'NO more', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tips', 'opinion': 'expensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'stuff', 'opinion': 'annoying', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google locked me out because after an update , my keyboard output was not as it should have been ( some keys were inverted ) .\n->google locked me out because after an update , my keyboard output was not as it should have been ( some keys were inverted ) .\n[{'aspect': 'keyboard output', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: but when we looked at the menu , there were n ' t a lot of choices , most of them were dumplings in the appetizer section .\n->but when we looked at the menu , there were n ' t a lot of choices , most of them were dumplings in the appetizer section .\n[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: $ 20 gets you unlimited sushi of a very high quality -- I even took a friend here from Japan who said it was one of the best sushi places in the US that he has been to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n$ 20 gets you unlimited sushi of a very high quality -- I even took a friend here from Japan who said it was one of the best sushi places in the US that he has been to .\n->", + "output": "{\"text\": \"$ 20 gets you unlimited sushi of a very high quality -- I even took a friend here from Japan who said it was one of the best sushi places in the US that he has been to .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'unlimited', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi places', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality', 'opinion': 'high', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the kind of place you ' d like to take all your friends to and still keep a secret .\n->this is the kind of place you ' d like to take all your friends to and still keep a secret .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the flavors are amazing and the value is phenomenal .\n->the flavors are amazing and the value is phenomenal .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: Sushi was n't anything spectacular for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSushi was n't anything spectacular for the price .\n->", + "output": "{\"text\": \"Sushi was n't anything spectacular for the price .\", \"labels\": \"[{'aspect': 'Sushi', 'opinion': 'spectacular', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i agree that dining at casa la femme is like no other dining experience !\n->i agree that dining at casa la femme is like no other dining experience !\n[{'aspect': 'casa la femme', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .\n->i have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .\n[{'aspect': 'lobster roll', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: But they do n't have a toaster , which is strange .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut they do n't have a toaster , which is strange .\n->", + "output": "{\"text\": \"But they do n't have a toaster , which is strange .\", \"labels\": \"[{'aspect': 'toaster', 'opinion': 'strange', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the playstore its immature .\n->the playstore its immature .\n[{'aspect': 'playstore', 'opinion': 'immature', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: So , for good food i 'd recommend it , but not for a fun night out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSo , for good food i 'd recommend it , but not for a fun night out .\n->", + "output": "{\"text\": \"So , for good food i 'd recommend it , but not for a fun night out .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n->- i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is a little slow at times though .\n->it is a little slow at times though .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The menu choices are similar but the taste lacked more flavor than it looked .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu choices are similar but the taste lacked more flavor than it looked .\n->", + "output": "{\"text\": \"The menu choices are similar but the taste lacked more flavor than it looked .\", \"labels\": \"[{'aspect': 'taste', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu choices', 'opinion': 'similar', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is decent even when this small place is packed .\n->The service is decent even when this small place is packed .\n[{'aspect': 'service', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'packed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i can not imagine better indian food in all of the city .\n->i can not imagine better indian food in all of the city .\n[{'aspect': 'indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: A must for all the Dosa lovers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA must for all the Dosa lovers .\n->", + "output": "{\"text\": \"A must for all the Dosa lovers .\", \"labels\": \"[{'aspect': 'Dosa', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The main downside to the place is the nazi-like guy running it who constantly complains about the noise level .\n->The main downside to the place is the nazi-like guy running it who constantly complains about the noise level .\n[{'aspect': 'noise level', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'guy', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: in summary , it looks great and performs very well .\n->in summary , it looks great and performs very well .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n->", + "output": "{\"text\": \"The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I like the ambience , it 's very dark and original .\n->I like the ambience , it 's very dark and original .\n[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: on the upside , the internet is lightning fast and it interfaces with tv through hdmi which is great , is bluetooth compatible and has two usb ports .\n->on the upside , the internet is lightning fast and it interfaces with tv through hdmi which is great , is bluetooth compatible and has two usb ports .\n[{'aspect': 'internet', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#USABILITY'}, {'aspect': 'hdmi', 'opinion': 'great', 'polarity': 'positive', 'category': 'PORTS#PORTABILITY'}]\ntext: The appetizers are just OK and the main courses were decidedly subpar .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe appetizers are just OK and the main courses were decidedly subpar .\n->", + "output": "{\"text\": \"The appetizers are just OK and the main courses were decidedly subpar .\", \"labels\": \"[{'aspect': 'appetizers', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'main courses', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: super off balance with respect to screen .\n->super off balance with respect to screen .\n[{'aspect': 'screen', 'opinion': 'off balance', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: it ' s okay for email , facebook , surfing the net , etc .\n->it ' s okay for email , facebook , surfing the net , etc .\n[{'aspect': 'email', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'facebook', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\ntext: The exotic food is beautifully presented and is a delight in delicious combinations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe exotic food is beautifully presented and is a delight in delicious combinations .\n->", + "output": "{\"text\": \"The exotic food is beautifully presented and is a delight in delicious combinations .\", \"labels\": \"[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - android apps and google play store are real game changers for the chromeos landscape .\n->- android apps and google play store are real game changers for the chromeos landscape .\n[{'aspect': 'google play store', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n->i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The staff is very attentive and we can almost always get a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is very attentive and we can almost always get a table .\n->", + "output": "{\"text\": \"The staff is very attentive and we can almost always get a table .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: La Rosa waltzes in , and I think they are doing it the best .\n->La Rosa waltzes in , and I think they are doing it the best .\n[{'aspect': 'La Rosa', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: so far , this chromebook is fantastic .\n->so far , this chromebook is fantastic .\n[{'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Try the mediterranean salad , it is a true experience for your taste buds ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the mediterranean salad , it is a true experience for your taste buds ! !\n->", + "output": "{\"text\": \"Try the mediterranean salad , it is a true experience for your taste buds ! !\", \"labels\": \"[{'aspect': 'mediterranean salad', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n->most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: they should have called it mascarpone with chocolate chips - good but a far cry from what the name implies .\n->they should have called it mascarpone with chocolate chips - good but a far cry from what the name implies .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: People are always friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPeople are always friendly .\n->", + "output": "{\"text\": \"People are always friendly .\", \"labels\": \"[{'aspect': 'People', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was good not great not worth the wait or another visit\n->Food was good not great not worth the wait or another visit\n[{'aspect': 'wait', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: worth visiting the 1st ave spot because it is the original store .\n->worth visiting the 1st ave spot because it is the original store .\n[{'aspect': '1st ave spot', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: The cream cheeses are out of this world and I love that coffee ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe cream cheeses are out of this world and I love that coffee ! !\n->", + "output": "{\"text\": \"The cream cheeses are out of this world and I love that coffee ! !\", \"labels\": \"[{'aspect': 'cream cheeses', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'coffee', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Because of the delicate thin crust , take-out pies get soggy in their boxes .\n->Because of the delicate thin crust , take-out pies get soggy in their boxes .\n[{'aspect': 'take-out pies', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'delicate', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: keep up the good work .\n->keep up the good work .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: From the entrees to the sides to the drinks , everything was creatively prepared yet still simple .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrom the entrees to the sides to the drinks , everything was creatively prepared yet still simple .\n->", + "output": "{\"text\": \"From the entrees to the sides to the drinks , everything was creatively prepared yet still simple .\", \"labels\": \"[{'aspect': 'entrees', 'opinion': 'creatively prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sides', 'opinion': 'creatively prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sides', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'creatively prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n->i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i noticed today the laptop was not charging anymore .\n->i noticed today the laptop was not charging anymore .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Service was prompt and courteous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was prompt and courteous .\n->", + "output": "{\"text\": \"Service was prompt and courteous .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Plus , on Wednesday nights the house wine is unlimited !\n->Plus , on Wednesday nights the house wine is unlimited !\n[{'aspect': 'house wine', 'opinion': 'unlimited', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i could not be happier with this computer because i am not the best person with technology .\n->i could not be happier with this computer because i am not the best person with technology .\n[{'aspect': 'computer', 'opinion': 'happier', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: It was not above ordinary and the beef version had cheap ( undercooked ) beef .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt was not above ordinary and the beef version had cheap ( undercooked ) beef .\n->", + "output": "{\"text\": \"It was not above ordinary and the beef version had cheap ( undercooked ) beef .\", \"labels\": \"[{'aspect': 'beef version', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'beef version', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n->i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n[{'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Chance is a small cozy restaurant , with a romantic feel to it , the decor is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nChance is a small cozy restaurant , with a romantic feel to it , the decor is great .\n->", + "output": "{\"text\": \"Chance is a small cozy restaurant , with a romantic feel to it , the decor is great .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place blew me away ... by far my new favorite restaurant on the uppereast side .\n->This place blew me away ... by far my new favorite restaurant on the uppereast side .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my favorite place lol\n->my favorite place lol\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Only wine and beer are served , but the house varities are actually quite good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOnly wine and beer are served , but the house varities are actually quite good .\n->", + "output": "{\"text\": \"Only wine and beer are served , but the house varities are actually quite good .\", \"labels\": \"[{'aspect': 'house varities', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i call acer support and after an hour they can not help me .\n->i call acer support and after an hour they can not help me .\n[{'aspect': 'acer support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: it ' s the only place you can get yummy authentic japanese comfort food .\n->it ' s the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'japanese comfort food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: we split a tasty vegetable samosa and the malai tikka wrap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe split a tasty vegetable samosa and the malai tikka wrap .\n->", + "output": "{\"text\": \"we split a tasty vegetable samosa and the malai tikka wrap .\", \"labels\": \"[{'aspect': 'vegetable samosa', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'malai tikka wrap', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fan blows like crazy and it makes so much noise .\n->the fan blows like crazy and it makes so much noise .\n[{'aspect': 'fan', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\nExample:\ntext: the flavors are amazing and the value is phenomenal .\n->the flavors are amazing and the value is phenomenal .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: Knowledge of the chef and the waitress are below average .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nKnowledge of the chef and the waitress are below average .\n->", + "output": "{\"text\": \"Knowledge of the chef and the waitress are below average .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: blackboard works fine for me .\n->blackboard works fine for me .\n[{'aspect': 'blackboard', 'opinion': 'fine', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .\n->The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'halibut special', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Decent wine selection too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDecent wine selection too .\n->", + "output": "{\"text\": \"Decent wine selection too .\", \"labels\": \"[{'aspect': 'wine selection', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service not the friendliest to our ` ` large party ' ' !\n->service not the friendliest to our ` ` large party ' ' !\n[{'aspect': 'service', 'opinion': 'not the friendliest', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: overall , i feel this is the best laptop i ' ve ever purchased or used .\n->overall , i feel this is the best laptop i ' ve ever purchased or used .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I have to say I have never had a disapointing meal here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have to say I have never had a disapointing meal here .\n->", + "output": "{\"text\": \"I have to say I have never had a disapointing meal here .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place was nice and calm .\n->the place was nice and calm .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'calm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: powered it down and back up a few time to check boot times and now ibhave huge black lines running down the screen .\n->powered it down and back up a few time to check boot times and now ibhave huge black lines running down the screen .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: If you want a casual neighborhood bistro that has great food and excellent service , this is the place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you want a casual neighborhood bistro that has great food and excellent service , this is the place .\n->", + "output": "{\"text\": \"If you want a casual neighborhood bistro that has great food and excellent service , this is the place .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: big screen !\n->big screen !\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: will never buy an msi product again , and will tell every person i know to stay far away .\n->will never buy an msi product again , and will tell every person i know to stay far away .\n[{'aspect': 'msi product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: great place to go for a drink too because they have 100 kinds of beer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat place to go for a drink too because they have 100 kinds of beer .\n->", + "output": "{\"text\": \"great place to go for a drink too because they have 100 kinds of beer .\", \"labels\": \"[{'aspect': 'drink', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thought all apple products are over priced , i wouldn ' t have anything else .\n->thought all apple products are over priced , i wouldn ' t have anything else .\n[{'aspect': 'apple products', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'COMPANY#PRICE'}]\nExample:\ntext: it boots up instantaneously .\n->it boots up instantaneously .\n[{'aspect': 'boots up', 'opinion': 'instantaneously', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMeanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n->", + "output": "{\"text\": \"Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\", \"labels\": \"[{'aspect': 'turnip soup with pureed basil', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but just at first glance , this thing is top quality .\n->but just at first glance , this thing is top quality .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i ' m in the us and asus wanted to charge me $ 60 cad for diagnosis only , and then said it would be an additional estimated $ 315 for repair .\n->i ' m in the us and asus wanted to charge me $ 60 cad for diagnosis only , and then said it would be an additional estimated $ 315 for repair .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: The food was well prepared and the service impecable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was well prepared and the service impecable .\n->", + "output": "{\"text\": \"The food was well prepared and the service impecable .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n->Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n[{'aspect': 'Pizza', 'opinion': 'Outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: always a nice crowd , but never loud .\n->always a nice crowd , but never loud .\n[{'aspect': 'crowd', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'crowd', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n->", + "output": "{\"text\": \"BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\", \"labels\": \"[{'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai spiced curry noodles with shrimp', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a wonderful device .\n->this is a wonderful device .\n[{'aspect': 'device', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food here does a great service to the name ( cantonese that is . . . ) .\n->the food here does a great service to the name ( cantonese that is . . . ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Good , dark atmosphere and the music is a nice touch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood , dark atmosphere and the music is a nice touch .\n->", + "output": "{\"text\": \"Good , dark atmosphere and the music is a nice touch .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'nice touch', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n->returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n->Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n[{'aspect': 'food suggestions', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: But the thai is definitely not great -- bland and undistinguished .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the thai is definitely not great -- bland and undistinguished .\n->", + "output": "{\"text\": \"But the thai is definitely not great -- bland and undistinguished .\", \"labels\": \"[{'aspect': 'thai', 'opinion': 'not great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'thai', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'thai', 'opinion': 'undistinguished', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wine list selection is good and wine - by - the - glass was generously filled to the top .\n->wine list selection is good and wine - by - the - glass was generously filled to the top .\n[{'aspect': 'wine list selection', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine - by - the - glass', 'opinion': 'generously filled', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: The duck confit is always amazing and the foie gras terrine with figs was out of this world .\n->The duck confit is always amazing and the foie gras terrine with figs was out of this world .\n[{'aspect': 'foie gras terrine with figs', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck confit', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great for groups , great for a date , great for early brunch or a nightcap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat for groups , great for a date , great for early brunch or a nightcap .\n->", + "output": "{\"text\": \"Great for groups , great for a date , great for early brunch or a nightcap .\", \"labels\": \"[{'aspect': 'brunch', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'nightcap', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love the mac laptops , they are durable , reliable , light with all day battery and have awesome designs .\n->i love the mac laptops , they are durable , reliable , light with all day battery and have awesome designs .\n[{'aspect': 'mac laptops', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'mac laptops', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac laptops', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac laptops', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#DESIGN_FEATURES'}, {'aspect': 'designs', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n->In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n->", + "output": "{\"text\": \"Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\", \"labels\": \"[{'aspect': 'food suggestions', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first ever macbook and i will never go back .\n->this is my first ever macbook and i will never go back .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: google is amazing .\n->google is amazing .\n[{'aspect': 'google', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: While the food was good ( certainly no Il Mulino ) the service was horrendous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the food was good ( certainly no Il Mulino ) the service was horrendous .\n->", + "output": "{\"text\": \"While the food was good ( certainly no Il Mulino ) the service was horrendous .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrendous', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: power cord broke within the first two weeks of use .\n->power cord broke within the first two weeks of use .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: Excellent dumplings served amid clean , chic decor .\n->Excellent dumplings served amid clean , chic decor .\n[{'aspect': 'dumplings', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'clean', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'chic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service is not what one would expect from a joint in this price category .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is not what one would expect from a joint in this price category .\n->", + "output": "{\"text\": \"Service is not what one would expect from a joint in this price category .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'price category', 'opinion': 'expect', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n->so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: we are very happy with the lenovo laptop .\n->we are very happy with the lenovo laptop .\n[{'aspect': 'lenovo laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i would recommend reservations on weekends though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would recommend reservations on weekends though .\n->", + "output": "{\"text\": \"i would recommend reservations on weekends though .\", \"labels\": \"[{'aspect': 'reservations', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: support got quite unpleasant when i ask about replacement .\n->support got quite unpleasant when i ask about replacement .\n[{'aspect': 'support', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: there are only two cons , the sound quality and the overheating .\n->there are only two cons , the sound quality and the overheating .\n[{'aspect': 'sound quality', 'opinion': 'cons', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'overheating', 'opinion': 'cons', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Great pizza for lunch place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat pizza for lunch place .\n->", + "output": "{\"text\": \"Great pizza for lunch place .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n->One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n[{'aspect': 'waiter', 'opinion': 'snobby', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The food is inventive but still keeps traditional indian flavoring .\n->The food is inventive but still keeps traditional indian flavoring .\n[{'aspect': 'food', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We were a group of 8 and well seved .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were a group of 8 and well seved .\n->", + "output": "{\"text\": \"We were a group of 8 and well seved .\", \"labels\": \"[{'aspect': 'seved', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the rma process needs improvement - buyer must pay to return the product for repair .\n->- the rma process needs improvement - buyer must pay to return the product for repair .\n[{'aspect': 'rma process', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n->", + "output": "{\"text\": \"I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\", \"labels\": \"[{'aspect': 'scallop roll', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just made the move from pc to macbook !\n->just made the move from pc to macbook !\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n->small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Best dish is nori-wrapped tuna .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest dish is nori-wrapped tuna .\n->", + "output": "{\"text\": \"Best dish is nori-wrapped tuna .\", \"labels\": \"[{'aspect': 'nori-wrapped tuna', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i thought it was a bad hdmi connection .\n->i thought it was a bad hdmi connection .\n[{'aspect': 'hdmi connection', 'opinion': 'bad', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: Other than being a little crowded and a bit overpriced , the atmosphere is filled with energy ( and the beautiful people of course ) and the food was surprising good !\n->Other than being a little crowded and a bit overpriced , the atmosphere is filled with energy ( and the beautiful people of course ) and the food was surprising good !\n[{'aspect': 'atmosphere', 'opinion': 'energy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'surprising good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was absolutely amazing ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was absolutely amazing ! !\n->", + "output": "{\"text\": \"The food was absolutely amazing ! !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n->While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n[{'aspect': 'drinks', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - excellent build quality - aluminium case is solid and has a premium feel\n->- excellent build quality - aluminium case is solid and has a premium feel\n[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminium case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminium case', 'opinion': 'premium', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: An excellent alternative to fast food joints and ordering in but , the food was slightly disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAn excellent alternative to fast food joints and ordering in but , the food was slightly disappointing .\n->", + "output": "{\"text\": \"An excellent alternative to fast food joints and ordering in but , the food was slightly disappointing .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worst place on smith street in brooklyn\n->worst place on smith street in brooklyn\n[{'aspect': 'place', 'opinion': 'worst', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Try ordering from the regular menu , then you would not regret !\n->Try ordering from the regular menu , then you would not regret !\n[{'aspect': 'menu', 'opinion': 'regret', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Mermaid Inn is an overall good restaurant with really good seafood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMermaid Inn is an overall good restaurant with really good seafood .\n->", + "output": "{\"text\": \"Mermaid Inn is an overall good restaurant with really good seafood .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lenovo has not disappointed with their products .\n->lenovo has not disappointed with their products .\n[{'aspect': 'lenovo', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'products', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: setting it up is awkward because it ' s chrome .\n->setting it up is awkward because it ' s chrome .\n[{'aspect': 'chrome', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: The staff is also attentive and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is also attentive and friendly .\n->", + "output": "{\"text\": \"The staff is also attentive and friendly .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this has worked great to overcome that problem .\n->this has worked great to overcome that problem .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: but i fed up with the price it cost to upgrade the product as well as the software .\n->but i fed up with the price it cost to upgrade the product as well as the software .\n[{'aspect': 'NULL', 'opinion': 'fed up', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: That is a problem since we paid about 20 bucks a dish , and had to order 5 dishes to get a decent taste .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThat is a problem since we paid about 20 bucks a dish , and had to order 5 dishes to get a decent taste .\n->", + "output": "{\"text\": \"That is a problem since we paid about 20 bucks a dish , and had to order 5 dishes to get a decent taste .\", \"labels\": \"[{'aspect': 'taste', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambiance -- relaxed and stylish .\n->Ambiance -- relaxed and stylish .\n[{'aspect': 'Ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: moderate prices .\n->moderate prices .\n[{'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\ntext: They were such a rip-off ( $ 8.95 for four small meat patties in steamed buns ) and not worth trying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey were such a rip-off ( $ 8.95 for four small meat patties in steamed buns ) and not worth trying .\n->", + "output": "{\"text\": \"They were such a rip-off ( $ 8.95 for four small meat patties in steamed buns ) and not worth trying .\", \"labels\": \"[{'aspect': 'meat patties in steamed buns', 'opinion': 'rip-off', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meat patties in steamed buns', 'opinion': 'not worth trying', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n->the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n[{'aspect': 'aluminum casing', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: The only fallback on this restaurant is the prices .\n->The only fallback on this restaurant is the prices .\n[{'aspect': 'prices', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'NULL'}]\ntext: All of the pizzas are terrific and the price is even better !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll of the pizzas are terrific and the price is even better !\n->", + "output": "{\"text\": \"All of the pizzas are terrific and the price is even better !\", \"labels\": \"[{'aspect': 'pizzas', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n->At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n[{'aspect': 'broth with noodles', 'opinion': 'mild', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - fully charges in about an hour\n->- fully charges in about an hour\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: It is about FOOD and Ambiance , and imagine how dreadful it will be it we only had to listen to an idle engine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is about FOOD and Ambiance , and imagine how dreadful it will be it we only had to listen to an idle engine .\n->", + "output": "{\"text\": \"It is about FOOD and Ambiance , and imagine how dreadful it will be it we only had to listen to an idle engine .\", \"labels\": \"[{'aspect': 'FOOD', 'opinion': 'dreadful', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'dreadful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice laptop for every day use , with some extra features .\n->nice laptop for every day use , with some extra features .\n[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: further , the msi dragon center ( default software on the installation ) caused a blue screen which lead to me having to factory reset .\n->further , the msi dragon center ( default software on the installation ) caused a blue screen which lead to me having to factory reset .\n[{'aspect': 'msi dragon center (', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: The food was good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was good too .\n->", + "output": "{\"text\": \"The food was good too .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is where it really really gets bad : the manager said , there is absolutely nothing we can do , it ' s a matter of taste that she did n ' t like it , and i can not comp it .\n->this is where it really really gets bad : the manager said , there is absolutely nothing we can do , it ' s a matter of taste that she did n ' t like it , and i can not comp it .\n[{'aspect': 'manager', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: however , when i switch back to laptop mode , the keyboard and trackpad are completely disabled and require a reboot to work again .\n->however , when i switch back to laptop mode , the keyboard and trackpad are completely disabled and require a reboot to work again .\n[{'aspect': 'keyboard', 'opinion': 'disabled', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'disabled', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: Admittedly , this is not the place for gigantic pieces of fish overflowing the plate ( and thank goodness , in my opinion ) but for simple , elegant sushi there is no better place in New York or anywhere in the US .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAdmittedly , this is not the place for gigantic pieces of fish overflowing the plate ( and thank goodness , in my opinion ) but for simple , elegant sushi there is no better place in New York or anywhere in the US .\n->", + "output": "{\"text\": \"Admittedly , this is not the place for gigantic pieces of fish overflowing the plate ( and thank goodness , in my opinion ) but for simple , elegant sushi there is no better place in New York or anywhere in the US .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n->nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n[{'aspect': 'mac os x', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: i have reservations about the all you can eat deal , however - - the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n->i have reservations about the all you can eat deal , however - - the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n[{'aspect': 'all you can eat deal', 'opinion': 'limited', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'all you can eat deal', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: The menu seemed to have a wide variety of dishes for seafood lovers and interesting ways of preparing them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu seemed to have a wide variety of dishes for seafood lovers and interesting ways of preparing them .\n->", + "output": "{\"text\": \"The menu seemed to have a wide variety of dishes for seafood lovers and interesting ways of preparing them .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'wide', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'variety of dishes', 'opinion': 'wide', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we are loving this chromebook !\n->we are loving this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n->the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n[{'aspect': 'ram', 'opinion': 'expandable', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\ntext: The white bean brushetta to start was incredible and the pasta was phenomenal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe white bean brushetta to start was incredible and the pasta was phenomenal .\n->", + "output": "{\"text\": \"The white bean brushetta to start was incredible and the pasta was phenomenal .\", \"labels\": \"[{'aspect': 'white bean brushetta', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n->Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n[{'aspect': 'Traditional French decour', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hall', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i love the screen quality and it is very fast for browsing .\n->i love the screen quality and it is very fast for browsing .\n[{'aspect': 'screen quality', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The rest of the menu is limited by everything is good eats .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe rest of the menu is limited by everything is good eats .\n->", + "output": "{\"text\": \"The rest of the menu is limited by everything is good eats .\", \"labels\": \"[{'aspect': 'eats', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: His drinks are very inventive , delicious and classy .\n->His drinks are very inventive , delicious and classy .\n[{'aspect': 'drinks', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'classy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: he has thoroughly enjoyed it\n->he has thoroughly enjoyed it\n[{'aspect': 'NULL', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Great food , great lay out and awesome service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food , great lay out and awesome service .\n->", + "output": "{\"text\": \"Great food , great lay out and awesome service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lay out', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they honored reservation on sunday afternoon very well .\n->they honored reservation on sunday afternoon very well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: nice little computer for the price .\n->nice little computer for the price .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: We walked in on a Wednesday night and were seated promptly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe walked in on a Wednesday night and were seated promptly .\n->", + "output": "{\"text\": \"We walked in on a Wednesday night and were seated promptly .\", \"labels\": \"[{'aspect': 'seated', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the screen was changing like creazy .\n->and the screen was changing like creazy .\n[{'aspect': 'screen', 'opinion': 'creazy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: twice in 10 days i had an issue , pointer , where they said turn it over and put a pin in it .\n->twice in 10 days i had an issue , pointer , where they said turn it over and put a pin in it .\n[{'aspect': 'pointer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'Out_Of_Scope#OPERATION_PERFORMANCE'}]\ntext: The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n->", + "output": "{\"text\": \"The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'freindly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also really like the finish on the case .\n->i also really like the finish on the case .\n[{'aspect': 'case', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n->I must say it 's a little pricey for the food because it was not as spectacular as the view .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n->", + "output": "{\"text\": \"The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\", \"labels\": \"[{'aspect': 'waitstaff', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n->After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n[{'aspect': 'chicken dish', 'opinion': 'complaining', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The flavors are great , and the menu is extensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe flavors are great , and the menu is extensive .\n->", + "output": "{\"text\": \"The flavors are great , and the menu is extensive .\", \"labels\": \"[{'aspect': 'flavors', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ordered the vitello alla marsala and i was pretty impressed .\n->i ordered the vitello alla marsala and i was pretty impressed .\n[{'aspect': 'vitello alla marsala', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I have been to Casimir over 5 times and I have always had a great time there .\n->I have been to Casimir over 5 times and I have always had a great time there .\n[{'aspect': 'Casimir', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food has been consistant for years and it never lets you down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food has been consistant for years and it never lets you down .\n->", + "output": "{\"text\": \"The food has been consistant for years and it never lets you down .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'consistant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great and they have a good selection of wines at reasonable prices .\n->The food is great and they have a good selection of wines at reasonable prices .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was slow , but the people were friendly .\n->service was slow , but the people were friendly .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: And forget what you read under me , the atmosphere is n't that bad either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd forget what you read under me , the atmosphere is n't that bad either .\n->", + "output": "{\"text\": \"And forget what you read under me , the atmosphere is n't that bad either .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': \"is n't that bad\", 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great wine list ( italian ) , good food , service was INITIALLY fine .\n->great wine list ( italian ) , good food , service was INITIALLY fine .\n[{'aspect': 'wine list', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after some googling i realized that the wifi issue was related to bluetooth being on .\n->after some googling i realized that the wifi issue was related to bluetooth being on .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n->", + "output": "{\"text\": \"We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'servants', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great for what i use it for .\n->great for what i use it for .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it is fantastic for the things that i need a computer for .\n->it is fantastic for the things that i need a computer for .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: sometimes i get good food and ok service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes i get good food and ok service .\n->", + "output": "{\"text\": \"sometimes i get good food and ok service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i barely do anything on it and it is a complete garbage can of a laptop .\n->i barely do anything on it and it is a complete garbage can of a laptop .\n[{'aspect': 'laptop', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: never fails to please .\n->never fails to please .\n[{'aspect': 'NULL', 'opinion': 'please', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: It 's a place for people who pay a lot for mediocre food , noise and a chance to be with their fellow bridge and tunnel folks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's a place for people who pay a lot for mediocre food , noise and a chance to be with their fellow bridge and tunnel folks .\n->", + "output": "{\"text\": \"It 's a place for people who pay a lot for mediocre food , noise and a chance to be with their fellow bridge and tunnel folks .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'noise', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: honestly , i ' m debating returning this laptop .\n->honestly , i ' m debating returning this laptop .\n[{'aspect': 'laptop', 'opinion': 'debating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: problem is nothing at prune is particularly memorable .\n->problem is nothing at prune is particularly memorable .\n[{'aspect': 'prune', 'opinion': 'memorable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAfter complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n->", + "output": "{\"text\": \"After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\", \"labels\": \"[{'aspect': 'chicken dish', 'opinion': 'complaining', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apps open instantly and the ac wifi performance is very nice .\n->apps open instantly and the ac wifi performance is very nice .\n[{'aspect': 'apps', 'opinion': 'instantly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'ac wifi', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the battery life is outstanding .\n->the battery life is outstanding .\n[{'aspect': 'battery life', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: The food was spicy and delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was spicy and delicious .\n->", + "output": "{\"text\": \"The food was spicy and delicious .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pacifico is a great place to casually hang out .\n->pacifico is a great place to casually hang out .\n[{'aspect': 'pacifico', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: this device would be a good choice if it weren ' t so poorly constructed .\n->this device would be a good choice if it weren ' t so poorly constructed .\n[{'aspect': 'device', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'poorly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: Bartender was unable to tear himself away from friends at bar .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBartender was unable to tear himself away from friends at bar .\n->", + "output": "{\"text\": \"Bartender was unable to tear himself away from friends at bar .\", \"labels\": \"[{'aspect': 'Bartender', 'opinion': 'unable to tear', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its cheap plastic and honestly , the keyboard its really bad .\n->its cheap plastic and honestly , the keyboard its really bad .\n[{'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: with so many poor experiences to be had in the theater district , is truly an excellent find !\n->with so many poor experiences to be had in the theater district , is truly an excellent find !\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n->", + "output": "{\"text\": \"Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n->it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n[{'aspect': 'NULL', 'opinion': 'limited', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'operating system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: cirspy crust margherita pizza\n->cirspy crust margherita pizza\n[{'aspect': 'margherita pizza', 'opinion': 'cirspy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'crust', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: This was my frist time at Cafe St. Bart 's and I must say how delicous the food and the service was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis was my frist time at Cafe St. Bart 's and I must say how delicous the food and the service was .\n->", + "output": "{\"text\": \"This was my frist time at Cafe St. Bart 's and I must say how delicous the food and the service was .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'delicous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n->great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: short charging cable .\n->short charging cable .\n[{'aspect': 'charging cable', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\ntext: One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOne would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n->", + "output": "{\"text\": \"One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'snobby', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But they do n't have a toaster , which is strange .\n->But they do n't have a toaster , which is strange .\n[{'aspect': 'toaster', 'opinion': 'strange', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: * * returning the device - - that unhappy with the item .\n->* * returning the device - - that unhappy with the item .\n[{'aspect': 'device', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'item', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: We actually gave 10 % tip ( which we have never done despite mediocre food and service ) , because we felt totally ripped off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe actually gave 10 % tip ( which we have never done despite mediocre food and service ) , because we felt totally ripped off .\n->", + "output": "{\"text\": \"We actually gave 10 % tip ( which we have never done despite mediocre food and service ) , because we felt totally ripped off .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what was even worse was the customer service .\n->what was even worse was the customer service .\n[{'aspect': 'customer service', 'opinion': 'worse', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n->this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n[{'aspect': 'trattoria', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'trattoria', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: We were looking forward to nice glass of Sangria when we arrived .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were looking forward to nice glass of Sangria when we arrived .\n->", + "output": "{\"text\": \"We were looking forward to nice glass of Sangria when we arrived .\", \"labels\": \"[{'aspect': 'glass of Sangria', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and $ 11 for a plate of bland guacamole ?\n->and $ 11 for a plate of bland guacamole ?\n[{'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i have had the computer for four months , and my computer suddenly wo n ' t turn .\n->i have had the computer for four months , and my computer suddenly wo n ' t turn .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I would highly recommend requesting a table by the window .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would highly recommend requesting a table by the window .\n->", + "output": "{\"text\": \"I would highly recommend requesting a table by the window .\", \"labels\": \"[{'aspect': 'table by the window', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n->chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n[{'aspect': 'chow fun', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork shu mai', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'loud', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i ca n ' t believe that it was , but please put the bag down before delivering food !\n->i ca n ' t believe that it was , but please put the bag down before delivering food !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: While we enjoyed the food , we were highly disappointed by the poor service ( waiter was not quite competent and SLOW service ) and lack of remorse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile we enjoyed the food , we were highly disappointed by the poor service ( waiter was not quite competent and SLOW service ) and lack of remorse .\n->", + "output": "{\"text\": \"While we enjoyed the food , we were highly disappointed by the poor service ( waiter was not quite competent and SLOW service ) and lack of remorse .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'not quite competent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'SLOW', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the small battery capacity is the number one reason i would not buy this product and would recommend with that caveat being disclosed .\n->the small battery capacity is the number one reason i would not buy this product and would recommend with that caveat being disclosed .\n[{'aspect': 'battery capacity', 'opinion': 'small', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: overall , worst chromebook ever and i can ' t wait until it dies !\n->overall , worst chromebook ever and i can ' t wait until it dies !\n[{'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: We 've been to Grocery three times and not once has an item on the menu disappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe 've been to Grocery three times and not once has an item on the menu disappointed .\n->", + "output": "{\"text\": \"We 've been to Grocery three times and not once has an item on the menu disappointed .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'disappointed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service is average .\n->Service is average .\n[{'aspect': 'Service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i bought this because it had a good price .\n->i bought this because it had a good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: A touch more jalapeno heat for contrast and it would have been very good indeed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA touch more jalapeno heat for contrast and it would have been very good indeed .\n->", + "output": "{\"text\": \"A touch more jalapeno heat for contrast and it would have been very good indeed .\", \"labels\": \"[{'aspect': 'jalapeno', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the Tom Kha soup was pathetic .\n->And the Tom Kha soup was pathetic .\n[{'aspect': 'Tom Kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: As much as I like the food there , I ca n't bring myself to go back .\n->As much as I like the food there , I ca n't bring myself to go back .\n[{'aspect': 'food', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n->", + "output": "{\"text\": \"We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\", \"labels\": \"[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n->i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n[{'aspect': 'realtek audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n->to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n[{'aspect': 'samsung', 'opinion': 'doubtful', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGuacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->", + "output": "{\"text\": \"Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\", \"labels\": \"[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Thius is a must for anyone who loves Shabu-Shabu .\n->Thius is a must for anyone who loves Shabu-Shabu .\n[{'aspect': 'Shabu-Shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Nothing fancy but really good food with pretty reasonable price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNothing fancy but really good food with pretty reasonable price .\n->", + "output": "{\"text\": \"Nothing fancy but really good food with pretty reasonable price .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the manager came to the table and said we can do what we want , so we paid for what we did enjoy , the drinks and appetizers , and walked out .\n->the manager came to the table and said we can do what we want , so we paid for what we did enjoy , the drinks and appetizers , and walked out .\n[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'drinks', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'appetizers', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: even with the volume turned up all the way , the sound is very low which means that you have a poor soundstage .\n->even with the volume turned up all the way , the sound is very low which means that you have a poor soundstage .\n[{'aspect': 'sound', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n->", + "output": "{\"text\": \"While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n->android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n[{'aspect': 'android', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: voice to text is good .\n->voice to text is good .\n[{'aspect': 'voice to text', 'opinion': 'good', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: Not worth the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot worth the prices .\n->", + "output": "{\"text\": \"Not worth the prices .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'worth', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The hostess is rude to the point of being offensive .\n->The hostess is rude to the point of being offensive .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The sushi seemed pretty fresh and was adequately proportioned .\n->The sushi seemed pretty fresh and was adequately proportioned .\n[{'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'proportioned', 'polarity': 'positive', 'category': 'NULL'}]\ntext: From the moment you enter till the moment you walk out the friendly and helpful staff was was just Fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrom the moment you enter till the moment you walk out the friendly and helpful staff was was just Fantastic .\n->", + "output": "{\"text\": \"From the moment you enter till the moment you walk out the friendly and helpful staff was was just Fantastic .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'Fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great item !\n->great item !\n[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we decided to eat in tea room which was small and cute .\n->we decided to eat in tea room which was small and cute .\n[{'aspect': 'tea room', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tea room', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service was good and food is wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was good and food is wonderful .\n->", + "output": "{\"text\": \"Service was good and food is wonderful .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would recommend reservations on weekends though .\n->i would recommend reservations on weekends though .\n[{'aspect': 'reservations', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: stop working the macbook pro\n->stop working the macbook pro\n[{'aspect': 'macbook pro', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: The quality of food at this restaurant accompanied by fantastic live jazz makes this place a perfect 10 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe quality of food at this restaurant accompanied by fantastic live jazz makes this place a perfect 10 !\n->", + "output": "{\"text\": \"The quality of food at this restaurant accompanied by fantastic live jazz makes this place a perfect 10 !\", \"labels\": \"[{'aspect': 'quality of food', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'live jazz', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , the power button placement is not very good .\n->also , the power button placement is not very good .\n[{'aspect': 'power button', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: ridiculous for something so expensive , some internet research shows numerous people having this issue .\n->ridiculous for something so expensive , some internet research shows numerous people having this issue .\n[{'aspect': 'NULL', 'opinion': 'ridiculous', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n->", + "output": "{\"text\": \"If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the portions are small but being that the food was so good makes up for that .\n->the portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the only thing i ' m bummed about is the lack of google play .\n->the only thing i ' m bummed about is the lack of google play .\n[{'aspect': 'google play', 'opinion': 'lack', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: Service and food is what any one would expect when spending that type of money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService and food is what any one would expect when spending that type of money .\n->", + "output": "{\"text\": \"Service and food is what any one would expect when spending that type of money .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'expect', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'expect', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chicken lollipop is my favorite , most of the dishes ( i have to agree with a previous reviewer ) are quite oily and very spicy , espeically the chilli chicken .\n->the chicken lollipop is my favorite , most of the dishes ( i have to agree with a previous reviewer ) are quite oily and very spicy , espeically the chilli chicken .\n[{'aspect': 'chicken lollipop', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chilli chicken', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chilli chicken', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: if you are going for the food , it will not be worth it .\n->if you are going for the food , it will not be worth it .\n[{'aspect': 'food', 'opinion': 'worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: LOVE the atmosphere - felt like I was in Paris .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLOVE the atmosphere - felt like I was in Paris .\n->", + "output": "{\"text\": \"LOVE the atmosphere - felt like I was in Paris .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n->the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: otherwise , it ' s a good computer .\n->otherwise , it ' s a good computer .\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Every course was better than the next .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEvery course was better than the next .\n->", + "output": "{\"text\": \"Every course was better than the next .\", \"labels\": \"[{'aspect': 'course', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great little machine while it was functioning 100 % .\n->great little machine while it was functioning 100 % .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\n->Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\n[{'aspect': 'dinner', 'opinion': \"could n't be happier\", 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The staff is incredibly helpful and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is incredibly helpful and attentive .\n->", + "output": "{\"text\": \"The staff is incredibly helpful and attentive .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - it freezes up depending on the program you use and you have to restart it .\n->- it freezes up depending on the program you use and you have to restart it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n->The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Cafe Noir', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The Yellowtail was particularly good as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Yellowtail was particularly good as well .\n->", + "output": "{\"text\": \"The Yellowtail was particularly good as well .\", \"labels\": \"[{'aspect': 'Yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other than that it ' s everything i imagined and more .\n->other than that it ' s everything i imagined and more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Try the crunchy tuna , it is to die for .\n->Try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I ate clams oreganta and spectacular salad with perfectly marinated cucumbers and tomatoes with lots of shrimp and basil .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ate clams oreganta and spectacular salad with perfectly marinated cucumbers and tomatoes with lots of shrimp and basil .\n->", + "output": "{\"text\": \"I ate clams oreganta and spectacular salad with perfectly marinated cucumbers and tomatoes with lots of shrimp and basil .\", \"labels\": \"[{'aspect': 'salad with perfectly marinated cucumbers and tomatoes with lots of shrimp and basil', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it surfs the internet fast .\n->it surfs the internet fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: the only real upgrade for the new one , before adding on options is faster memory .\n->the only real upgrade for the new one , before adding on options is faster memory .\n[{'aspect': 'memory', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: The menu changed , portions were even smaller than before , a lentil dish was salty beyond edibility , a basmati rice dish lacked flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu changed , portions were even smaller than before , a lentil dish was salty beyond edibility , a basmati rice dish lacked flavor .\n->", + "output": "{\"text\": \"The menu changed , portions were even smaller than before , a lentil dish was salty beyond edibility , a basmati rice dish lacked flavor .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'lentil dish', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'basmati rice dish', 'opinion': 'lacked flavor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will not recommend it .\n->will not recommend it .\n[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n->update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n[{'aspect': 'laptop', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Patroon features a nice cigar bar and has great staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPatroon features a nice cigar bar and has great staff .\n->", + "output": "{\"text\": \"Patroon features a nice cigar bar and has great staff .\", \"labels\": \"[{'aspect': 'cigar bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pesto pizza was excellent , thin-crust pizza with a nice amount of spicy Italian cheese that I 'd never heard of before .\n->The pesto pizza was excellent , thin-crust pizza with a nice amount of spicy Italian cheese that I 'd never heard of before .\n[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spicy Italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it felt flimsy , but decent .\n->it felt flimsy , but decent .\n[{'aspect': 'NULL', 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Very affordable and excellent ambient !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nVery affordable and excellent ambient !\n->", + "output": "{\"text\": \"Very affordable and excellent ambient !\", \"labels\": \"[{'aspect': 'ambient', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambient', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: short charging cable .\n->short charging cable .\n[{'aspect': 'charging cable', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\nExample:\ntext: everything is very smooth and fast .\n->everything is very smooth and fast .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: More important , the sushi rivals the best in Tokyo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMore important , the sushi rivals the best in Tokyo .\n->", + "output": "{\"text\": \"More important , the sushi rivals the best in Tokyo .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n->i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: nice keyboard .\n->nice keyboard .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: Why do people rave about the ambience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhy do people rave about the ambience .\n->", + "output": "{\"text\": \"Why do people rave about the ambience .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'rave', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after years of using amazon for hundreds of orders , this is my very first negative review :\n->after years of using amazon for hundreds of orders , this is my very first negative review :\n[{'aspect': 'amazon', 'opinion': 'negative', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: His drinks are very inventive , delicious and classy .\n->His drinks are very inventive , delicious and classy .\n[{'aspect': 'drinks', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'classy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n->", + "output": "{\"text\": \"Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\", \"labels\": \"[{'aspect': 'fruit of the oil', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'sweetness', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First off , the waitress was completely unattentive the 2 times we saw her ( odd in a restaurant with 6 tables ) and got our order wrong .\n->First off , the waitress was completely unattentive the 2 times we saw her ( odd in a restaurant with 6 tables ) and got our order wrong .\n[{'aspect': 'waitress', 'opinion': 'unattentive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: we were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .\n->we were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Great sushi experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat sushi experience .\n->", + "output": "{\"text\": \"Great sushi experience .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service was the only thing good about this restaurant .\n->the service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The only friendly staff member was the guy at the bar .\n->The only friendly staff member was the guy at the bar .\n[{'aspect': 'staff member', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: If you 're looking for a great meal at a decent price , go to Del Frisco 's !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you 're looking for a great meal at a decent price , go to Del Frisco 's !\n->", + "output": "{\"text\": \"If you 're looking for a great meal at a decent price , go to Del Frisco 's !\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: When we sat , we got great and fast service .\n->When we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: gates and this machine allows me to do this .\n->gates and this machine allows me to do this .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n->", + "output": "{\"text\": \"Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\", \"labels\": \"[{'aspect': 'congee', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'sweet tasting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'donut like deep fried dough they call Ow Ley Soh', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'donut like deep fried dough they call Ow Ley Soh', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unhygienic\n->unhygienic\n[{'aspect': 'NULL', 'opinion': 'unhygienic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it is fast and lightweight .\n->it is fast and lightweight .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'it', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The food was delicious and the waiter was incredibly helpful and attentive ( considering we were the only ones there for the first hour ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was delicious and the waiter was incredibly helpful and attentive ( considering we were the only ones there for the first hour ) .\n->", + "output": "{\"text\": \"The food was delicious and the waiter was incredibly helpful and attentive ( considering we were the only ones there for the first hour ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n->so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: and $ 11 for a plate of bland guacamole ?\n->and $ 11 for a plate of bland guacamole ?\n[{'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: The place was real empty but that was because this was the first Sunday they ever opened .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place was real empty but that was because this was the first Sunday they ever opened .\n->", + "output": "{\"text\": \"The place was real empty but that was because this was the first Sunday they ever opened .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'empty', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my laptop is affected by the staingate issue and apple denied responsibility , saying that it ' s a cosmetic problem caused by improper cleaning , yet there are thousands of reports on this .\n->my laptop is affected by the staingate issue and apple denied responsibility , saying that it ' s a cosmetic problem caused by improper cleaning , yet there are thousands of reports on this .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: the decor is vibrant and eye - pleasing with several semi - private boths on the right side of the dining hall , which are great for a date .\n->the decor is vibrant and eye - pleasing with several semi - private boths on the right side of the dining hall , which are great for a date .\n[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'eye - pleasing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'semi - private boths', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->", + "output": "{\"text\": \"This little place definitely exceeded my expectations and you sure get a lot of food for your money .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'lot', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: awsome machine .\n->awsome machine .\n[{'aspect': 'machine', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: quacamole at pacifico is yummy , as are the wings with chimmichuri .\n->quacamole at pacifico is yummy , as are the wings with chimmichuri .\n[{'aspect': 'quacamole', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wings with chimmichuri', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Ambience is delightful , service impeccable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmbience is delightful , service impeccable .\n->", + "output": "{\"text\": \"Ambience is delightful , service impeccable .\", \"labels\": \"[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !\n->it \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'cute', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i don ' t know why google print is so touchy .\n->i don ' t know why google print is so touchy .\n[{'aspect': 'google print', 'opinion': 'touchy', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: You get the sense that the people there care about their restaurant and about your experience and that is very nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou get the sense that the people there care about their restaurant and about your experience and that is very nice .\n->", + "output": "{\"text\": \"You get the sense that the people there care about their restaurant and about your experience and that is very nice .\", \"labels\": \"[{'aspect': 'people', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: crisp screen .\n->crisp screen .\n[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n->all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Both are delicious , the cooks are friendly and are willing to take a moment and speak to you and shake your hand .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBoth are delicious , the cooks are friendly and are willing to take a moment and speak to you and shake your hand .\n->", + "output": "{\"text\": \"Both are delicious , the cooks are friendly and are willing to take a moment and speak to you and shake your hand .\", \"labels\": \"[{'aspect': 'cooks', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n->this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'computer replacement', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: i have never been disappointed in the red eye .\n->i have never been disappointed in the red eye .\n[{'aspect': 'red eye', 'opinion': 'never been disappointed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The staff was accomodating , the food was absolutely delicious and the place is lovely .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff was accomodating , the food was absolutely delicious and the place is lovely .\n->", + "output": "{\"text\": \"The staff was accomodating , the food was absolutely delicious and the place is lovely .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m typing this review in a dark room , and it reminds me how much i love this backlit keyboard .\n->i ' m typing this review in a dark room , and it reminds me how much i love this backlit keyboard .\n[{'aspect': 'backlit keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n->i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n[{'aspect': 'zenkichi', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'unhurried', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n->", + "output": "{\"text\": \"my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\", \"labels\": \"[{'aspect': 'Scallion Pancake', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Scallion Pancake', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shredded Squid Family Style', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shuizhu Fish', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this by far one of the best laptops i ' ve ever purchased .\n->this by far one of the best laptops i ' ve ever purchased .\n[{'aspect': 'laptops', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n->My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n[{'aspect': 'dinner', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\ntext: They smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n->", + "output": "{\"text\": \"They smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\", \"labels\": \"[{'aspect': 'spinach mushroom calzone', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'canned vegetables', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery broke after just 4 months from baying it am so disappointed with the product\n->the battery broke after just 4 months from baying it am so disappointed with the product\n[{'aspect': 'battery', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}, {'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: We ended the dinner with a surprisingly light and flaky apple tarte tatin .\n->We ended the dinner with a surprisingly light and flaky apple tarte tatin .\n[{'aspect': 'apple tarte tatin', 'opinion': 'flaky', 'polarity': 'positive', 'category': 'NULL'}]\ntext: There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n->", + "output": "{\"text\": \"There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\", \"labels\": \"[{'aspect': 'Blue Point oysters', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice atmosphere , the service was very pleasant and the desert was good .\n->nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it works great !\n->it works great !\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n->", + "output": "{\"text\": \"I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\", \"labels\": \"[{'aspect': 'noodle dishes', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is reliable and the price is moderate .\n->The food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I love to visit Murrays for my bagel fix .\n->I love to visit Murrays for my bagel fix .\n[{'aspect': 'bagel', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: But for whatever reason , prices are about twice as high .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut for whatever reason , prices are about twice as high .\n->", + "output": "{\"text\": \"But for whatever reason , prices are about twice as high .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'high', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n->Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n[{'aspect': 'dishes', 'opinion': 'sake-friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n->The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n[{'aspect': 'waitstaff', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n->", + "output": "{\"text\": \"I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our food was great too !\n->Our food was great too !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Pizza was a little soggy .\n->Pizza was a little soggy .\n[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Great atmoshere and worth every bit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat atmoshere and worth every bit .\n->", + "output": "{\"text\": \"Great atmoshere and worth every bit .\", \"labels\": \"[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook seems to be working fine now and my daughter does love it .\n->the chromebook seems to be working fine now and my daughter does love it .\n[{'aspect': 'chromebook', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Have frequented 'ino for several years and the food remains excellent .\n->Have frequented 'ino for several years and the food remains excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff was knowledgeable and full of personality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff was knowledgeable and full of personality .\n->", + "output": "{\"text\": \"The staff was knowledgeable and full of personality .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'knowledgeable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the concept of japanese tapas is newly created and clearly does n ' t work .\n->the concept of japanese tapas is newly created and clearly does n ' t work .\n[{'aspect': 'japanese tapas', 'opinion': \"does n ' t work\", 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the second the screen did not rotate .\n->the second the screen did not rotate .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: Only complaint would be that at an average cost of $ 12- $ 15 per meal , I 'd like not to have to worry about finding a seat !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOnly complaint would be that at an average cost of $ 12- $ 15 per meal , I 'd like not to have to worry about finding a seat !\n->", + "output": "{\"text\": \"Only complaint would be that at an average cost of $ 12- $ 15 per meal , I 'd like not to have to worry about finding a seat !\", \"labels\": \"[{'aspect': 'cost', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seat', 'opinion': 'not to have to worry', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: many apps won ' t download and work on it like they do on an ellipsis .\n->many apps won ' t download and work on it like they do on an ellipsis .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: it 's a perfect place to have a amazing indian food .\n->it 's a perfect place to have a amazing indian food .\n[{'aspect': 'indian food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The banana tower is an amazing dessert as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe banana tower is an amazing dessert as well .\n->", + "output": "{\"text\": \"The banana tower is an amazing dessert as well .\", \"labels\": \"[{'aspect': 'banana tower', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had to wipe this pc about 8 times in the short 2 months i have owned it .\n->i have had to wipe this pc about 8 times in the short 2 months i have owned it .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n->have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n[{'aspect': 'machine', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nExcellent atmosphere , delicious dishes good and friendly service .\n->", + "output": "{\"text\": \"Excellent atmosphere , delicious dishes good and friendly service .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: perfect laptop for everyday use .\n->perfect laptop for everyday use .\n[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: 10 minutes after finishing this review started having a new issue : touch pad started acting out .\n->10 minutes after finishing this review started having a new issue : touch pad started acting out .\n[{'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: A cool place to hang with your friends for a couple of healthy drinks and desserts .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA cool place to hang with your friends for a couple of healthy drinks and desserts .\n->", + "output": "{\"text\": \"A cool place to hang with your friends for a couple of healthy drinks and desserts .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after about 60 days use the power adapter / charger stopped working .\n->after about 60 days use the power adapter / charger stopped working .\n[{'aspect': 'power adapter / charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: samsung has not been helpful in getting this fixed .\n->samsung has not been helpful in getting this fixed .\n[{'aspect': 'samsung', 'opinion': 'not been helpful', 'polarity': 'negative', 'category': 'COMPANY#OPERATION_PERFORMANCE'}]\ntext: I like Mamoun 's food as well , but side by side , Kati Rolls just produce tastier food hands down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI like Mamoun 's food as well , but side by side , Kati Rolls just produce tastier food hands down .\n->", + "output": "{\"text\": \"I like Mamoun 's food as well , but side by side , Kati Rolls just produce tastier food hands down .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tastier', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cute place , nice wait staff but would never go there again .\n->Cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n->i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n[{'aspect': 'word on line', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n->", + "output": "{\"text\": \"The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was lousy - too sweet or too salty and the portions tiny .\n->The food was lousy - too sweet or too salty and the portions tiny .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n->The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n[{'aspect': 'ingredients', 'opinion': 'fresher', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'crispier', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'slice', 'opinion': 'less oily', 'polarity': 'positive', 'category': 'NULL'}]\ntext: A cool bar with great food , and tons of excellent beer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nA cool bar with great food , and tons of excellent beer .\n->", + "output": "{\"text\": \"A cool bar with great food , and tons of excellent beer .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: dokebi gives williamsburg the right one - two punch of classic korean food and fusion twists like pork belly tacos .\n->dokebi gives williamsburg the right one - two punch of classic korean food and fusion twists like pork belly tacos .\n[{'aspect': 'korean food', 'opinion': 'classic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fusion twists', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork belly tacos', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: an awesome organic dog , and a conscious eco friendly establishment .\n->an awesome organic dog , and a conscious eco friendly establishment .\n[{'aspect': 'dog', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dog', 'opinion': 'organic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'establishment', 'opinion': 'eco friendly', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Be sure to try the seasonal , and always delicious , specials .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBe sure to try the seasonal , and always delicious , specials .\n->", + "output": "{\"text\": \"Be sure to try the seasonal , and always delicious , specials .\", \"labels\": \"[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nice ambiance , nice little bar , good bartender , Francois , and good service .\n->Nice ambiance , nice little bar , good bartender , Francois , and good service .\n[{'aspect': 'bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bartender', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: first occasionally and later to a point where the laptop became unusable .\n->first occasionally and later to a point where the laptop became unusable .\n[{'aspect': 'laptop', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The ambience is authentic and relaxing and we have always received attentive and prompt service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ambience is authentic and relaxing and we have always received attentive and prompt service .\n->", + "output": "{\"text\": \"The ambience is authentic and relaxing and we have always received attentive and prompt service .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n->The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n[{'aspect': 'menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Chilean Sea Bass', 'opinion': 'except', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the mbp 13 ` ` is plenty mobile .\n->the mbp 13 ` ` is plenty mobile .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: Fresh , authentic , french cuisine in substantial portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFresh , authentic , french cuisine in substantial portions .\n->", + "output": "{\"text\": \"Fresh , authentic , french cuisine in substantial portions .\", \"labels\": \"[{'aspect': 'french cuisine', 'opinion': 'Fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french cuisine', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'substantial', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n->it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n[{'aspect': 'anti - reflective coating', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: most of the servers are very attentive , friendly and quite attractive .\n->most of the servers are very attentive , friendly and quite attractive .\n[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: Drinks got screwed up , she acted put upon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDrinks got screwed up , she acted put upon .\n->", + "output": "{\"text\": \"Drinks got screwed up , she acted put upon .\", \"labels\": \"[{'aspect': 'Drinks', 'opinion': 'screwed up', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food and staff always surprise me with the new heights they are taken to .\n->The food and staff always surprise me with the new heights they are taken to .\n[{'aspect': 'food', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: today , it stopped powering on .\n->today , it stopped powering on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: The one vegetarian entree ( Abby 's treasure ) was actually quite a surprise - it was delicious and had wintermelon covering an assortment of fresh mushrooms and vegetables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe one vegetarian entree ( Abby 's treasure ) was actually quite a surprise - it was delicious and had wintermelon covering an assortment of fresh mushrooms and vegetables .\n->", + "output": "{\"text\": \"The one vegetarian entree ( Abby 's treasure ) was actually quite a surprise - it was delicious and had wintermelon covering an assortment of fresh mushrooms and vegetables .\", \"labels\": \"[{'aspect': 'vegetarian entree', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetarian entree', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Abby 's treasure\", 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Abby 's treasure\", 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assortment of fresh mushrooms and vegetables', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n->it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n[{'aspect': 'spinach', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n->i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'larger', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'hdmi', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: But the pizza is way to expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the pizza is way to expensive .\n->", + "output": "{\"text\": \"But the pizza is way to expensive .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if all you are looking for is a reliable laptop to write papers on or to browse the web , this is good .\n->if all you are looking for is a reliable laptop to write papers on or to browse the web , this is good .\n[{'aspect': 'laptop', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The bagels are fabulous .\n->The bagels are fabulous .\n[{'aspect': 'bagels', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: And the staff is also young , energeic and hot ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd the staff is also young , energeic and hot ! ! ! !\n->", + "output": "{\"text\": \"And the staff is also young , energeic and hot ! ! ! !\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'young', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'energeic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: toons has recently been redone , so it ' s now a very attractive space .\n->toons has recently been redone , so it ' s now a very attractive space .\n[{'aspect': 'toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: In addition , the food is very good and the prices are reasonable .\n->In addition , the food is very good and the prices are reasonable .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Even better , they know how to cook French classics like Steak au Poivre and Onglet without burning it to death or overcooking it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven better , they know how to cook French classics like Steak au Poivre and Onglet without burning it to death or overcooking it .\n->", + "output": "{\"text\": \"Even better , they know how to cook French classics like Steak au Poivre and Onglet without burning it to death or overcooking it .\", \"labels\": \"[{'aspect': 'Steak au Poivre', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Onglet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first chromebook purchase and i have to say that i ' m enjoying the speed and simplicity of it .\n->this is my first chromebook purchase and i have to say that i ' m enjoying the speed and simplicity of it .\n[{'aspect': 'chromebook', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n->i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'larger', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'hdmi', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: We had a wonderful meal at Naples 45 a month ago on a visit to NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had a wonderful meal at Naples 45 a month ago on a visit to NYC .\n->", + "output": "{\"text\": \"We had a wonderful meal at Naples 45 a month ago on a visit to NYC .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the appetizers are also delicious !\n->the appetizers are also delicious !\n[{'aspect': 'appetizers', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i also like the display .\n->i also like the display .\n[{'aspect': 'display', 'opinion': 'like', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: Good drink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood drink .\n->", + "output": "{\"text\": \"Good drink .\", \"labels\": \"[{'aspect': 'drink', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really recommend the very simple unda ( egg ) rolls .\n->i really recommend the very simple unda ( egg ) rolls .\n[{'aspect': 'unda ( egg ) rolls', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'unda ( egg ) rolls', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: 5 pound laptop with its nine hour battery life .\n->5 pound laptop with its nine hour battery life .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Even though I made the reservation at 3pm for the same night through Dinnerbroker , we were seated at a table with one of the best view !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven though I made the reservation at 3pm for the same night through Dinnerbroker , we were seated at a table with one of the best view !\n->", + "output": "{\"text\": \"Even though I made the reservation at 3pm for the same night through Dinnerbroker , we were seated at a table with one of the best view !\", \"labels\": \"[{'aspect': 'table', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is a lot of fun .\n->The place is a lot of fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 720p screen that ' s not very bright .\n->720p screen that ' s not very bright .\n[{'aspect': '720p screen', 'opinion': 'not very bright', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: I did n't take a look at the rest menu , but the oysters were fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI did n't take a look at the rest menu , but the oysters were fantastic .\n->", + "output": "{\"text\": \"I did n't take a look at the rest menu , but the oysters were fantastic .\", \"labels\": \"[{'aspect': 'oysters', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: honestly , i ' m debating returning this laptop .\n->honestly , i ' m debating returning this laptop .\n[{'aspect': 'laptop', 'opinion': 'debating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n->I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n[{'aspect': 'pastrami on challah sandwich', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The grilled cheese at home afterwards was better . ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe grilled cheese at home afterwards was better . ! !\n->", + "output": "{\"text\": \"The grilled cheese at home afterwards was better . ! !\", \"labels\": \"[{'aspect': 'grilled cheese', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fish was adequate , but inexpertly sliced .\n->the fish was adequate , but inexpertly sliced .\n[{'aspect': 'fish', 'opinion': 'adequate', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'fish', 'opinion': 'inexpertly sliced', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n->on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Great service , great food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat service , great food .\n->", + "output": "{\"text\": \"Great service , great food .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it has a glassy - smooth surface that resists finger - prints and goop , is very large , and has a firm but not too - clunky feeling click to it .\n->it has a glassy - smooth surface that resists finger - prints and goop , is very large , and has a firm but not too - clunky feeling click to it .\n[{'aspect': 'surface', 'opinion': 'glassy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'surface', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'surface', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'surface', 'opinion': 'firm', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes i get bad food and bad service , sometimes i get good good and bad service .\n->", + "output": "{\"text\": \"sometimes i get bad food and bad service , sometimes i get good good and bad service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great Indian food and the service is incredible .\n->Great Indian food and the service is incredible .\n[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great shabu shabu\n->great shabu shabu\n[{'aspect': 'shabu shabu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n->", + "output": "{\"text\": \"In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\", \"labels\": \"[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The quantity is also very good , you will come out satisfied .\n->The quantity is also very good , you will come out satisfied .\n[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n->if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: It 's the perfect restaurant for NY life style , it got cool design , awesome drinks and food and lot 's of good looking people eating and hanging at the pink bar ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's the perfect restaurant for NY life style , it got cool design , awesome drinks and food and lot 's of good looking people eating and hanging at the pink bar ...\n->", + "output": "{\"text\": \"It 's the perfect restaurant for NY life style , it got cool design , awesome drinks and food and lot 's of good looking people eating and hanging at the pink bar ...\", \"labels\": \"[{'aspect': 'design', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'pink', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: run do n ' t walk .\n->run do n ' t walk .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I have eaten there 3-4 times and the food was always good .\n->I have eaten there 3-4 times and the food was always good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Good , fast service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood , fast service .\n->", + "output": "{\"text\": \"Good , fast service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she loves this laptop .\n->she loves this laptop .\n[{'aspect': 'laptop', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n->but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n[{'aspect': 'battery', 'opinion': 'erroneous', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'way too sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'cover / lid', 'opinion': 'cheaply', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: I was in love with Pongsri on 48th , but compared to Suan it is slow in service and overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI was in love with Pongsri on 48th , but compared to Suan it is slow in service and overpriced .\n->", + "output": "{\"text\": \"I was in love with Pongsri on 48th , but compared to Suan it is slow in service and overpriced .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is fantastic .\n->the build quality is fantastic .\n[{'aspect': 'build quality', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The dinner menu is diverse and top-notch as well .\n->The dinner menu is diverse and top-notch as well .\n[{'aspect': 'dinner menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner menu', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n->", + "output": "{\"text\": \"My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\", \"labels\": \"[{'aspect': 'cheese', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first time writing a review for a restaurant because the food and service was excellent .\n->this is my first time writing a review for a restaurant because the food and service was excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great wine list ( italian ) , good food , service was INITIALLY fine .\n->great wine list ( italian ) , good food , service was INITIALLY fine .\n[{'aspect': 'wine list', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: It 's charmingly small and that leads to an atmoshere that is extremely cozy and romantic , even .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's charmingly small and that leads to an atmoshere that is extremely cozy and romantic , even .\n->", + "output": "{\"text\": \"It 's charmingly small and that leads to an atmoshere that is extremely cozy and romantic , even .\", \"labels\": \"[{'aspect': 'atmoshere', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshere', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google play support is lip service only .\n->google play support is lip service only .\n[{'aspect': 'google play support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: loved it\n->loved it\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Love the Jazz bands on Fri and Sat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLove the Jazz bands on Fri and Sat .\n->", + "output": "{\"text\": \"Love the Jazz bands on Fri and Sat .\", \"labels\": \"[{'aspect': 'Jazz bands', 'opinion': 'Love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza was pretty good and huge .\n->The pizza was pretty good and huge .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: now the curser / track pad is gone .\n->now the curser / track pad is gone .\n[{'aspect': 'curser / track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: The service was ok .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was ok .\n->", + "output": "{\"text\": \"The service was ok .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The hostess is rude to the point of being offensive .\n->The hostess is rude to the point of being offensive .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: definitely recommend this chromebook , it ' s a beautiful machine .\n->definitely recommend this chromebook , it ' s a beautiful machine .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Do n't expect to sit down inside though , there are only a few tables and they are always full .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDo n't expect to sit down inside though , there are only a few tables and they are always full .\n->", + "output": "{\"text\": \"Do n't expect to sit down inside though , there are only a few tables and they are always full .\", \"labels\": \"[{'aspect': 'tables', 'opinion': 'few', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'full', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m giving this five stars considering the price .\n->i ' m giving this five stars considering the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: when we inquired about ports - the waitress listed off several but did not know taste variations or cost .\n->when we inquired about ports - the waitress listed off several but did not know taste variations or cost .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The staff is accomodating , the ambiance is exciting and yet relaxed , and the food is out of this world !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is accomodating , the ambiance is exciting and yet relaxed , and the food is out of this world !\n->", + "output": "{\"text\": \"The staff is accomodating , the ambiance is exciting and yet relaxed , and the food is out of this world !\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'exciting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the ultimate tablet .\n->this is the ultimate tablet .\n[{'aspect': 'tablet', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i highly recommend it .\n->i highly recommend it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .\n->", + "output": "{\"text\": \"The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'halibut special', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the solution i am trying for now is to disable the touch screen .\n->the solution i am trying for now is to disable the touch screen .\n[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: i was there on sat . for my birthday and we had an excellent time .\n->i was there on sat . for my birthday and we had an excellent time .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The manager claimed that he could not compensate us for anything on the bill which just shows the lack of sophistication from the entire group .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe manager claimed that he could not compensate us for anything on the bill which just shows the lack of sophistication from the entire group .\n->", + "output": "{\"text\": \"The manager claimed that he could not compensate us for anything on the bill which just shows the lack of sophistication from the entire group .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'lack of sophistication', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is so easy to get a reservation at a top place in NYC with a week 's notice .\n->It is so easy to get a reservation at a top place in NYC with a week 's notice .\n[{'aspect': 'reservation', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I LOVE their Thai\n->I LOVE their Thai\n[{'aspect': 'Thai', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it 's the only place you can get yummy authentic japanese comfort food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit 's the only place you can get yummy authentic japanese comfort food .\n->", + "output": "{\"text\": \"it 's the only place you can get yummy authentic japanese comfort food .\", \"labels\": \"[{'aspect': 'japanese comfort food', 'opinion': 'yummy authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - new radeon graphics card ( finally ! )\n->- new radeon graphics card ( finally ! )\n[{'aspect': 'radeon graphics card', 'opinion': 'new', 'polarity': 'positive', 'category': 'GRAPHICS#GENERAL'}]\nExample:\ntext: Most importantly , food is excellent .\n->Most importantly , food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe took advanatage of the half price sushi deal on saturday so it was well worth it .\n->", + "output": "{\"text\": \"We took advanatage of the half price sushi deal on saturday so it was well worth it .\", \"labels\": \"[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: could have been a great computer if not for the terrible keyboard construction .\n->could have been a great computer if not for the terrible keyboard construction .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard construction', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: Scalina Fedeli reminded me why service is so integral to fine dining .\n->Scalina Fedeli reminded me why service is so integral to fine dining .\n[{'aspect': 'service', 'opinion': 'integral', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff are attentive , and have smiles on their faces .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff are attentive , and have smiles on their faces .\n->", + "output": "{\"text\": \"The staff are attentive , and have smiles on their faces .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you know , i wanted to love this machine .\n->you know , i wanted to love this machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: they are lightweight and easy to carry .\n->they are lightweight and easy to carry .\n[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Largest and freshest pieces of sushi , and delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLargest and freshest pieces of sushi , and delicious !\n->", + "output": "{\"text\": \"Largest and freshest pieces of sushi , and delicious !\", \"labels\": \"[{'aspect': 'pieces of sushi', 'opinion': 'Largest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish i could give it away at this point .\n->i wish i could give it away at this point .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I like the ambience , it 's very dark and original .\n->I like the ambience , it 's very dark and original .\n[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n->", + "output": "{\"text\": \"Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\", \"labels\": \"[{'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dancing , white river and millenium rolls are musts .\n->the dancing , white river and millenium rolls are musts .\n[{'aspect': 'dancing , white river and millenium rolls', 'opinion': 'musts', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: however , the resolution could be higher , the scaling is bad , the colors are a bit washy , the weight is off as said earlier , the keys are kinda awkward , travel could be more , and the keys start squeaking soon after use\n->however , the resolution could be higher , the scaling is bad , the colors are a bit washy , the weight is off as said earlier , the keys are kinda awkward , travel could be more , and the keys start squeaking soon after use\n[{'aspect': 'resolution', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'scaling', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'colors', 'opinion': 'washy', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keys', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: but when we looked at the menu , there were n't a lot of choices , most of them were dumplings in the appetizer section .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut when we looked at the menu , there were n't a lot of choices , most of them were dumplings in the appetizer section .\n->", + "output": "{\"text\": \"but when we looked at the menu , there were n't a lot of choices , most of them were dumplings in the appetizer section .\", \"labels\": \"[{'aspect': 'menu', 'opinion': \"were n't a lot of choices\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is not consistent .\n->it is not consistent .\n[{'aspect': 'NULL', 'opinion': 'not consistent', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the pizza is overpriced and soggy .\n->the pizza is overpriced and soggy .\n[{'aspect': 'pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWait staff is blantently unappreciative of your business but its the best pie on the UWS !\n->", + "output": "{\"text\": \"Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\", \"labels\": \"[{'aspect': 'Wait staff', 'opinion': 'unappreciative', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved it and would go again .\n->i loved it and would go again .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it ' s just average , just shredded , no seasoning on it .\n->it ' s just average , just shredded , no seasoning on it .\n[{'aspect': 'NULL', 'opinion': 'average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n->", + "output": "{\"text\": \"The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'fresher', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'crispier', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'slice', 'opinion': 'less oily', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of the worst offenders is the battery life .\n->one of the worst offenders is the battery life .\n[{'aspect': 'battery life', 'opinion': 'worst', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n->one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n[{'aspect': 'chromeos', 'opinion': 'frustrate', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\ntext: Fish is so very fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFish is so very fresh .\n->", + "output": "{\"text\": \"Fish is so very fresh .\", \"labels\": \"[{'aspect': 'Fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is not inviting and the food is totally weird .\n->this place is not inviting and the food is totally weird .\n[{'aspect': 'place', 'opinion': 'not inviting', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the location and ambience is ok but the food is what makes up for it .\n->the location and ambience is ok but the food is what makes up for it .\n[{'aspect': 'location', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LOCATION#GENERAL'}, {'aspect': 'ambience', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The prices were CHEAP compared to the quality of service and food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe prices were CHEAP compared to the quality of service and food .\n->", + "output": "{\"text\": \"The prices were CHEAP compared to the quality of service and food .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'CHEAP', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can live with the so - so touchpad since the rest of it feels solid / sturdy .\n->i can live with the so - so touchpad since the rest of it feels solid / sturdy .\n[{'aspect': 'touchpad', 'opinion': 'so - so', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'touchpad', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'touchpad', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: bought an acer computer that did not work .\n->bought an acer computer that did not work .\n[{'aspect': 'acer computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: Top spot in town for Vietnamese classics , better than places that cost a lot more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTop spot in town for Vietnamese classics , better than places that cost a lot more .\n->", + "output": "{\"text\": \"Top spot in town for Vietnamese classics , better than places that cost a lot more .\", \"labels\": \"[{'aspect': 'Vietnamese classics', 'opinion': 'Top', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you will find yourself returning quite often .\n->you will find yourself returning quite often .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the kitchen however , is almost always slow .\n->the kitchen however , is almost always slow .\n[{'aspect': 'kitchen', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: less wait time for me !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nless wait time for me !\n->", + "output": "{\"text\": \"less wait time for me !\", \"labels\": \"[{'aspect': 'wait time', 'opinion': 'less', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is fine and they allow you to enjoy the view .\n->The service is fine and they allow you to enjoy the view .\n[{'aspect': 'service', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'view', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n->We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n[{'aspect': 'waitress', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'servants', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The decor is really blah , and not at all hip or happening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe decor is really blah , and not at all hip or happening .\n->", + "output": "{\"text\": \"The decor is really blah , and not at all hip or happening .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'blah', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'not at all hip', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s so easy to travel with .\n->it ' s so easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\n->", + "output": "{\"text\": \"I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'ashamed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n->* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: their designs are made for phones and on this huge screen , they are palpitated .\n->their designs are made for phones and on this huge screen , they are palpitated .\n[{'aspect': 'designs', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n->", + "output": "{\"text\": \"The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\", \"labels\": \"[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spaghetti with Scallops and Shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you just have to deal with a low battery and that ' s all\n->you just have to deal with a low battery and that ' s all\n[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: and the tom kha soup was pathetic .\n->and the tom kha soup was pathetic .\n[{'aspect': 'tom kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The food was authentic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was authentic .\n->", + "output": "{\"text\": \"The food was authentic .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I ordered the smoked salmon and roe appetizer and it was off flavor .\n->I ordered the smoked salmon and roe appetizer and it was off flavor .\n[{'aspect': 'smoked salmon and roe appetizer', 'opinion': 'off flavor', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .\n->the service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The dim sum is delectable while the prices are quite easy on the wallet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dim sum is delectable while the prices are quite easy on the wallet .\n->", + "output": "{\"text\": \"The dim sum is delectable while the prices are quite easy on the wallet .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'delectable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best Reuben sandwich ever !\n->Best Reuben sandwich ever !\n[{'aspect': 'Reuben sandwich', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the restaurant is cute but not upscale .\n->the restaurant is cute but not upscale .\n[{'aspect': 'restaurant', 'opinion': 'cute', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'not upscale', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\ntext: I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n->", + "output": "{\"text\": \"I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\", \"labels\": \"[{'aspect': 'spinach ravioli in a light oil and garlic sauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after running good for the initial 25 first days it won ' t power on .\n->after running good for the initial 25 first days it won ' t power on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is great quality .\n->it is great quality .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: The worst excuse for Japanese food I 've ever encountered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe worst excuse for Japanese food I 've ever encountered .\n->", + "output": "{\"text\": \"The worst excuse for Japanese food I 've ever encountered .\", \"labels\": \"[{'aspect': 'Japanese food', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great price for a brand new product .\n->great price for a brand new product .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n->this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'retina display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'compatibility', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: We had Pam 's special fried fish and it was amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had Pam 's special fried fish and it was amazing .\n->", + "output": "{\"text\": \"We had Pam 's special fried fish and it was amazing .\", \"labels\": \"[{'aspect': \"Pam 's special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n->I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n[{'aspect': 'restaurant', 'opinion': 'amazing time', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: on the way out , we heard of other guests complaining about similar issues .\n->on the way out , we heard of other guests complaining about similar issues .\n[{'aspect': 'NULL', 'opinion': 'complaining', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: My boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\n->", + "output": "{\"text\": \"My boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\", \"labels\": \"[{'aspect': 'New England Chowder', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Lobster Bisque', 'opinion': 'award', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great little machine while it was functioning 100 % .\n->great little machine while it was functioning 100 % .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n->Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Not enough wines by the glass either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot enough wines by the glass either .\n->", + "output": "{\"text\": \"Not enough wines by the glass either .\", \"labels\": \"[{'aspect': 'wines by the glass', 'opinion': 'Not enough', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this item 7 months ago and i love it .\n->i purchased this item 7 months ago and i love it .\n[{'aspect': 'item', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I loved everythig about it-especially the shows and actors .\n->I loved everythig about it-especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI must say it 's a little pricey for the food because it was not as spectacular as the view .\n->", + "output": "{\"text\": \"I must say it 's a little pricey for the food because it was not as spectacular as the view .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: rao is a good restaurant , but it ' s nothing special .\n->rao is a good restaurant , but it ' s nothing special .\n[{'aspect': 'rao', 'opinion': 'good', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'rao', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i gave it 3 out of 5 stars , because there is no sd card slot !\n->i gave it 3 out of 5 stars , because there is no sd card slot !\n[{'aspect': 'sd card slot', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: We were worried we would have trouble getting in , but somehow managed to have a short wait .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were worried we would have trouble getting in , but somehow managed to have a short wait .\n->", + "output": "{\"text\": \"We were worried we would have trouble getting in , but somehow managed to have a short wait .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: multiple ports for anything you need .\n->multiple ports for anything you need .\n[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: The pizza was pretty good and huge .\n->The pizza was pretty good and huge .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My wife had the fried shrimp which are huge and loved it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy wife had the fried shrimp which are huge and loved it .\n->", + "output": "{\"text\": \"My wife had the fried shrimp which are huge and loved it .\", \"labels\": \"[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: further , the msi dragon center ( default software on the installation ) caused a blue screen which lead to me having to factory reset .\n->further , the msi dragon center ( default software on the installation ) caused a blue screen which lead to me having to factory reset .\n[{'aspect': 'msi dragon center (', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The rest of the menu is limited by everything is good eats .\n->The rest of the menu is limited by everything is good eats .\n[{'aspect': 'eats', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service is awful -- the last time I was there ( and I do mean the last time ) we were told that they needed our table so we would have to leave .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is awful -- the last time I was there ( and I do mean the last time ) we were told that they needed our table so we would have to leave .\n->", + "output": "{\"text\": \"The service is awful -- the last time I was there ( and I do mean the last time ) we were told that they needed our table so we would have to leave .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: authentic pakistani food .\n->authentic pakistani food .\n[{'aspect': 'pakistani food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i just find the battery draining to quickly in my opinion .\n->i just find the battery draining to quickly in my opinion .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Try the green curry ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the green curry ! ! !\n->", + "output": "{\"text\": \"Try the green curry ! ! !\", \"labels\": \"[{'aspect': 'green curry', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Personal pans are the perfect size for those hungry nights .\n->Personal pans are the perfect size for those hungry nights .\n[{'aspect': 'Personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Right off the L in Brooklyn this is a nice cozy place with good pizza .\n->Right off the L in Brooklyn this is a nice cozy place with good pizza .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'nice cozy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n->", + "output": "{\"text\": \"We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\", \"labels\": \"[{'aspect': 'scallops', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: would never go back there .\n->would never go back there .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: to me back - lighting on a keyboard is a make or break so i might return the laptop though ( bought it because i could have sworn i read it was backlit when i purchased it ) .\n->to me back - lighting on a keyboard is a make or break so i might return the laptop though ( bought it because i could have sworn i read it was backlit when i purchased it ) .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: We were also seated promptly at the time of our reservation and the service was very quick and professional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were also seated promptly at the time of our reservation and the service was very quick and professional .\n->", + "output": "{\"text\": \"We were also seated promptly at the time of our reservation and the service was very quick and professional .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely has issues with windows 10 .\n->definitely has issues with windows 10 .\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: we were charged full price .\n->we were charged full price .\n[{'aspect': 'NULL', 'opinion': 'full', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The food there are sastifying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food there are sastifying .\n->", + "output": "{\"text\": \"The food there are sastifying .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'sastifying', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .\n->this is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n->in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'chrome os', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\ntext: I highly recommend visiting this restaurant and having dinner and drinks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend visiting this restaurant and having dinner and drinks !\n->", + "output": "{\"text\": \"I highly recommend visiting this restaurant and having dinner and drinks !\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i generally like this place .\n->i generally like this place .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Drinks way over priced .\n->Drinks way over priced .\n[{'aspect': 'Drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'over', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat wine selection , Gigondas is worth the price , and the house champagne is a great value .\n->", + "output": "{\"text\": \"Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\", \"labels\": \"[{'aspect': 'wine selection', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Gigondas', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'worth', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fast boot up ( 3 seconds )\n->- fast boot up ( 3 seconds )\n[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' m going back .\n->i ' m going back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: at night , but it 's hard to hear your own conversation with everyone else competing for that same luxury - the music playing in the background is also voluminous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat night , but it 's hard to hear your own conversation with everyone else competing for that same luxury - the music playing in the background is also voluminous .\n->", + "output": "{\"text\": \"at night , but it 's hard to hear your own conversation with everyone else competing for that same luxury - the music playing in the background is also voluminous .\", \"labels\": \"[{'aspect': 'music', 'opinion': 'voluminous', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n->one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n[{'aspect': 'chromeos', 'opinion': 'frustrate', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\nExample:\ntext: if you want to watch movies or listen to music , this might not be the machine for you .\n->if you want to watch movies or listen to music , this might not be the machine for you .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I particularly love their yellowfun tuna and their mussel selection .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI particularly love their yellowfun tuna and their mussel selection .\n->", + "output": "{\"text\": \"I particularly love their yellowfun tuna and their mussel selection .\", \"labels\": \"[{'aspect': 'yellowfun tuna', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussel selection', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n->The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n[{'aspect': 'food', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portioins', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: All we received was an apology as we left to see our show without dinner .\n->All we received was an apology as we left to see our show without dinner .\n[{'aspect': 'dinner', 'opinion': 'without', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: I love to visit Murrays for my bagel fix .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI love to visit Murrays for my bagel fix .\n->", + "output": "{\"text\": \"I love to visit Murrays for my bagel fix .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fantastic computer !\n->fantastic computer !\n[{'aspect': 'computer', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n->The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n[{'aspect': 'in-house lady DJ', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n->", + "output": "{\"text\": \"We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\", \"labels\": \"[{'aspect': 'scenery', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner reservations', 'opinion': 'early', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n->the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screen', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: Hats off to the chef .\n->Hats off to the chef .\n[{'aspect': 'chef', 'opinion': 'Hats off', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Cheese plate is a varied delight and great bargain at $ 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCheese plate is a varied delight and great bargain at $ 10 .\n->", + "output": "{\"text\": \"Cheese plate is a varied delight and great bargain at $ 10 .\", \"labels\": \"[{'aspect': 'Cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n->Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n[{'aspect': 'pesto pizza', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house salad', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bottle of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the only thing i don ' t like is that the power button sits beside the delete key .\n->the only thing i don ' t like is that the power button sits beside the delete key .\n[{'aspect': 'power button', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: Most of the servers are very attentive , friendly and quite attractive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMost of the servers are very attentive , friendly and quite attractive .\n->", + "output": "{\"text\": \"Most of the servers are very attentive , friendly and quite attractive .\", \"labels\": \"[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: saul is the best restaurant on smith street and in brooklyn .\n->saul is the best restaurant on smith street and in brooklyn .\n[{'aspect': 'saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: service is not exactly five star , but thats not really a big deal .\n->service is not exactly five star , but thats not really a big deal .\n[{'aspect': 'service', 'opinion': 'not exactly five star', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: Service is average .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is average .\n->", + "output": "{\"text\": \"Service is average .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n->Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n[{'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n->However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n[{'aspect': 'pizza', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: But they 've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut they 've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual ) .\n->", + "output": "{\"text\": \"But they 've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual ) .\", \"labels\": \"[{'aspect': 'Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual )', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Chennai Garden is my favorite Indian restaurant in the city .\n->Chennai Garden is my favorite Indian restaurant in the city .\n[{'aspect': 'Chennai Garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after that , it ' s actually been running well .\n->after that , it ' s actually been running well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The steak is good , the fish is good and the sushi was surprisingly great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe steak is good , the fish is good and the sushi was surprisingly great .\n->", + "output": "{\"text\": \"The steak is good , the fish is good and the sushi was surprisingly great .\", \"labels\": \"[{'aspect': 'steak', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: buy this thing if you want a cheap , snappy web browser on - the - go .\n->buy this thing if you want a cheap , snappy web browser on - the - go .\n[{'aspect': 'NULL', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i do n ' t think 16 gb is enough .\n->i do n ' t think 16 gb is enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\ntext: The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n->", + "output": "{\"text\": \"The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'not very attentive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the portions are large and the servers always surprise us with a different starter .\n->the portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i was due to upgrade and this product seemed perfect for me .\n->i was due to upgrade and this product seemed perfect for me .\n[{'aspect': 'product', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: My GF and I still choose to eat there a lot because of diverse cocktails , the chill decor , and the decent sushi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy GF and I still choose to eat there a lot because of diverse cocktails , the chill decor , and the decent sushi .\n->", + "output": "{\"text\": \"My GF and I still choose to eat there a lot because of diverse cocktails , the chill decor , and the decent sushi .\", \"labels\": \"[{'aspect': 'cocktails', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'chill', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: two stars because the current laptop i have works great .\n->two stars because the current laptop i have works great .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the only real upgrade for the new one , before adding on options is faster memory .\n->the only real upgrade for the new one , before adding on options is faster memory .\n[{'aspect': 'memory', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: We ended the dinner with a surprisingly light and flaky apple tarte tatin .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ended the dinner with a surprisingly light and flaky apple tarte tatin .\n->", + "output": "{\"text\": \"We ended the dinner with a surprisingly light and flaky apple tarte tatin .\", \"labels\": \"[{'aspect': 'apple tarte tatin', 'opinion': 'flaky', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far great machine .\n->so far great machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: The buffet had a nice selection .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe buffet had a nice selection .\n->", + "output": "{\"text\": \"The buffet had a nice selection .\", \"labels\": \"[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'selection', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n->the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n[{'aspect': 'charging port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n->if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n[{'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Waitstaff are very friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWaitstaff are very friendly .\n->", + "output": "{\"text\": \"Waitstaff are very friendly .\", \"labels\": \"[{'aspect': 'Waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he offers subpar service and has no personality .\n->he offers subpar service and has no personality .\n[{'aspect': 'service', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great item !\n->great item !\n[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Service was slow had to wait to order and get food although not crowded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was slow had to wait to order and get food although not crowded .\n->", + "output": "{\"text\": \"Service was slow had to wait to order and get food although not crowded .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i / o : tons of ports , a dvd burner , solid keyboard with good feel to the key strokes , and a very precise and responsive trackpad .\n->i / o : tons of ports , a dvd burner , solid keyboard with good feel to the key strokes , and a very precise and responsive trackpad .\n[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'dvd burner', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OPTICAL_DRIVES#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'key strokes', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'key strokes', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'trackpad', 'opinion': 'precise', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the entree was bland and small , dessert was not inspired .\n->the entree was bland and small , dessert was not inspired .\n[{'aspect': 'entree', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'entree', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'dessert', 'opinion': 'not inspired', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The food was below average , the service was pathetic , there was no ambience at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was below average , the service was pathetic , there was no ambience at all .\n->", + "output": "{\"text\": \"The food was below average , the service was pathetic , there was no ambience at all .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were greeted promptly by the waiter who was very nice and cordial .\n->we were greeted promptly by the waiter who was very nice and cordial .\n[{'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'cordial', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The drinks are always well made and wine selection is fairly priced .\n->The drinks are always well made and wine selection is fairly priced .\n[{'aspect': 'drinks', 'opinion': 'well made', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine selection', 'opinion': 'fairly priced', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I REALLY ENJOYED THE SHOWS PUT ON BY THE ACTORS .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI REALLY ENJOYED THE SHOWS PUT ON BY THE ACTORS .\n->", + "output": "{\"text\": \"I REALLY ENJOYED THE SHOWS PUT ON BY THE ACTORS .\", \"labels\": \"[{'aspect': 'SHOWS', 'opinion': 'ENJOYED', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ACTORS', 'opinion': 'ENJOYED', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We also had shared a house salad that was fresh .\n->We also had shared a house salad that was fresh .\n[{'aspect': 'house salad', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n->i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: service was efficient courteous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was efficient courteous .\n->", + "output": "{\"text\": \"service was efficient courteous .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'efficient courteous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: maintenance and opening case is too hard\n->maintenance and opening case is too hard\n[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: purchased as a mothers day gift but i ' ve come to respect the quality and performance of lenovo .\n->purchased as a mothers day gift but i ' ve come to respect the quality and performance of lenovo .\n[{'aspect': 'lenovo', 'opinion': 'respect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'lenovo', 'opinion': 'respect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n->", + "output": "{\"text\": \"The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\", \"labels\": \"[{'aspect': 'miso soup', 'opinion': 'lacked flavor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'unfortunately', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the crunchy tuna , it is to die for .\n->Try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i give it 4 stars because i believe that these devices are perfect for adults who just want to surf the internet or watch netflix .\n->i give it 4 stars because i believe that these devices are perfect for adults who just want to surf the internet or watch netflix .\n[{'aspect': 'devices', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: They are the best bagels I 've had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey are the best bagels I 've had .\n->", + "output": "{\"text\": \"They are the best bagels I 've had .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and i hate to say this but i doubt i ' ll ever go back .\n->and i hate to say this but i doubt i ' ll ever go back .\n[{'aspect': 'NULL', 'opinion': 'doubt', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n->The people that work there are always so friendly you forget you are in New York sometimes .\n[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Toons has recently been redone , so it 's now a very attractive space .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nToons has recently been redone , so it 's now a very attractive space .\n->", + "output": "{\"text\": \"Toons has recently been redone , so it 's now a very attractive space .\", \"labels\": \"[{'aspect': 'space', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: slim , very fast , great screen , lightweight , solid state drives , great battery ( even though it burns the battery life when i play my flight simulator due to processing demands of the graphics ! )\n->slim , very fast , great screen , lightweight , solid state drives , great battery ( even though it burns the battery life when i play my flight simulator due to processing demands of the graphics ! )\n[{'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: should ' ve stick with my pixelbook which was awesome and never have me trouble .\n->should ' ve stick with my pixelbook which was awesome and never have me trouble .\n[{'aspect': 'pixelbook', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: While the prices are nothing special , the portions are huge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the prices are nothing special , the portions are huge .\n->", + "output": "{\"text\": \"While the prices are nothing special , the portions are huge .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'special', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is fantastic .\n->the build quality is fantastic .\n[{'aspect': 'build quality', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: this laptop has good hardware specs , but the screen has very poor color coverage : 59 % .\n->this laptop has good hardware specs , but the screen has very poor color coverage : 59 % .\n[{'aspect': 'screen', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIts a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n->", + "output": "{\"text\": \"Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'nice quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n[{'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Right off the L in Brooklyn this is a nice cozy place with good pizza .\n->Right off the L in Brooklyn this is a nice cozy place with good pizza .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'nice cozy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: However , I think this place is a good hang out spot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , I think this place is a good hang out spot .\n->", + "output": "{\"text\": \"However , I think this place is a good hang out spot .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s now been totally reliable for half a year or so .\n->it ' s now been totally reliable for half a year or so .\n[{'aspect': 'NULL', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n->this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The wine list is extensive and impressive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is extensive and impressive .\n->", + "output": "{\"text\": \"The wine list is extensive and impressive .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am happy i did the food was awsome .\n->i am happy i did the food was awsome .\n[{'aspect': 'food', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i barely do anything on it and it is a complete garbage can of a laptop .\n->i barely do anything on it and it is a complete garbage can of a laptop .\n[{'aspect': 'laptop', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: While the staff at this little bistro is very friendly , I have never experienced more incompetency .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the staff at this little bistro is very friendly , I have never experienced more incompetency .\n->", + "output": "{\"text\": \"While the staff at this little bistro is very friendly , I have never experienced more incompetency .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so rude ! ! !\n->so rude ! ! !\n[{'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The portions are small but being that the food was so good makes up for that .\n->The portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The quail was fantastic and unique and the pastas were full of flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe quail was fantastic and unique and the pastas were full of flavor .\n->", + "output": "{\"text\": \"The quail was fantastic and unique and the pastas were full of flavor .\", \"labels\": \"[{'aspect': 'quail', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quail', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastas', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: waitstaff are very friendly .\n->waitstaff are very friendly .\n[{'aspect': 'waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: He takes real pride in his food and his business .\n->He takes real pride in his food and his business .\n[{'aspect': 'food', 'opinion': 'pride', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff is courteous and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is courteous and friendly .\n->", + "output": "{\"text\": \"The staff is courteous and friendly .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we will definitely go back .\n->we will definitely go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n->even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: Aside from the rushed service , we were very impressed with the food and the drinks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAside from the rushed service , we were very impressed with the food and the drinks .\n->", + "output": "{\"text\": \"Aside from the rushed service , we were very impressed with the food and the drinks .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'rushed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is always very crowded and popular .\n->This place is always very crowded and popular .\n[{'aspect': 'place', 'opinion': 'crowded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'popular', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Great atmoshere and worth every bit .\n->Great atmoshere and worth every bit .\n[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n->", + "output": "{\"text\": \"I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\", \"labels\": \"[{'aspect': 'Indian dining experience', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After we got our sashimi order , I could not believe how small the portions were !\n->After we got our sashimi order , I could not believe how small the portions were !\n[{'aspect': 'sashimi', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: delicious bagels , especially when right out of the oven .\n->delicious bagels , especially when right out of the oven .\n[{'aspect': 'bagels', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This big draw is the all you can sushi here for $ 19.95 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis big draw is the all you can sushi here for $ 19.95 !\n->", + "output": "{\"text\": \"This big draw is the all you can sushi here for $ 19.95 !\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'draw', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have used this laptop only for work and the battery lasts and hour at most on mid performance .\n->i have used this laptop only for work and the battery lasts and hour at most on mid performance .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: good looking screen , has been bright enough for daily use , including outdoor .\n->good looking screen , has been bright enough for daily use , including outdoor .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: Some of the workers ignore me and talk to the female customers , other times , they 've skipped my order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSome of the workers ignore me and talk to the female customers , other times , they 've skipped my order .\n->", + "output": "{\"text\": \"Some of the workers ignore me and talk to the female customers , other times , they 've skipped my order .\", \"labels\": \"[{'aspect': 'workers', 'opinion': 'ignore', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'order', 'opinion': 'skipped', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If we were to move from the upper east side , we would genuinely miss this restaurant .\n->If we were to move from the upper east side , we would genuinely miss this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after reading a lot of the reviews on here , i was unsure about laptop .\n->after reading a lot of the reviews on here , i was unsure about laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: The specials are usually quite good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe specials are usually quite good too .\n->", + "output": "{\"text\": \"The specials are usually quite good too .\", \"labels\": \"[{'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery is okay .\n->battery is okay .\n[{'aspect': 'battery', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n->short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n[{'aspect': 'seating', 'opinion': 'short', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'private', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: They 've the best desserts and mixed drinks as well as snack foods .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey 've the best desserts and mixed drinks as well as snack foods .\n->", + "output": "{\"text\": \"They 've the best desserts and mixed drinks as well as snack foods .\", \"labels\": \"[{'aspect': 'desserts', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mixed drinks', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'snack foods', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n->if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n[{'aspect': 'corona', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: If you love wine and cheese and delicious french fare , you 'll love Artisanal !\n->If you love wine and cheese and delicious french fare , you 'll love Artisanal !\n[{'aspect': 'wine', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french fare', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Really cool stauff inside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nReally cool stauff inside .\n->", + "output": "{\"text\": \"Really cool stauff inside .\", \"labels\": \"[{'aspect': 'stauff', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pad se ew chicken was delicious , however the pad thai was far too oily .\n->the pad se ew chicken was delicious , however the pad thai was far too oily .\n[{'aspect': 'pad se ew chicken', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: we ordered some beef and noodle soup dishes from the thai section of the menu but nothing we got was thai .\n->we ordered some beef and noodle soup dishes from the thai section of the menu but nothing we got was thai .\n[{'aspect': 'beef and noodle soup dishes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: While the new restaurant still features much of the same classical furniture that made Tiffin so attractive , the menu has been overhauled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the new restaurant still features much of the same classical furniture that made Tiffin so attractive , the menu has been overhauled .\n->", + "output": "{\"text\": \"While the new restaurant still features much of the same classical furniture that made Tiffin so attractive , the menu has been overhauled .\", \"labels\": \"[{'aspect': 'classical furniture', 'opinion': 'classical', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'overhauled', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love it .\n->i love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: An excellent service\n->An excellent service\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The best burger I have had in the Village .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe best burger I have had in the Village .\n->", + "output": "{\"text\": \"The best burger I have had in the Village .\", \"labels\": \"[{'aspect': 'burger', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: be prepared to wait , because the place is pretty tiny .\n->be prepared to wait , because the place is pretty tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: fine dining restaurant quality .\n->fine dining restaurant quality .\n[{'aspect': 'dining', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Only drawback - they wo n't toast your bagel , and they do n't make eggs for the bagel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOnly drawback - they wo n't toast your bagel , and they do n't make eggs for the bagel .\n->", + "output": "{\"text\": \"Only drawback - they wo n't toast your bagel , and they do n't make eggs for the bagel .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everythig about it - especially the shows and actors .\n->i loved everythig about it - especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: just not good at all .\n->just not good at all .\n[{'aspect': 'NULL', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: As always we had a great glass of wine while we waited .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs always we had a great glass of wine while we waited .\n->", + "output": "{\"text\": \"As always we had a great glass of wine while we waited .\", \"labels\": \"[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price , 5 stars .\n->for the price , 5 stars .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n->the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n[{'aspect': 'soy sauce', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'vinegar-soaked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n->", + "output": "{\"text\": \"The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quite frankly , this is some of the worst sushi I have ever tried .\n->Quite frankly , this is some of the worst sushi I have ever tried .\n[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: sent it in , arrived back in an inadequate box ( i shipped it in the oroginal with protective foam came back in the box from samsung w / o any protection ) and 3 days after i recieved the item back the stylus fell apart .\n->sent it in , arrived back in an inadequate box ( i shipped it in the oroginal with protective foam came back in the box from samsung w / o any protection ) and 3 days after i recieved the item back the stylus fell apart .\n[{'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: Of course this atmosphere is lacking , but what do you expect from a 24 hour bagel place anyways ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOf course this atmosphere is lacking , but what do you expect from a 24 hour bagel place anyways ?\n->", + "output": "{\"text\": \"Of course this atmosphere is lacking , but what do you expect from a 24 hour bagel place anyways ?\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish i could be refunded !\n->i wish i could be refunded !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: overall i have to remind myself that this laptop is only 600 dollars , and with certain gains i ' ll lose some things unless i ' m willing to spend out of budget .\n->overall i have to remind myself that this laptop is only 600 dollars , and with certain gains i ' ll lose some things unless i ' m willing to spend out of budget .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: Service was very good and warm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was very good and warm .\n->", + "output": "{\"text\": \"Service was very good and warm .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n->I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n[{'aspect': 'spinach ravioli in a light oil and garlic sauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the monitor is bright and colorful .\n->the monitor is bright and colorful .\n[{'aspect': 'monitor', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'monitor', 'opinion': 'colorful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n->", + "output": "{\"text\": \"Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mbp 13 ` ` is plenty mobile .\n->the mbp 13 ` ` is plenty mobile .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: also the case design is sort of rounded at both sides - a minor issue but it makes the device pop open in my purse and detracts from the ` ` feel ` ` of the device by making it feel a lot thicker than it actually is .\n->also the case design is sort of rounded at both sides - a minor issue but it makes the device pop open in my purse and detracts from the ` ` feel ` ` of the device by making it feel a lot thicker than it actually is .\n[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n->", + "output": "{\"text\": \"Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'view', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got an excellent piece of cheesecake and we had several other nice pastries .\n->i got an excellent piece of cheesecake and we had several other nice pastries .\n[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: We love the food , drinks , and atmosphere !\n->We love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n->", + "output": "{\"text\": \"The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they ' re like a little - known gem , practically unknown in my area .\n->they ' re like a little - known gem , practically unknown in my area .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Although we were looking for regular lettuce and some walnuts the salads we got were great .\n->Although we were looking for regular lettuce and some walnuts the salads we got were great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lettuce', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'walnuts', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The service was a bit slow , but they were very friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was a bit slow , but they were very friendly .\n->", + "output": "{\"text\": \"The service was a bit slow , but they were very friendly .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: performance is mediocre\n->performance is mediocre\n[{'aspect': 'NULL', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n->only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Leon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLeon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .\n->", + "output": "{\"text\": \"Leon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .\", \"labels\": \"[{'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'lively', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'French bistro fare', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: absolutly horrible product , cheap components make for unreliable products , but when the nearest service location is 50 + miles away i wonder how much apple truly cares ?\n->absolutly horrible product , cheap components make for unreliable products , but when the nearest service location is 50 + miles away i wonder how much apple truly cares ?\n[{'aspect': 'product', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'components', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'products', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'service location', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: Best Italian food I ever had ( and being Italian , that means alot ) .\n->Best Italian food I ever had ( and being Italian , that means alot ) .\n[{'aspect': 'Italian food', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city .\n->", + "output": "{\"text\": \"The food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the service was a bit slow .\n->but the service was a bit slow .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: apple told me that they ca n ' t order those screws so i basically threw my money away on this laptop .\n->apple told me that they ca n ' t order those screws so i basically threw my money away on this laptop .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Thius is a must for anyone who loves Shabu-Shabu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThius is a must for anyone who loves Shabu-Shabu .\n->", + "output": "{\"text\": \"Thius is a must for anyone who loves Shabu-Shabu .\", \"labels\": \"[{'aspect': 'Shabu-Shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n->chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n[{'aspect': 'chromeos', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chromeos', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was absolutely horrible !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was absolutely horrible !\n->", + "output": "{\"text\": \"The food was absolutely horrible !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The prices were CHEAP compared to the quality of service and food .\n->The prices were CHEAP compared to the quality of service and food .\n[{'aspect': 'prices', 'opinion': 'CHEAP', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was able to download all of my games in a quick amount of time too .\n->i was able to download all of my games in a quick amount of time too .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Similar to other Indian restaurants , they use the dinner special to attract customers at the door .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSimilar to other Indian restaurants , they use the dinner special to attract customers at the door .\n->", + "output": "{\"text\": \"Similar to other Indian restaurants , they use the dinner special to attract customers at the door .\", \"labels\": \"[{'aspect': 'dinner special', 'opinion': 'attract', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they are loud enough to fill a small , quiet room , but that is about it .\n->they are loud enough to fill a small , quiet room , but that is about it .\n[{'aspect': 'NULL', 'opinion': 'loud', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: bad touchpad , jerky movement , imprecise , no controls to improve .\n->bad touchpad , jerky movement , imprecise , no controls to improve .\n[{'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'jerky', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'imprecise', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n->", + "output": "{\"text\": \"The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But the pizza is way to expensive .\n->But the pizza is way to expensive .\n[{'aspect': 'pizza', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: - backlit keyboard rocks\n->- backlit keyboard rocks\n[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n->", + "output": "{\"text\": \"The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\", \"labels\": \"[{'aspect': 'crackling calamari salad', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crackling calamari salad', 'opinion': 'lightly dressed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my dad says it works extremely well !\n->my dad says it works extremely well !\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: however , it ' s the service that leaves a bad taste in my mouth .\n->however , it ' s the service that leaves a bad taste in my mouth .\n[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNot the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n->", + "output": "{\"text\": \"Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\", \"labels\": \"[{'aspect': 'sushi place', 'opinion': 'Not the greatest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi place', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely has issues with windows 10 .\n->definitely has issues with windows 10 .\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i like it , good construction , can load android apps .\n->i like it , good construction , can load android apps .\n[{'aspect': 'construction', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nShockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n->", + "output": "{\"text\": \"Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\", \"labels\": \"[{'aspect': 'group dinner', 'opinion': 'easy', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the whole thing seems very sturdy but not stiff .\n->the whole thing seems very sturdy but not stiff .\n[{'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not stiff', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: The Thali was small , thoroughly unremarkable , and $ 14.95 .\n->The Thali was small , thoroughly unremarkable , and $ 14.95 .\n[{'aspect': 'Thali', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Thali', 'opinion': 'unremarkable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The Thai ice tea was amazingly smooth and yummy !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Thai ice tea was amazingly smooth and yummy !\n->", + "output": "{\"text\": \"The Thai ice tea was amazingly smooth and yummy !\", \"labels\": \"[{'aspect': 'Thai ice tea', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai ice tea', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it travels well , great battery life , and is powerful enough for 100 % of the tasks i need to do ( web , streaming , word processing , reports , email ) .\n->it travels well , great battery life , and is powerful enough for 100 % of the tasks i need to do ( web , streaming , word processing , reports , email ) .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: I recommend the meatballs and caprese salad and the beans on toast were a wonderful start to the meal !\n->I recommend the meatballs and caprese salad and the beans on toast were a wonderful start to the meal !\n[{'aspect': 'meatballs', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caprese salad', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beans on toast', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'wonderful', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Drinks way over priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDrinks way over priced .\n->", + "output": "{\"text\": \"Drinks way over priced .\", \"labels\": \"[{'aspect': 'Drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'over', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i absolutely love this beyond any computer / tablet device i have purchased in the last 10 years , trust me , i ' ve bought a lot of them along the way .\n->i absolutely love this beyond any computer / tablet device i have purchased in the last 10 years , trust me , i ' ve bought a lot of them along the way .\n[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet device', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n->for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: The main downside to the place is the nazi-like guy running it who constantly complains about the noise level .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe main downside to the place is the nazi-like guy running it who constantly complains about the noise level .\n->", + "output": "{\"text\": \"The main downside to the place is the nazi-like guy running it who constantly complains about the noise level .\", \"labels\": \"[{'aspect': 'noise level', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'guy', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n->i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Saul is pretty good , but definitely not great .\n->Saul is pretty good , but definitely not great .\n[{'aspect': 'Saul', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Saul', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Moderate prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nModerate prices .\n->", + "output": "{\"text\": \"Moderate prices .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'Moderate', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n->they are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n[{'aspect': 'NULL', 'opinion': 'not helpful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: touch screen is really responsive .\n->touch screen is really responsive .\n[{'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: I think I 've had some the best meals of my life at minnow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI think I 've had some the best meals of my life at minnow .\n->", + "output": "{\"text\": \"I think I 've had some the best meals of my life at minnow .\", \"labels\": \"[{'aspect': 'meals', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the second one arrived , i had it from end of may june , to september started to notice the a key was a bit unresponsive at times .\n->the second one arrived , i had it from end of may june , to september started to notice the a key was a bit unresponsive at times .\n[{'aspect': 'a key', 'opinion': 'unresponsive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n->if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Most importantly , food is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMost importantly , food is excellent .\n->", + "output": "{\"text\": \"Most importantly , food is excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only wish the power button was somewhere else , its too easy to hit accidentally .\n->only wish the power button was somewhere else , its too easy to hit accidentally .\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\nExample:\ntext: i think that it is absolutely brilliant and well runned business operation .\n->i think that it is absolutely brilliant and well runned business operation .\n[{'aspect': 'NULL', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'well runned', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Great roofdeck , nice group of 30 somethings , but no music , kind of quiet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat roofdeck , nice group of 30 somethings , but no music , kind of quiet .\n->", + "output": "{\"text\": \"Great roofdeck , nice group of 30 somethings , but no music , kind of quiet .\", \"labels\": \"[{'aspect': 'roofdeck', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'quiet', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromebooks are light weight and start up immediately and are very easy to use .\n->chromebooks are light weight and start up immediately and are very easy to use .\n[{'aspect': 'chromebooks', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebooks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Saul is the best restaurant on Smith Street and in Brooklyn .\n->Saul is the best restaurant on Smith Street and in Brooklyn .\n[{'aspect': 'Saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: However , service was as plain as sesame crusted Salmon I had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , service was as plain as sesame crusted Salmon I had .\n->", + "output": "{\"text\": \"However , service was as plain as sesame crusted Salmon I had .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'sesame crusted Salmon', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fact that i can flip it into tablet is cool , the audio is decent , i love the feel of the keyboard , i didn ' t think touch screen was a big deal , but i use it all the time now with joy , ya .\n->the fact that i can flip it into tablet is cool , the audio is decent , i love the feel of the keyboard , i didn ' t think touch screen was a big deal , but i use it all the time now with joy , ya .\n[{'aspect': 'tablet', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch screen', 'opinion': 'joy', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: We went to eat at the Jekyll and Hyde restaurant on Friday night and really enjoyed the fun atmosphere and good food .\n->We went to eat at the Jekyll and Hyde restaurant on Friday night and really enjoyed the fun atmosphere and good food .\n[{'aspect': 'atmosphere', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: It 's great to go for a quick lunch either alone or with a friend .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's great to go for a quick lunch either alone or with a friend .\n->", + "output": "{\"text\": \"It 's great to go for a quick lunch either alone or with a friend .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was ok .\n->food was ok .\n[{'aspect': 'food', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it is the best chromebook that i have ever used .\n->it is the best chromebook that i have ever used .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The food was mediocre at best but it was the horrible service that made me vow never to go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was mediocre at best but it was the horrible service that made me vow never to go back .\n->", + "output": "{\"text\": \"The food was mediocre at best but it was the horrible service that made me vow never to go back .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this laptop for software development .\n->i bought this laptop for software development .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: losing the function keys for a toucher was a deal breaker .\n->losing the function keys for a toucher was a deal breaker .\n[{'aspect': 'function keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n->", + "output": "{\"text\": \"You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\", \"labels\": \"[{'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n->but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n[{'aspect': 'battery', 'opinion': 'erroneous', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'way too sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'cover / lid', 'opinion': 'cheaply', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i bought this for my daughter for school and she loves it .\n->i bought this for my daughter for school and she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n->", + "output": "{\"text\": \"The service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hostess and the waitress were incredibly rude and did everything they could to rush us out .\n->the hostess and the waitress were incredibly rude and did everything they could to rush us out .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitress', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the service was friendly and the atmosphere was casual .\n->the service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\ntext: Other than being a little crowded and a bit overpriced , the atmosphere is filled with energy ( and the beautiful people of course ) and the food was surprising good !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOther than being a little crowded and a bit overpriced , the atmosphere is filled with energy ( and the beautiful people of course ) and the food was surprising good !\n->", + "output": "{\"text\": \"Other than being a little crowded and a bit overpriced , the atmosphere is filled with energy ( and the beautiful people of course ) and the food was surprising good !\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'energy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'surprising good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\n->Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\n[{'aspect': 'dinner', 'opinion': \"could n't be happier\", 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the bottom plastic piece - this is not a unibody machine - will be too hot to rest on bare skin .\n->the bottom plastic piece - this is not a unibody machine - will be too hot to rest on bare skin .\n[{'aspect': 'plastic piece', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: The music is the best among all the Indian restaurants I have visited .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe music is the best among all the Indian restaurants I have visited .\n->", + "output": "{\"text\": \"The music is the best among all the Indian restaurants I have visited .\", \"labels\": \"[{'aspect': 'music', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .\n->I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .\n[{'aspect': 'sea urchin', 'opinion': 'heavenly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Delicious crab cakes too .\n->Delicious crab cakes too .\n[{'aspect': 'crab cakes', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The bread and lamb chops I had before the meal were quite good , however .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bread and lamb chops I had before the meal were quite good , however .\n->", + "output": "{\"text\": \"The bread and lamb chops I had before the meal were quite good , however .\", \"labels\": \"[{'aspect': 'bread', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb chops', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good computer good memory\n->good computer good memory\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: Seriously , this is the best all you can eat in town- As everyone says , the Spicy Tuna hand rolls are the best- have 4 of these , and you 've broken even .\n->Seriously , this is the best all you can eat in town- As everyone says , the Spicy Tuna hand rolls are the best- have 4 of these , and you 've broken even .\n[{'aspect': 'Spicy Tuna hand rolls', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Rao 's has the best service and atmosphere in NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRao 's has the best service and atmosphere in NYC .\n->", + "output": "{\"text\": \"Rao 's has the best service and atmosphere in NYC .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n->by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: this is a great notebook .\n->this is a great notebook .\n[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: They never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\n->", + "output": "{\"text\": \"They never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\", \"labels\": \"[{'aspect': 'noodles', 'opinion': 'complimentary', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sugar', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'threw', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - android apps work rather well , even if not perfectly yet .\n->- android apps work rather well , even if not perfectly yet .\n[{'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'not perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: we were not dissappointed in the least bit by this little gem .\n->we were not dissappointed in the least bit by this little gem .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Excellent dumplings served amid clean , chic decor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nExcellent dumplings served amid clean , chic decor .\n->", + "output": "{\"text\": \"Excellent dumplings served amid clean , chic decor .\", \"labels\": \"[{'aspect': 'dumplings', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'clean', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'chic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i wish the sound quality was better .\n->- i wish the sound quality was better .\n[{'aspect': 'sound quality', 'opinion': 'better', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n->Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'view of the new york city skiline', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The kitchen however , is almost always slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe kitchen however , is almost always slow .\n->", + "output": "{\"text\": \"The kitchen however , is almost always slow .\", \"labels\": \"[{'aspect': 'kitchen', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like this laptop , for a 15 ` ` monitor laptop with i5 - 8250u cpu , the weight is acceptable for me to carry it to work between different office .\n->i like this laptop , for a 15 ` ` monitor laptop with i5 - 8250u cpu , the weight is acceptable for me to carry it to work between different office .\n[{'aspect': 'weight', 'opinion': 'acceptable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: you can ' t beat the price for what you are getting with this computer .\n->you can ' t beat the price for what you are getting with this computer .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: Cute place , nice wait staff but would never go there again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCute place , nice wait staff but would never go there again .\n->", + "output": "{\"text\": \"Cute place , nice wait staff but would never go there again .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great machine for all my needs .\n->great machine for all my needs .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n->today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: people are rude bit again it 's new york !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npeople are rude bit again it 's new york !\n->", + "output": "{\"text\": \"people are rude bit again it 's new york !\", \"labels\": \"[{'aspect': 'people', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wound up returning it .\n->i wound up returning it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: everyone was cheerfully cooperative and helpful .\n->everyone was cheerfully cooperative and helpful .\n[{'aspect': 'NULL', 'opinion': 'cheerfully cooperative', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n->", + "output": "{\"text\": \"The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\", \"labels\": \"[{'aspect': 'plain slice', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n->i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: it ' s a small cute restaurant .\n->it ' s a small cute restaurant .\n[{'aspect': 'restaurant', 'opinion': 'small', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'cute', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Service was also horrible and the ambience is not that great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was also horrible and the ambience is not that great .\n->", + "output": "{\"text\": \"Service was also horrible and the ambience is not that great .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n->we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'gulab jamun ( dessert )', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Great bagels , spreads and a good place to hang out in .\n->Great bagels , spreads and a good place to hang out in .\n[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spreads', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service is great , takeout is good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is great , takeout is good too .\n->", + "output": "{\"text\": \"Service is great , takeout is good too .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'takeout', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n->If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n[{'aspect': 'meal', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I love and I know gourmet food by excellence !\n->I love and I know gourmet food by excellence !\n[{'aspect': 'gourmet food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'gourmet food', 'opinion': 'excellence', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Everything is excellent , the menu is quite extensive , and you eat with a view on both sides of the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEverything is excellent , the menu is quite extensive , and you eat with a view on both sides of the city .\n->", + "output": "{\"text\": \"Everything is excellent , the menu is quite extensive , and you eat with a view on both sides of the city .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The red curry is weak and tasteless , the pad thai is stuck together and lumpy , the rice is often overcooked , and the seafood is pretty sketchy .\n->The red curry is weak and tasteless , the pad thai is stuck together and lumpy , the rice is often overcooked , and the seafood is pretty sketchy .\n[{'aspect': 'red curry', 'opinion': 'weak', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'red curry', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'lumpy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'sketchy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The food here is rather good , but only if you like to wait for it .\n->The food here is rather good , but only if you like to wait for it .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: For the quality of food , a little too expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor the quality of food , a little too expensive .\n->", + "output": "{\"text\": \"For the quality of food , a little too expensive .\", \"labels\": \"[{'aspect': 'quality of food', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wifi radio loses signal too frequently .\n->wifi radio loses signal too frequently .\n[{'aspect': 'wifi radio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The bruschetta and panini 's are so yummy !\n->The bruschetta and panini 's are so yummy !\n[{'aspect': 'bruschetta', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'panini', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Here 's to the fake fish tanks too ...\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHere 's to the fake fish tanks too ...\n->", + "output": "{\"text\": \"Here 's to the fake fish tanks too ...\", \"labels\": \"[{'aspect': 'fish tanks', 'opinion': 'fake', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she loves this laptop .\n->she loves this laptop .\n[{'aspect': 'laptop', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n->If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n[{'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bottle minimun', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I 've never had bad service and the fish is fresh and delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've never had bad service and the fish is fresh and delicious .\n->", + "output": "{\"text\": \"I 've never had bad service and the fish is fresh and delicious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the track pad is one of the best i have seen for a non - apple touch pad .\n->the track pad is one of the best i have seen for a non - apple touch pad .\n[{'aspect': 'track pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n->the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n[{'aspect': '4gb / 32gb version', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': '4gb / 32gb version', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage space', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: we decided to eat in tea room which was small and cute .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe decided to eat in tea room which was small and cute .\n->", + "output": "{\"text\": \"we decided to eat in tea room which was small and cute .\", \"labels\": \"[{'aspect': 'tea room', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tea room', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Well , their deliveries take for ever and the food is usually cold .\n->Well , their deliveries take for ever and the food is usually cold .\n[{'aspect': 'deliveries', 'opinion': 'for ever', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n->The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n[{'aspect': 'crackling calamari salad', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crackling calamari salad', 'opinion': 'lightly dressed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: What an amazing meal and experience !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhat an amazing meal and experience !\n->", + "output": "{\"text\": \"What an amazing meal and experience !\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is far and away the best i ' ve ever had .\n->it is far and away the best i ' ve ever had .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: just straight up cheap , good food .\n->just straight up cheap , good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: Unique apppetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nUnique apppetizers .\n->", + "output": "{\"text\": \"Unique apppetizers .\", \"labels\": \"[{'aspect': 'apppetizers', 'opinion': 'Unique', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - battery sucks .\n->- battery sucks .\n[{'aspect': 'battery', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: - trackpad is too finicky and not my favorite\n->- trackpad is too finicky and not my favorite\n[{'aspect': 'trackpad', 'opinion': 'finicky', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\ntext: The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n->", + "output": "{\"text\": \"The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: an unexpected benefit for me has been the iphone / mbp integration .\n->an unexpected benefit for me has been the iphone / mbp integration .\n[{'aspect': 'iphone / mbp integration', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: but that is highly forgivable .\n->but that is highly forgivable .\n[{'aspect': 'NULL', 'opinion': 'forgivable', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: IT is the best deal in town for a Monday night dinner at a fine restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIT is the best deal in town for a Monday night dinner at a fine restaurant .\n->", + "output": "{\"text\": \"IT is the best deal in town for a Monday night dinner at a fine restaurant .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve never had a computer as fast as this .\n->i ' ve never had a computer as fast as this .\n[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' m amazed how bad this machine is for the money , and for being described as a ` ` mid - level gamer . ` `\n->i ' m amazed how bad this machine is for the money , and for being described as a ` ` mid - level gamer . ` `\n[{'aspect': 'machine', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'machine', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n->", + "output": "{\"text\": \"The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'long', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dim sum atmosphere', 'opinion': 'typical raucous', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as always we had a great glass of wine while we waited .\n->as always we had a great glass of wine while we waited .\n[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: i also like the display .\n->i also like the display .\n[{'aspect': 'display', 'opinion': 'like', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: This place has good potential , but needs a significant amount of work before we can justify spending that much money on indian food you can get everywhere else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place has good potential , but needs a significant amount of work before we can justify spending that much money on indian food you can get everywhere else .\n->", + "output": "{\"text\": \"This place has good potential , but needs a significant amount of work before we can justify spending that much money on indian food you can get everywhere else .\", \"labels\": \"[{'aspect': 'money', 'opinion': 'much', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love the airdrop and receiving imessages on my mbp ( although it can cut into productivity if you ' re not careful ) .\n->i love the airdrop and receiving imessages on my mbp ( although it can cut into productivity if you ' re not careful ) .\n[{'aspect': 'airdrop', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: it is great quality .\n->it is great quality .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEverything is always cooked to perfection , the service is excellent , the decor cool and understated .\n->", + "output": "{\"text\": \"Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'understated', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it cost 8 dollars and shipping is not cheap .\n->it cost 8 dollars and shipping is not cheap .\n[{'aspect': 'shipping', 'opinion': 'not cheap', 'polarity': 'negative', 'category': 'SHIPPING#PRICE'}]\nExample:\ntext: this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n->this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n[{'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The Thai food is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Thai food is good .\n->", + "output": "{\"text\": \"The Thai food is good .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: highly recommended .\n->highly recommended .\n[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i especially loved the high ( er ) resolution display , compared to most other chromebooks .\n->i especially loved the high ( er ) resolution display , compared to most other chromebooks .\n[{'aspect': 'display', 'opinion': 'loved', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: Great selection of wine , and seafood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat selection of wine , and seafood .\n->", + "output": "{\"text\": \"Great selection of wine , and seafood .\", \"labels\": \"[{'aspect': 'selection of wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest i ' ve ever had ) .\n->the dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest i ' ve ever had ) .\n[{'aspect': 'dishes', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb sausages', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sardines with biscuits', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'large whole shrimp', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pistachio ice cream', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Acceptable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAcceptable prices .\n->", + "output": "{\"text\": \"Acceptable prices .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'Acceptable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !\n->i asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !\n[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Service was also horrible and the ambience is not that great .\n->Service was also horrible and the ambience is not that great .\n[{'aspect': 'Service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'NULL'}]\ntext: As I made the title , it 's an affordable restaurant for great taste .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs I made the title , it 's an affordable restaurant for great taste .\n->", + "output": "{\"text\": \"As I made the title , it 's an affordable restaurant for great taste .\", \"labels\": \"[{'aspect': 'taste', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was quick .\n->service was quick .\n[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: laptop in perfect condition .\n->laptop in perfect condition .\n[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Definitely not worth the price !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDefinitely not worth the price !\n->", + "output": "{\"text\": \"Definitely not worth the price !\", \"labels\": \"[{'aspect': 'price', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve used it for 3 days and still have n ' t plugged it in !\n->i ' ve used it for 3 days and still have n ' t plugged it in !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: The grilled cheese at home afterwards was better . ! !\n->The grilled cheese at home afterwards was better . ! !\n[{'aspect': 'grilled cheese', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Orsay , is a very pleasant throw back to traditional French food , and French service as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOrsay , is a very pleasant throw back to traditional French food , and French service as well .\n->", + "output": "{\"text\": \"Orsay , is a very pleasant throw back to traditional French food , and French service as well .\", \"labels\": \"[{'aspect': 'French food', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'French food', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Do n't expect to sit down inside though , there are only a few tables and they are always full .\n->Do n't expect to sit down inside though , there are only a few tables and they are always full .\n[{'aspect': 'tables', 'opinion': 'few', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'full', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n->Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n[{'aspect': 'Thai food', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: It 's easy to get a table for a large group and you do n't get hustled out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's easy to get a table for a large group and you do n't get hustled out .\n->", + "output": "{\"text\": \"It 's easy to get a table for a large group and you do n't get hustled out .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was a repeat visit and we ' ll definitely be back again .\n->this was a repeat visit and we ' ll definitely be back again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i ' m here to eat and enjoy the company of friends , seeking a pleasant experience .\n->i know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i ' m here to eat and enjoy the company of friends , seeking a pleasant experience .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Great food at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food at reasonable prices .\n->", + "output": "{\"text\": \"Great food at reasonable prices .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you purchase this product , go into it with realistic expectations and patience getting familiar with the setup and you ' ll probably love it , too !\n->if you purchase this product , go into it with realistic expectations and patience getting familiar with the setup and you ' ll probably love it , too !\n[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food tasted very good .\n->the food tasted very good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Salads are a delicious way to begin the meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSalads are a delicious way to begin the meal .\n->", + "output": "{\"text\": \"Salads are a delicious way to begin the meal .\", \"labels\": \"[{'aspect': 'Salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After really enjoying ourselves at the bar we sat down at a table and had dinner .\n->After really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'table', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: trackbad : it could be better but it ' s not bad 8 .\n->trackbad : it could be better but it ' s not bad 8 .\n[{'aspect': 'trackbad', 'opinion': 'better', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackbad', 'opinion': 'not bad', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: I ordered tamarind duck and my wife ordered noodles with ground beef , and we were both delighted by the way the dishes evoked Thai flavors in unexpected ways .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ordered tamarind duck and my wife ordered noodles with ground beef , and we were both delighted by the way the dishes evoked Thai flavors in unexpected ways .\n->", + "output": "{\"text\": \"I ordered tamarind duck and my wife ordered noodles with ground beef , and we were both delighted by the way the dishes evoked Thai flavors in unexpected ways .\", \"labels\": \"[{'aspect': 'tamarind duck', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'noodles with ground beef', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai flavors', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pretty fast processor .\n->pretty fast processor .\n[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: after the ssd upgrade , the computer is very fast .\n->after the ssd upgrade , the computer is very fast .\n[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: The prices are about $ 9 for an entree for dinner and even less for lunch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe prices are about $ 9 for an entree for dinner and even less for lunch .\n->", + "output": "{\"text\": \"The prices are about $ 9 for an entree for dinner and even less for lunch .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'less', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entree', 'opinion': 'less', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'less', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'less', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service has always been friendly and efficient .\n->Service has always been friendly and efficient .\n[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the charging issue i can live with as well , even though it is annoying .\n->the charging issue i can live with as well , even though it is annoying .\n[{'aspect': 'charging', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: Should you happen to be impressed by the cuisine definitely try it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nShould you happen to be impressed by the cuisine definitely try it .\n->", + "output": "{\"text\": \"Should you happen to be impressed by the cuisine definitely try it .\", \"labels\": \"[{'aspect': 'cuisine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my wife upgraded from the 2 gigs of ram version to this , and it seems like there is more than twice the performance .\n->my wife upgraded from the 2 gigs of ram version to this , and it seems like there is more than twice the performance .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: be prepared to wait , because the place is pretty tiny .\n->be prepared to wait , because the place is pretty tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: I had the black cod with yuzu sauce , which was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had the black cod with yuzu sauce , which was wonderful .\n->", + "output": "{\"text\": \"I had the black cod with yuzu sauce , which was wonderful .\", \"labels\": \"[{'aspect': 'black cod with yuzu sauce', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen looks fantastic and movies look great .\n->the screen looks fantastic and movies look great .\n[{'aspect': 'screen', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: great spot , whether looking for a couple of drinks or quiet dinner .\n->great spot , whether looking for a couple of drinks or quiet dinner .\n[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Prices too high for this cramped and unappealing resturant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPrices too high for this cramped and unappealing resturant .\n->", + "output": "{\"text\": \"Prices too high for this cramped and unappealing resturant .\", \"labels\": \"[{'aspect': 'Prices', 'opinion': 'high', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just got this thing today & was really excited about it but this has been frustrating & disappointing .\n->i just got this thing today & was really excited about it but this has been frustrating & disappointing .\n[{'aspect': 'NULL', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food is good , especially their more basic dishes , and the drinks are delicious .\n->The food is good , especially their more basic dishes , and the drinks are delicious .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Total hipster-wannabe attitude in an otherwise sweet spot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTotal hipster-wannabe attitude in an otherwise sweet spot .\n->", + "output": "{\"text\": \"Total hipster-wannabe attitude in an otherwise sweet spot .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop looks beautiful and the 8th gen intel core is a performance powerhouse .\n->the laptop looks beautiful and the 8th gen intel core is a performance powerhouse .\n[{'aspect': 'laptop', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '8th gen intel core', 'opinion': 'powerhouse', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: it was terrible .\n->it was terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Everything , from the soft bread , soggy salad , and 50 minute wait time , with an incredibly rude service to deliver below average food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEverything , from the soft bread , soggy salad , and 50 minute wait time , with an incredibly rude service to deliver below average food .\n->", + "output": "{\"text\": \"Everything , from the soft bread , soggy salad , and 50 minute wait time , with an incredibly rude service to deliver below average food .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: second where the heck is my other 8 gigs of ram ?\n->second where the heck is my other 8 gigs of ram ?\n[{'aspect': '8 gigs of ram', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n->you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n->", + "output": "{\"text\": \"The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'moist not dry', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the in - house lady dj on saturday nights has outrageously good taste in music , and moreover , takes requests .\n->the in - house lady dj on saturday nights has outrageously good taste in music , and moreover , takes requests .\n[{'aspect': 'in - house lady dj', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: in fact , the only way you know it ' s probably not new is because it didn ' t arrive in an apple original box ; it arrived wrapped ( neat and tight ) in bubble wrap in an amazon box .\n->in fact , the only way you know it ' s probably not new is because it didn ' t arrive in an apple original box ; it arrived wrapped ( neat and tight ) in bubble wrap in an amazon box .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: The menu prices are a bit expensive for what you get in quality and portion size .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu prices are a bit expensive for what you get in quality and portion size .\n->", + "output": "{\"text\": \"The menu prices are a bit expensive for what you get in quality and portion size .\", \"labels\": \"[{'aspect': 'menu prices', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very slow , and always hangs\n->very slow , and always hangs\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: This place is worth going even if only for their beer .\n->This place is worth going even if only for their beer .\n[{'aspect': 'beer', 'opinion': 'worth going', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n->", + "output": "{\"text\": \"The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'not over-bearing or rushed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n->however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n[{'aspect': 'kimchee', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'slimy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'korean fair', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: - i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n->- i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: ( food was delivered by a busboy , not waiter ) We got no cheese offered for the pasta , our water and wine glasses remained EMPTY our entire meal , when we would have easily spent another $ 20 on wine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( food was delivered by a busboy , not waiter ) We got no cheese offered for the pasta , our water and wine glasses remained EMPTY our entire meal , when we would have easily spent another $ 20 on wine .\n->", + "output": "{\"text\": \"( food was delivered by a busboy , not waiter ) We got no cheese offered for the pasta , our water and wine glasses remained EMPTY our entire meal , when we would have easily spent another $ 20 on wine .\", \"labels\": \"[{'aspect': 'water and wine glasses', 'opinion': 'EMPTY', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n->We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n[{'aspect': 'dinner specials', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner specials', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The takeout is great too since they give high quality tupperware as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe takeout is great too since they give high quality tupperware as well .\n->", + "output": "{\"text\": \"The takeout is great too since they give high quality tupperware as well .\", \"labels\": \"[{'aspect': 'takeout', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n->While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I was very impressed by this low-key upper eastsider and their authentically thai cuisine ! ! !\n->I was very impressed by this low-key upper eastsider and their authentically thai cuisine ! ! !\n[{'aspect': 'thai cuisine', 'opinion': 'authentically', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was not fresh , the sauces were bland and very oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was not fresh , the sauces were bland and very oily .\n->", + "output": "{\"text\": \"The food was not fresh , the sauces were bland and very oily .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: easy to setup and use\n->easy to setup and use\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: The atmosphere is unheralded , the service impecible , and the food magnificant .\n->The atmosphere is unheralded , the service impecible , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n->", + "output": "{\"text\": \"This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\", \"labels\": \"[{'aspect': 'night scene', 'opinion': 'alive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spot', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we paid a fixed pricce but got nothing ! !\n->we paid a fixed pricce but got nothing ! !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great buy .\n->great buy .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Again , the waitress was awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAgain , the waitress was awesome .\n->", + "output": "{\"text\": \"Again , the waitress was awesome .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely the best chromebook out there .\n->definitely the best chromebook out there .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I loved everythig about it-especially the shows and actors .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI loved everythig about it-especially the shows and actors .\n->", + "output": "{\"text\": \"I loved everythig about it-especially the shows and actors .\", \"labels\": \"[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my wife had barely touched that mess of a dish .\n->my wife had barely touched that mess of a dish .\n[{'aspect': 'dish', 'opinion': 'mess', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the max screen brightness isn ' t very bright\n->the max screen brightness isn ' t very bright\n[{'aspect': 'screen', 'opinion': \"' t very bright\", 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n->", + "output": "{\"text\": \"The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hot sauce', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchscreen works well , the apps generally work , the performance is good .\n->the touchscreen works well , the apps generally work , the performance is good .\n[{'aspect': 'touchscreen', 'opinion': 'well', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n->a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n[{'aspect': 'chrome os devices', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: Scalina Fedeli reminded me why service is so integral to fine dining .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nScalina Fedeli reminded me why service is so integral to fine dining .\n->", + "output": "{\"text\": \"Scalina Fedeli reminded me why service is so integral to fine dining .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'integral', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no backlit keyboard is kinda a bummer but i digress .\n->no backlit keyboard is kinda a bummer but i digress .\n[{'aspect': 'backlit keyboard', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: this place would be so much better served by being run by a group that actually understands customer service .\n->this place would be so much better served by being run by a group that actually understands customer service .\n[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i recommend the thai popcorn : )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recommend the thai popcorn : )\n->", + "output": "{\"text\": \"i recommend the thai popcorn : )\", \"labels\": \"[{'aspect': 'thai popcorn', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is usually good but it certainly is n't a relaxing place to go .\n->The food is usually good but it certainly is n't a relaxing place to go .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': \"is n't a relaxing\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: power cord broke within the first two weeks of use .\n->power cord broke within the first two weeks of use .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: Everything looks great , the drinks , the decor , the food , even the people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEverything looks great , the drinks , the decor , the food , even the people .\n->", + "output": "{\"text\": \"Everything looks great , the drinks , the decor , the food , even the people .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i wish they would change back to what it was before .\n->i wish they would change back to what it was before .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n->", + "output": "{\"text\": \"The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\", \"labels\": \"[{'aspect': 'parathas', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kebabs', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it also has enough power to multi - task .\n->it also has enough power to multi - task .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: go here .\n->go here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The tuna and wasabe potatoes are excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe tuna and wasabe potatoes are excellent .\n->", + "output": "{\"text\": \"The tuna and wasabe potatoes are excellent .\", \"labels\": \"[{'aspect': 'tuna', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wasabe potatoes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n->i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n[{'aspect': 'battery', 'opinion': 'better', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n->on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n[{'aspect': 'trackpad', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n->", + "output": "{\"text\": \"The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\", \"labels\": \"[{'aspect': 'coat check girls', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food is great .\n->food is great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The dinner menu is diverse and top-notch as well .\n->The dinner menu is diverse and top-notch as well .\n[{'aspect': 'dinner menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner menu', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n->", + "output": "{\"text\": \"We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\", \"labels\": \"[{'aspect': 'desserts', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cannoli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: openvpn support needs some serious work still .\n->openvpn support needs some serious work still .\n[{'aspect': 'openvpn support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#DESIGN_FEATURES'}]\nExample:\ntext: The hostess is rude to the point of being offensive .\n->The hostess is rude to the point of being offensive .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Good crowd , good outdoor seating , with a hip japanese vibe .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood crowd , good outdoor seating , with a hip japanese vibe .\n->", + "output": "{\"text\": \"Good crowd , good outdoor seating , with a hip japanese vibe .\", \"labels\": \"[{'aspect': 'outdoor seating', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vibe', 'opinion': 'hip', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , the batterly life that is reported by industry affiliates is way off .\n->also , the batterly life that is reported by industry affiliates is way off .\n[{'aspect': 'batterly life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n->We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We asked to be moved ( which took half an hour ) , and then were seated in a high traffic area in the back , even though the rest of the room was practically empty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe asked to be moved ( which took half an hour ) , and then were seated in a high traffic area in the back , even though the rest of the room was practically empty .\n->", + "output": "{\"text\": \"We asked to be moved ( which took half an hour ) , and then were seated in a high traffic area in the back , even though the rest of the room was practically empty .\", \"labels\": \"[{'aspect': 'room', 'opinion': 'empty', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n->Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the owner truly caters to all your needs .\n->the owner truly caters to all your needs .\n[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n->", + "output": "{\"text\": \"However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'drawn', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i still find the one piece touch pads unreliable to use even after all the tweaking .\n->i still find the one piece touch pads unreliable to use even after all the tweaking .\n[{'aspect': 'touch pads', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: good screen definition .\n->good screen definition .\n[{'aspect': 'screen definition', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhere tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n->", + "output": "{\"text\": \"Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\", \"labels\": \"[{'aspect': 'tanks', 'opinion': 'sad-looking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tanks', 'opinion': 'clear', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'healthy-looking', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in summary , it looks great and performs very well .\n->in summary , it looks great and performs very well .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: This is the perfect date spot for Williamsburg couples .\n->This is the perfect date spot for Williamsburg couples .\n[{'aspect': 'date spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The Halibut was too salty , dessert was so so ( do n't waste any of your calories ) and service was poor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Halibut was too salty , dessert was so so ( do n't waste any of your calories ) and service was poor .\n->", + "output": "{\"text\": \"The Halibut was too salty , dessert was so so ( do n't waste any of your calories ) and service was poor .\", \"labels\": \"[{'aspect': 'Halibut', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'so so', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keep up the good work .\n->keep up the good work .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the key board is one of the best i ' ve ever typed on .\n->the key board is one of the best i ' ve ever typed on .\n[{'aspect': 'key board', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: The view is spectacular , and the food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe view is spectacular , and the food is great .\n->", + "output": "{\"text\": \"The view is spectacular , and the food is great .\", \"labels\": \"[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have to say I have never had a disapointing meal here .\n->I have to say I have never had a disapointing meal here .\n[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s a good enough laptop .\n->it ' s a good enough laptop .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Good bagels and good cream cheese .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood bagels and good cream cheese .\n->", + "output": "{\"text\": \"Good bagels and good cream cheese .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheese', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n->it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n[{'aspect': 'anti - reflective coating', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: after that , it ' s actually been running well .\n->after that , it ' s actually been running well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The food is wonderful , tasty and filling , and the service is professional and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is wonderful , tasty and filling , and the service is professional and friendly .\n->", + "output": "{\"text\": \"The food is wonderful , tasty and filling , and the service is professional and friendly .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'filling', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A cool bar with great food , and tons of excellent beer .\n->A cool bar with great food , and tons of excellent beer .\n[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: do n ' t dine at tamarind for the vegetarian dishes , they are simply not up to par with the non - veg selections .\n->do n ' t dine at tamarind for the vegetarian dishes , they are simply not up to par with the non - veg selections .\n[{'aspect': 'vegetarian dishes', 'opinion': 'not up to par', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'non - veg selections', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: It is also extremely well priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is also extremely well priced .\n->", + "output": "{\"text\": \"It is also extremely well priced .\", \"labels\": \"[{'aspect': 'priced', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my boyfriend had prime rib it was good .\n->my boyfriend had prime rib it was good .\n[{'aspect': 'prime rib', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: It is definitely a good spot for snacks and chat .\n->It is definitely a good spot for snacks and chat .\n[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Always great service !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlways great service !\n->", + "output": "{\"text\": \"Always great service !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is good and the resturant is clean .\n->The service is good and the resturant is clean .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'resturant', 'opinion': 'clean', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n->by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: I LOVED THE SHOWS .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI LOVED THE SHOWS .\n->", + "output": "{\"text\": \"I LOVED THE SHOWS .\", \"labels\": \"[{'aspect': 'SHOWS', 'opinion': 'LOVED', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus support is horrible .\n->asus support is horrible .\n[{'aspect': 'asus support', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: went here on sat 1 / 26 and was disappointed .\n->went here on sat 1 / 26 and was disappointed .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: We could have made a meal of the yummy dumplings from the dumpling menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe could have made a meal of the yummy dumplings from the dumpling menu .\n->", + "output": "{\"text\": \"We could have made a meal of the yummy dumplings from the dumpling menu .\", \"labels\": \"[{'aspect': 'dumplings', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very let down by the reliability of this machine .\n->very let down by the reliability of this machine .\n[{'aspect': 'machine', 'opinion': 'let down', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: we did arrive late for our reservation so i can not complain too much about the wait for a table .\n->we did arrive late for our reservation so i can not complain too much about the wait for a table .\n[{'aspect': 'wait', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: Then , get ripped on free box wine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThen , get ripped on free box wine .\n->", + "output": "{\"text\": \"Then , get ripped on free box wine .\", \"labels\": \"[{'aspect': 'box wine', 'opinion': 'free', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - this chromebook has access to the android beta channel for android apps\n->- this chromebook has access to the android beta channel for android apps\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Terrible , terrible management - deserves to be shut-down .\n->Terrible , terrible management - deserves to be shut-down .\n[{'aspect': 'management', 'opinion': 'Terrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n->", + "output": "{\"text\": \"My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'ranting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'raving', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the machine is easy to use , snappy , and everything the reviewers say .\n->the machine is easy to use , snappy , and everything the reviewers say .\n[{'aspect': 'machine', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'machine', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the audio is great .\n->the audio is great .\n[{'aspect': 'audio', 'opinion': 'great', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: The food itself was just ok - nothing spectacular - but the service was awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food itself was just ok - nothing spectacular - but the service was awful .\n->", + "output": "{\"text\": \"The food itself was just ok - nothing spectacular - but the service was awful .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doesn ' t get super loud , but for normal usage situations it ' s fine .\n->it doesn ' t get super loud , but for normal usage situations it ' s fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s a great computer .\n->it ' s a great computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The pizza is yummy and I like the atmoshpere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza is yummy and I like the atmoshpere .\n->", + "output": "{\"text\": \"The pizza is yummy and I like the atmoshpere .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is not exactly five star , but thats not really a big deal .\n->service is not exactly five star , but thats not really a big deal .\n[{'aspect': 'service', 'opinion': 'not exactly five star', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: beautiful ips display .\n->beautiful ips display .\n[{'aspect': 'ips display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: Also , do n't plan on asking for your favorite roll , if it 's not on the menu , you ca n't have it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlso , do n't plan on asking for your favorite roll , if it 's not on the menu , you ca n't have it .\n->", + "output": "{\"text\": \"Also , do n't plan on asking for your favorite roll , if it 's not on the menu , you ca n't have it .\", \"labels\": \"[{'aspect': 'roll', 'opinion': 'favorite', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had my asus chromebook for several months and feel liberated from electronic hell .\n->i have had my asus chromebook for several months and feel liberated from electronic hell .\n[{'aspect': 'asus chromebook', 'opinion': 'liberated', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n->Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n[{'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood food .\n->", + "output": "{\"text\": \"Good food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: creative , consistent , fresh .\n->creative , consistent , fresh .\n[{'aspect': 'NULL', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'consistent', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: bad touchpad , jerky movement , imprecise , no controls to improve .\n->bad touchpad , jerky movement , imprecise , no controls to improve .\n[{'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'jerky', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'imprecise', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: those rolls were big , but not good and sashimi was n't fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthose rolls were big , but not good and sashimi was n't fresh .\n->", + "output": "{\"text\": \"those rolls were big , but not good and sashimi was n't fresh .\", \"labels\": \"[{'aspect': 'sashimi', 'opinion': \"was n't fresh\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 8g is enough to do most daily activities , i use nvidia geforce now to play much higher end games like destiny 2 , r6 and what not .\n->8g is enough to do most daily activities , i use nvidia geforce now to play much higher end games like destiny 2 , r6 and what not .\n[{'aspect': 'nvidia geforce', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'GRAPHICS#GENERAL'}]\nExample:\ntext: great pizza for lunch place .\n->great pizza for lunch place .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: try the spicy shrimp appetizer ( again , not the greatest value in the world but worth the price ) and the lamb vindaloo is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry the spicy shrimp appetizer ( again , not the greatest value in the world but worth the price ) and the lamb vindaloo is great .\n->", + "output": "{\"text\": \"try the spicy shrimp appetizer ( again , not the greatest value in the world but worth the price ) and the lamb vindaloo is great .\", \"labels\": \"[{'aspect': 'shrimp appetizer', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shrimp appetizer', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shrimp appetizer', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb vindaloo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our experience did not ever get any better .\n->our experience did not ever get any better .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Zero ambiance to boot .\n->Zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'Zero', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Food was good not great not worth the wait or another visit\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was good not great not worth the wait or another visit\n->", + "output": "{\"text\": \"Food was good not great not worth the wait or another visit\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n->Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch buffet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n->There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n[{'aspect': 'Blue Point oysters', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: I have had so many dinners here and it 's always been perfect - on a date with my husband , with my mom , with girlfriends and larger groups .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have had so many dinners here and it 's always been perfect - on a date with my husband , with my mom , with girlfriends and larger groups .\n->", + "output": "{\"text\": \"I have had so many dinners here and it 's always been perfect - on a date with my husband , with my mom , with girlfriends and larger groups .\", \"labels\": \"[{'aspect': 'dinners', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n->The Pad thai , lad nar and various other dishes all look good on paper but , I 've had better thai food in less asthetically pleasing places .\n[{'aspect': 'thai food', 'opinion': 'better', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n->You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n[{'aspect': 'crabmeat lasagna', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The menu has lots of options : I hope to go back to try those potato pancakes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu has lots of options : I hope to go back to try those potato pancakes .\n->", + "output": "{\"text\": \"The menu has lots of options : I hope to go back to try those potato pancakes .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'lots', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'potato pancakes', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the internet download speed with the acer was akin to an old dial - up modem speed .\n->the internet download speed with the acer was akin to an old dial - up modem speed .\n[{'aspect': 'internet download speed with the acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: customer service is difficult .\n->customer service is difficult .\n[{'aspect': 'customer service', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: The staff is n't the friendliest or most competent , and I am stickler for service , but everything else about this place makes up for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is n't the friendliest or most competent , and I am stickler for service , but everything else about this place makes up for it .\n->", + "output": "{\"text\": \"The staff is n't the friendliest or most competent , and I am stickler for service , but everything else about this place makes up for it .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'competent', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard key fragile .\n->keyboard key fragile .\n[{'aspect': 'keyboard', 'opinion': 'fragile', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: we have been to this place many times , and always have great food , wine , and service .\n->we have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n->", + "output": "{\"text\": \"My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unique apppetizers .\n->unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: oh and the customer service is garbage .\n->oh and the customer service is garbage .\n[{'aspect': 'customer service', 'opinion': 'garbage', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: Delicious crab cakes too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDelicious crab cakes too .\n->", + "output": "{\"text\": \"Delicious crab cakes too .\", \"labels\": \"[{'aspect': 'crab cakes', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great wine list ( italian ) , good food , service was INITIALLY fine .\n->great wine list ( italian ) , good food , service was INITIALLY fine .\n[{'aspect': 'wine list', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: horrible light bleed from the top edge of the screen\n->horrible light bleed from the top edge of the screen\n[{'aspect': 'screen', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n->", + "output": "{\"text\": \"I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n->They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n[{'aspect': 'reservation', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Definite go if you 're used to good Indian restaurant food from abroad .\n->Definite go if you 're used to good Indian restaurant food from abroad .\n[{'aspect': 'Indian restaurant food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The bruschetta and panini 's are so yummy !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bruschetta and panini 's are so yummy !\n->", + "output": "{\"text\": \"The bruschetta and panini 's are so yummy !\", \"labels\": \"[{'aspect': 'bruschetta', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'panini', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook 14 has a 1080p ips display with fantastic viewing angles and excellent brightness .\n->the chromebook 14 has a 1080p ips display with fantastic viewing angles and excellent brightness .\n[{'aspect': 'display', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'display', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: But when you are seated the waitresses are great , they explain everything on the menu , and the price of the food is really cheap for the service you get .\n->But when you are seated the waitresses are great , they explain everything on the menu , and the price of the food is really cheap for the service you get .\n[{'aspect': 'waitresses', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Always popular , always full , always a wait .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlways popular , always full , always a wait .\n->", + "output": "{\"text\": \"Always popular , always full , always a wait .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'always', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have to say , this is a very nice product .\n->i have to say , this is a very nice product .\n[{'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n->The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n[{'aspect': 'wait staff', 'opinion': 'freindly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n->", + "output": "{\"text\": \"If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\", \"labels\": \"[{'aspect': 'nori', 'opinion': 'not-so-fresh', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor is really blah , and not at all hip or happening .\n->The decor is really blah , and not at all hip or happening .\n[{'aspect': 'decor', 'opinion': 'blah', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'not at all hip', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: winnie and her staff are the best crew you can find serving you .\n->winnie and her staff are the best crew you can find serving you .\n[{'aspect': 'winnie', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: I ca n't wait for summer , when they serve outside on their gigantic patio .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ca n't wait for summer , when they serve outside on their gigantic patio .\n->", + "output": "{\"text\": \"I ca n't wait for summer , when they serve outside on their gigantic patio .\", \"labels\": \"[{'aspect': 'patio', 'opinion': 'gigantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is very nice for me .\n->the keyboard is very nice for me .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: Plus , on Wednesday nights the house wine is unlimited !\n->Plus , on Wednesday nights the house wine is unlimited !\n[{'aspect': 'house wine', 'opinion': 'unlimited', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Best Pastrami I ever had and great portion without being ridiculous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest Pastrami I ever had and great portion without being ridiculous .\n->", + "output": "{\"text\": \"Best Pastrami I ever had and great portion without being ridiculous .\", \"labels\": \"[{'aspect': 'Pastrami', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food has been consistant for years and it never lets you down .\n->The food has been consistant for years and it never lets you down .\n[{'aspect': 'food', 'opinion': 'consistant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good looking screen , has been bright enough for daily use , including outdoor .\n->good looking screen , has been bright enough for daily use , including outdoor .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: The first 2 courses were very good , but the chocolate sampler was too rich for me and the dessert wine far too sweet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe first 2 courses were very good , but the chocolate sampler was too rich for me and the dessert wine far too sweet .\n->", + "output": "{\"text\": \"The first 2 courses were very good , but the chocolate sampler was too rich for me and the dessert wine far too sweet .\", \"labels\": \"[{'aspect': 'courses', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chocolate sampler', 'opinion': 'too rich', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert wine', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i generally like this place .\n->i generally like this place .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: poor service and management\n->poor service and management\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Butter was melted , white wine warm , cheese oozing everywhere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nButter was melted , white wine warm , cheese oozing everywhere .\n->", + "output": "{\"text\": \"Butter was melted , white wine warm , cheese oozing everywhere .\", \"labels\": \"[{'aspect': 'Butter', 'opinion': 'melted', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'white wine', 'opinion': 'warm', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'oozing', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything about the experience has been terrible .\n->everything about the experience has been terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: has everything i was looking for , such as touch screen capability , tablet convertible , small bezel , lightweight and small footprint , * backlit * keyboard , not one but two * * usb - c * * ports , octane 2 .\n->has everything i was looking for , such as touch screen capability , tablet convertible , small bezel , lightweight and small footprint , * backlit * keyboard , not one but two * * usb - c * * ports , octane 2 .\n[{'aspect': 'touch screen capability', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'tablet', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'bezel', 'opinion': 'small', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'footprint', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'footprint', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '* backlit * keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': '* * usb - c * * ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\ntext: We had a party in their private room and they made it truly memorable and were very helpful in the planning .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had a party in their private room and they made it truly memorable and were very helpful in the planning .\n->", + "output": "{\"text\": \"We had a party in their private room and they made it truly memorable and were very helpful in the planning .\", \"labels\": \"[{'aspect': 'private room', 'opinion': 'truly memorable', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n->Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'water', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: If you live in new york city , you 'll find better food at small restaurants outside of time square and spend half the amount .\n->If you live in new york city , you 'll find better food at small restaurants outside of time square and spend half the amount .\n[{'aspect': 'food', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\ntext: It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n->", + "output": "{\"text\": \"It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n->bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n[{'aspect': 'specific unit', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'specific unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the performance of my chromebook is the issue .\n->the performance of my chromebook is the issue .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: We were disappointed with the pre-fixe menu of only 2 choices per course ( other restaurants offer 3 choices ) and ended up ordering a la carte .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were disappointed with the pre-fixe menu of only 2 choices per course ( other restaurants offer 3 choices ) and ended up ordering a la carte .\n->", + "output": "{\"text\": \"We were disappointed with the pre-fixe menu of only 2 choices per course ( other restaurants offer 3 choices ) and ended up ordering a la carte .\", \"labels\": \"[{'aspect': 'pre-fixe menu', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'choices per course', 'opinion': 'disappointed', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: every time in new york i make it a point to visit restaurant saul on smith street .\n->every time in new york i make it a point to visit restaurant saul on smith street .\n[{'aspect': 'restaurant saul', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: but it lost the coil whine roulette - - badly .\n->but it lost the coil whine roulette - - badly .\n[{'aspect': 'NULL', 'opinion': 'badly', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Authentic Pakistani food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAuthentic Pakistani food .\n->", + "output": "{\"text\": \"Authentic Pakistani food .\", \"labels\": \"[{'aspect': 'Pakistani food', 'opinion': 'Authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ll be back for sure .\n->i ' ll be back for sure .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: We had the lobster sandwich and it was FANTASTIC .\n->We had the lobster sandwich and it was FANTASTIC .\n[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n->", + "output": "{\"text\": \"The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i hate windows 10 .\n->i hate windows 10 .\n[{'aspect': 'windows 10', 'opinion': 'hate', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: asus support is responsive but ineffective .\n->asus support is responsive but ineffective .\n[{'aspect': 'asus support', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus support', 'opinion': 'ineffective', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\ntext: The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n->", + "output": "{\"text\": \"The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\", \"labels\": \"[{'aspect': 'eggplant parmesan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'baked ziti with meatsauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->The place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .\n[{'aspect': 'place', 'opinion': 'BISTRO', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'served efficiently', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after the 4th time i asked again and the waiter than said after our dinner .\n->after the 4th time i asked again and the waiter than said after our dinner .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Quality ingredients preparation all around , and a very fair price for NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nQuality ingredients preparation all around , and a very fair price for NYC .\n->", + "output": "{\"text\": \"Quality ingredients preparation all around , and a very fair price for NYC .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'Quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'fair', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I got an excellent piece of cheesecake and we had several other nice pastries .\n->I got an excellent piece of cheesecake and we had several other nice pastries .\n[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food and staff always surprise me with the new heights they are taken to .\n->The food and staff always surprise me with the new heights they are taken to .\n[{'aspect': 'food', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service was very good - prompt , attentive and non-intrusive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was very good - prompt , attentive and non-intrusive .\n->", + "output": "{\"text\": \"Service was very good - prompt , attentive and non-intrusive .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i highly recommend to anyone to give this place a try .\n->i highly recommend to anyone to give this place a try .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n->", + "output": "{\"text\": \"My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\", \"labels\": \"[{'aspect': 'crabmeat', 'opinion': 'unnecessarily', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: support got quite unpleasant when i ask about replacement .\n->support got quite unpleasant when i ask about replacement .\n[{'aspect': 'support', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: Food is great and inexpensive .\n->Food is great and inexpensive .\n[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: In such a crappy part of town to find a good value for lunch , this place is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn such a crappy part of town to find a good value for lunch , this place is great .\n->", + "output": "{\"text\": \"In such a crappy part of town to find a good value for lunch , this place is great .\", \"labels\": \"[{'aspect': 'value', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i returned it because the speaker is dead low .\n->i returned it because the speaker is dead low .\n[{'aspect': 'speaker', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: the performance of my chromebook is the issue .\n->the performance of my chromebook is the issue .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIn fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n->", + "output": "{\"text\": \"In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can carry both of them in a reasonably sized purse and not hurt my shoulder .\n->i can carry both of them in a reasonably sized purse and not hurt my shoulder .\n[{'aspect': 'NULL', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: told us to sit anywhere , and when we sat he said the table was reserved .\n->told us to sit anywhere , and when we sat he said the table was reserved .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Even though the restaurant was packed , we were seated promptly and even asked for a table upstairs with no problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven though the restaurant was packed , we were seated promptly and even asked for a table upstairs with no problems .\n->", + "output": "{\"text\": \"Even though the restaurant was packed , we were seated promptly and even asked for a table upstairs with no problems .\", \"labels\": \"[{'aspect': 'seated', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s not worth what i paid for it .\n->it ' s not worth what i paid for it .\n[{'aspect': 'it', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this chromebook is awesome .\n->this chromebook is awesome .\n[{'aspect': 'chromebook', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Service is friendly , prices are good - delivery time was a little slow , but for the way this pizza tastes , I 'm willing to overlook it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is friendly , prices are good - delivery time was a little slow , but for the way this pizza tastes , I 'm willing to overlook it .\n->", + "output": "{\"text\": \"Service is friendly , prices are good - delivery time was a little slow , but for the way this pizza tastes , I 'm willing to overlook it .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery time', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dishes came out around 5 minutes apart .\n->the dishes came out around 5 minutes apart .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: not only is the touchpad not great in use but it also feels poorly made .\n->not only is the touchpad not great in use but it also feels poorly made .\n[{'aspect': 'touchpad', 'opinion': 'not great', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\n->", + "output": "{\"text\": \"The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n->this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n[{'aspect': 'chromebook', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'boot up', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: none of this means the acer chromebook 14 is a bad chromebook ; on the contrary , it is an excellent chromebook !\n->none of this means the acer chromebook 14 is a bad chromebook ; on the contrary , it is an excellent chromebook !\n[{'aspect': 'acer chromebook 14', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->", + "output": "{\"text\": \"The service is excellent , the decor is great , and the food is delicious and comes in large portions .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was very good , a great deal , and the place its self was great .\n->The food was very good , a great deal , and the place its self was great .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: there were challenges with the chromebook specific apps and the google play store apps worked sometimes , but most of them had the size issue where they are only the size of a phone screen .\n->there were challenges with the chromebook specific apps and the google play store apps worked sometimes , but most of them had the size issue where they are only the size of a phone screen .\n[{'aspect': 'chromebook specific apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}, {'aspect': 'google play store apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: An excellent service\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAn excellent service\n->", + "output": "{\"text\": \"An excellent service\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was also very good .\n->Service was also very good .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i take all my nyc guests to vt ' s .\n->i take all my nyc guests to vt ' s .\n[{'aspect': \"vt ' s\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n->", + "output": "{\"text\": \"I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\", \"labels\": \"[{'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a chrome book it is excellent , but android support is unsatisfying .\n->as a chrome book it is excellent , but android support is unsatisfying .\n[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'android support', 'opinion': 'unsatisfying', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: Salads are a delicious way to begin the meal .\n->Salads are a delicious way to begin the meal .\n[{'aspect': 'Salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The pizza was really good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza was really good .\n->", + "output": "{\"text\": \"The pizza was really good .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n->* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The atmosphere is unheralded , the service impecible , and the food magnificant .\n->The atmosphere is unheralded , the service impecible , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The only possible drawback to this last point is that as of the date of this posting , the additional menu items are only written in Chinese .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only possible drawback to this last point is that as of the date of this posting , the additional menu items are only written in Chinese .\n->", + "output": "{\"text\": \"The only possible drawback to this last point is that as of the date of this posting , the additional menu items are only written in Chinese .\", \"labels\": \"[{'aspect': 'menu items', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can not go wrong with this place .\n->you can not go wrong with this place .\n[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the all - u - can - eat sushi is definitely in very poor quality .\n->the all - u - can - eat sushi is definitely in very poor quality .\n[{'aspect': 'all - u - can - eat sushi', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Kosher dills are the perfect compliment for your unforgetable sandwich and they give you plenty of them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nKosher dills are the perfect compliment for your unforgetable sandwich and they give you plenty of them .\n->", + "output": "{\"text\": \"Kosher dills are the perfect compliment for your unforgetable sandwich and they give you plenty of them .\", \"labels\": \"[{'aspect': 'Kosher dills', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sandwich', 'opinion': 'unforgetable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the worst customer service ever !\n->and the worst customer service ever !\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: I can not imagine a friendlier staff working in a restaurant .\n->I can not imagine a friendlier staff working in a restaurant .\n[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is so cheap and the waiters are nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is so cheap and the waiters are nice .\n->", + "output": "{\"text\": \"The food is so cheap and the waiters are nice .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you do n ' t need a full blown laptop this is a good choice .\n->if you do n ' t need a full blown laptop this is a good choice .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n->it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: The food there is so good that even to order out the wait is incredible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food there is so good that even to order out the wait is incredible .\n->", + "output": "{\"text\": \"The food there is so good that even to order out the wait is incredible .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'incredible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I love the fact that the pizza tastes so good and is so cheap .\n->I love the fact that the pizza tastes so good and is so cheap .\n[{'aspect': 'pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We had a party in their private room and they made it truly memorable and were very helpful in the planning .\n->We had a party in their private room and they made it truly memorable and were very helpful in the planning .\n[{'aspect': 'private room', 'opinion': 'truly memorable', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Their sushi , Kamikaze and other Rolls are fresh and well presented .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir sushi , Kamikaze and other Rolls are fresh and well presented .\n->", + "output": "{\"text\": \"Their sushi , Kamikaze and other Rolls are fresh and well presented .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'well presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Kamikaze', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Kamikaze', 'opinion': 'well presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Rolls', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Rolls', 'opinion': 'well presented', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 2 stars taken for horrible sound quality\n->2 stars taken for horrible sound quality\n[{'aspect': 'sound quality', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: therefore , my advice to you if you ' re a student and you need a laptop for school , this laptop is the best choice for you .\n->therefore , my advice to you if you ' re a student and you need a laptop for school , this laptop is the best choice for you .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: For authentic Thai food , look no further than Toons .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor authentic Thai food , look no further than Toons .\n->", + "output": "{\"text\": \"For authentic Thai food , look no further than Toons .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n->the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard response', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: The view is breathtaking the service is top notch ... the ambiance is wonderful .\n->The view is breathtaking the service is top notch ... the ambiance is wonderful .\n[{'aspect': 'view', 'opinion': 'breathtaking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The highlight of the night was the mayonaisse for my side of fries I received from one of the food runners , which is not good considering the bill was nearly $ 100 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe highlight of the night was the mayonaisse for my side of fries I received from one of the food runners , which is not good considering the bill was nearly $ 100 .\n->", + "output": "{\"text\": \"The highlight of the night was the mayonaisse for my side of fries I received from one of the food runners , which is not good considering the bill was nearly $ 100 .\", \"labels\": \"[{'aspect': 'mayonaisse', 'opinion': 'highlight', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food runners', 'opinion': 'not good', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other than my hope that it would be light enough to use as a tablet all the time , this is the chromebook i ' ve been wanting for a long time now .\n->other than my hope that it would be light enough to use as a tablet all the time , this is the chromebook i ' ve been wanting for a long time now .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: Average to good Thai food , but terrible delivery .\n->Average to good Thai food , but terrible delivery .\n[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n->", + "output": "{\"text\": \"I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\", \"labels\": \"[{'aspect': 'braised lamb shank in red wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'special', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never fails to please .\n->never fails to please .\n[{'aspect': 'NULL', 'opinion': 'please', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: - fingerprint reader is working well\n->- fingerprint reader is working well\n[{'aspect': 'fingerprint reader', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSmall servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n->", + "output": "{\"text\": \"Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\", \"labels\": \"[{'aspect': 'salmon', 'opinion': 'wasnt impressed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servings', 'opinion': 'Small', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n->I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n[{'aspect': 'Indian dining experience', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you are in need of a reliable laptop that is lightweight , fast , and convertible , i highly recommend the asus c302 !\n->if you are in need of a reliable laptop that is lightweight , fast , and convertible , i highly recommend the asus c302 !\n[{'aspect': 'asus c302', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus c302', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus c302', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus c302', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus c302', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'laptop', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: The food is very good for it 's price , better than most fried dumplings I 've had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is very good for it 's price , better than most fried dumplings I 've had .\n->", + "output": "{\"text\": \"The food is very good for it 's price , better than most fried dumplings I 've had .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried dumplings', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , this is where the build quality really shines .\n->again , this is where the build quality really shines .\n[{'aspect': 'build quality', 'opinion': 'shines', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: only wish the power button was somewhere else , its too easy to hit accidentally .\n->only wish the power button was somewhere else , its too easy to hit accidentally .\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\ntext: Obv caviar is top of the line but the rest of the menu is so diverse it gives you a chance to taste so manydifferent varietys .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nObv caviar is top of the line but the rest of the menu is so diverse it gives you a chance to taste so manydifferent varietys .\n->", + "output": "{\"text\": \"Obv caviar is top of the line but the rest of the menu is so diverse it gives you a chance to taste so manydifferent varietys .\", \"labels\": \"[{'aspect': 'Obv caviar', 'opinion': 'top of the line', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speaking of charges , it ' s so nice to be able to use usb c .\n->speaking of charges , it ' s so nice to be able to use usb c .\n[{'aspect': 'usb c', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n->yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n[{'aspect': 'place', 'opinion': 'classy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: I ate here a week ago and found most dishes average at best and too expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI ate here a week ago and found most dishes average at best and too expensive .\n->", + "output": "{\"text\": \"I ate here a week ago and found most dishes average at best and too expensive .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'too expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: look and feel of asus unit seems high quality but keyboard failed in 45 days .\n->look and feel of asus unit seems high quality but keyboard failed in 45 days .\n[{'aspect': 'asus unit', 'opinion': 'high', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'failed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n->We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n[{'aspect': 'scallops', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nStill , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n->", + "output": "{\"text\": \"Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'loving', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dining experiences', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been using this notebook for a month and i absolutely love it !\n->i have been using this notebook for a month and i absolutely love it !\n[{'aspect': 'notebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Unique apppetizers .\n->Unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'Unique', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOther guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->", + "output": "{\"text\": \"Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good for students that carry it from class to class .\n->good for students that carry it from class to class .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n->Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n[{'aspect': 'fruit of the oil', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'sweetness', 'polarity': 'positive', 'category': 'NULL'}]\ntext: not only does make the best pizza in NY , maybe anywhere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only does make the best pizza in NY , maybe anywhere .\n->", + "output": "{\"text\": \"not only does make the best pizza in NY , maybe anywhere .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i put it into tablet mode , everything is great .\n->when i put it into tablet mode , everything is great .\n[{'aspect': 'tablet mode', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - excellent keyboard in all aspects - feel , rigidity , and backlight\n->- excellent keyboard in all aspects - feel , rigidity , and backlight\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The spicy Tuna roll is huge and probably the best that I 've had at this price range .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe spicy Tuna roll is huge and probably the best that I 've had at this price range .\n->", + "output": "{\"text\": \"The spicy Tuna roll is huge and probably the best that I 've had at this price range .\", \"labels\": \"[{'aspect': 'Tuna roll', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price range', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is great system that is lightweight , has excellent battery life , and offers a fantastic keyboard .\n->this chromebook is great system that is lightweight , has excellent battery life , and offers a fantastic keyboard .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'system', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'system', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: everything is very smooth and fast .\n->everything is very smooth and fast .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Our waiter and all of the people helping him were attentive and genuine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur waiter and all of the people helping him were attentive and genuine .\n->", + "output": "{\"text\": \"Our waiter and all of the people helping him were attentive and genuine .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'genuine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n->My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n[{'aspect': 'crabmeat', 'opinion': 'unnecessarily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: All in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n->All in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n[{'aspect': 'sushi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->", + "output": "{\"text\": \"The place is a BISTRO which means : simple dishes and wine served efficiently in a bustling atmosphere .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'BISTRO', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'served efficiently', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I am not a vegetarian but , almost all the dishes were great .\n->I am not a vegetarian but , almost all the dishes were great .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: loved this chromebook .\n->loved this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: This is one of the best comfort food places in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is one of the best comfort food places in the city .\n->", + "output": "{\"text\": \"This is one of the best comfort food places in the city .\", \"labels\": \"[{'aspect': 'comfort food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the specs are good on this for a good cheap low end gaming machine .\n->the specs are good on this for a good cheap low end gaming machine .\n[{'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: fan noise could be a bit quieter when the cpu is being taxed but not annoyingly loud either , under lite use the pc is silent .\n->fan noise could be a bit quieter when the cpu is being taxed but not annoyingly loud either , under lite use the pc is silent .\n[{'aspect': 'fan', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: I had a huge pastrami sandwich on a roll .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had a huge pastrami sandwich on a roll .\n->", + "output": "{\"text\": \"I had a huge pastrami sandwich on a roll .\", \"labels\": \"[{'aspect': 'pastrami sandwich', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n->lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n[{'aspect': 'touch pad', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: worth the trip from manhattan .\n->worth the trip from manhattan .\n[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Though you will undoubtedly be seated at a table with what seems like barely enough room ( no matter what the size of your party ) , the warm atomosphere is worth the cramped quarters- you 'll have fun and forgot about the tight spot you 're in .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThough you will undoubtedly be seated at a table with what seems like barely enough room ( no matter what the size of your party ) , the warm atomosphere is worth the cramped quarters- you 'll have fun and forgot about the tight spot you 're in .\n->", + "output": "{\"text\": \"Though you will undoubtedly be seated at a table with what seems like barely enough room ( no matter what the size of your party ) , the warm atomosphere is worth the cramped quarters- you 'll have fun and forgot about the tight spot you 're in .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'enough', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'atomosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'room', 'opinion': 'enough', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spot', 'opinion': 'tight', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been using a chromebook now for three years and am totally satisfied .\n->i have been using a chromebook now for three years and am totally satisfied .\n[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food is great and the environment is even better .\n->the food is great and the environment is even better .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'environment', 'opinion': 'better', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: The fish was not fresh and the rice tasted old and stale .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fish was not fresh and the rice tasted old and stale .\n->", + "output": "{\"text\": \"The fish was not fresh and the rice tasted old and stale .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: first went here to enjoy their garden terrace .\n->first went here to enjoy their garden terrace .\n[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: Besides , when you have bad service , that 's less money you have to tip .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBesides , when you have bad service , that 's less money you have to tip .\n->", + "output": "{\"text\": \"Besides , when you have bad service , that 's less money you have to tip .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'money', 'opinion': 'less', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tip', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m still worried about the quality of capacitor and conductor inside this thing .\n->i ' m still worried about the quality of capacitor and conductor inside this thing .\n[{'aspect': 'capacitor', 'opinion': 'worried', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}, {'aspect': 'conductor', 'opinion': 'worried', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: crisp screen .\n->crisp screen .\n[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: The wine list is also really nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is also really nice .\n->", + "output": "{\"text\": \"The wine list is also really nice .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': \"ca n't be beat\", 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: From the moment you enter till the moment you walk out the friendly and helpful staff was was just Fantastic .\n->From the moment you enter till the moment you walk out the friendly and helpful staff was was just Fantastic .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'Fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->", + "output": "{\"text\": \"Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': \"ca n't be beat\", 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n->The waitstaff are all very busy , it 's not outstanding service , but I 've never been dealt with rudely .\n[{'aspect': 'waitstaff', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: This place is always packed .\n->This place is always packed .\n[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Compared to Ess-a , Tal offers a less doughy bagel !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCompared to Ess-a , Tal offers a less doughy bagel !\n->", + "output": "{\"text\": \"Compared to Ess-a , Tal offers a less doughy bagel !\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'less doughy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was average or above including some surprising tasty dishes .\n->the food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n->my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Ballato 's is consistently delicious authentic italian food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBallato 's is consistently delicious authentic italian food .\n->", + "output": "{\"text\": \"Ballato 's is consistently delicious authentic italian food .\", \"labels\": \"[{'aspect': 'italian food', 'opinion': 'delicious authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a lot of websites don ' t work properly with the chromebook , even though you ' re using the same browser .\n->a lot of websites don ' t work properly with the chromebook , even though you ' re using the same browser .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The menu has lots of options : I hope to go back to try those potato pancakes .\n->The menu has lots of options : I hope to go back to try those potato pancakes .\n[{'aspect': 'menu', 'opinion': 'lots', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'potato pancakes', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service was attentive , yet discreet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was attentive , yet discreet .\n->", + "output": "{\"text\": \"The service was attentive , yet discreet .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no dvd drive , but who uses those anymore anyway ?\n->no dvd drive , but who uses those anymore anyway ?\n[{'aspect': 'dvd drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: this is a painfully slow computer .\n->this is a painfully slow computer .\n[{'aspect': 'computer', 'opinion': 'painfully', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: They offer the same menu but have creative drinks that are loaded with alcohol and cheeky names -- but they do cost you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey offer the same menu but have creative drinks that are loaded with alcohol and cheeky names -- but they do cost you .\n->", + "output": "{\"text\": \"They offer the same menu but have creative drinks that are loaded with alcohol and cheeky names -- but they do cost you .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'same', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'creative', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 2015 version has the old keyboard with keys that you can actually type on without the fear of a typo every other word .\n->the 2015 version has the old keyboard with keys that you can actually type on without the fear of a typo every other word .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: The pizza is yummy and I like the atmoshpere .\n->The pizza is yummy and I like the atmoshpere .\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We went to eat at the Jekyll and Hyde restaurant on Friday night and really enjoyed the fun atmosphere and good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe went to eat at the Jekyll and Hyde restaurant on Friday night and really enjoyed the fun atmosphere and good food .\n->", + "output": "{\"text\": \"We went to eat at the Jekyll and Hyde restaurant on Friday night and really enjoyed the fun atmosphere and good food .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: went here on sat 1 / 26 and was disappointed .\n->went here on sat 1 / 26 and was disappointed .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Not a great place for family or general dining .\n->Not a great place for family or general dining .\n[{'aspect': 'place', 'opinion': 'Not a great', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Unless you are eating in the Pizzeria side of this place , and are not in a rush , this place is a bad idea .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nUnless you are eating in the Pizzeria side of this place , and are not in a rush , this place is a bad idea .\n->", + "output": "{\"text\": \"Unless you are eating in the Pizzeria side of this place , and are not in a rush , this place is a bad idea .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ' ll be there for every anniversary , birthday , valentines day . . .\n->you ' ll be there for every anniversary , birthday , valentines day . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: this place survives on reputation alone .\n->this place survives on reputation alone .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: Ambiance is barely romantic but management tries .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmbiance is barely romantic but management tries .\n->", + "output": "{\"text\": \"Ambiance is barely romantic but management tries .\", \"labels\": \"[{'aspect': 'Ambiance', 'opinion': 'barely romantic', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': 'tries', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n->it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Faan is sooo good .\n->Faan is sooo good .\n[{'aspect': 'Faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I must warn the reader that the portions sizes are very small ( especially the appetizers ) , so if you plan to eat until you are full and do not intend to order the chef 's special tasting menu , prepare to order and pay for an appetizer ( 1 dish for each person because the portions are not for sharing ) , a main entree , and the cold udon at the end of the meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI must warn the reader that the portions sizes are very small ( especially the appetizers ) , so if you plan to eat until you are full and do not intend to order the chef 's special tasting menu , prepare to order and pay for an appetizer ( 1 dish for each person because the portions are not for sharing ) , a main entree , and the cold udon at the end of the meal .\n->", + "output": "{\"text\": \"I must warn the reader that the portions sizes are very small ( especially the appetizers ) , so if you plan to eat until you are full and do not intend to order the chef 's special tasting menu , prepare to order and pay for an appetizer ( 1 dish for each person because the portions are not for sharing ) , a main entree , and the cold udon at the end of the meal .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'appetizers', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall : the specs for this computer looked pretty good , but after using it for a few weeks , the negatives outweigh the positives , and i ' m going to have to return it .\n->overall : the specs for this computer looked pretty good , but after using it for a few weeks , the negatives outweigh the positives , and i ' m going to have to return it .\n[{'aspect': 'specs for this computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is a lot of fun with live entertainment and all kinds of disney type special effects .\n->it is a lot of fun with live entertainment and all kinds of disney type special effects .\n[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: After we got our sashimi order , I could not believe how small the portions were !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAfter we got our sashimi order , I could not believe how small the portions were !\n->", + "output": "{\"text\": \"After we got our sashimi order , I could not believe how small the portions were !\", \"labels\": \"[{'aspect': 'sashimi', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the food was fantastic .\n->And the food was fantastic .\n[{'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: be prepared to wait a good five hours give or take until the system runs smoother .\n->be prepared to wait a good five hours give or take until the system runs smoother .\n[{'aspect': 'system', 'opinion': 'good', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'system', 'opinion': 'smoother', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Nothing better than buying a snapple for $ 3.25 too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNothing better than buying a snapple for $ 3.25 too .\n->", + "output": "{\"text\": \"Nothing better than buying a snapple for $ 3.25 too .\", \"labels\": \"[{'aspect': 'snapple', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well , i have to say , i ' m fairly impressed with my new chrome flipbook !\n->well , i have to say , i ' m fairly impressed with my new chrome flipbook !\n[{'aspect': 'chrome flipbook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard and display quality have always been asus strengths in my experience .\n->the keyboard and display quality have always been asus strengths in my experience .\n[{'aspect': 'display', 'opinion': 'strengths', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\n->", + "output": "{\"text\": \"The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\", \"labels\": \"[{'aspect': 'dosas', 'opinion': 'skimpy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dosas', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n->The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n->this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'computer replacement', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: The service was excellent and the food was delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was excellent and the food was delicious .\n->", + "output": "{\"text\": \"The service was excellent and the food was delicious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the addition of the play store puts the device into a sweet spot no other device can come close to matching .\n->the addition of the play store puts the device into a sweet spot no other device can come close to matching .\n[{'aspect': 'device', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i look forward to eating here again\n->i look forward to eating here again\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: If I wanted to deal with a crappy scene and annoying customers I 'd go out in Manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf I wanted to deal with a crappy scene and annoying customers I 'd go out in Manhattan .\n->", + "output": "{\"text\": \"If I wanted to deal with a crappy scene and annoying customers I 'd go out in Manhattan .\", \"labels\": \"[{'aspect': 'scene', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'customers', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n->ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: 5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n->5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n[{'aspect': 'emmc storage', 'opinion': 'slower', 'polarity': 'negative', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: Until you realize that their five minutes is meaningless and your wait may be anywhere from two to twenty minutes it may be frustrating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nUntil you realize that their five minutes is meaningless and your wait may be anywhere from two to twenty minutes it may be frustrating .\n->", + "output": "{\"text\": \"Until you realize that their five minutes is meaningless and your wait may be anywhere from two to twenty minutes it may be frustrating .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n->With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait-staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': \"does n't care\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'diner', 'opinion': 'glorified', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: but right out the box the battery will not charge .\n->but right out the box the battery will not charge .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n->", + "output": "{\"text\": \"Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\", \"labels\": \"[{'aspect': 'people serving', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"does n't quite match up\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , the laptop has no dvd drive , and no such driver dvd was provided .\n->however , the laptop has no dvd drive , and no such driver dvd was provided .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: so before you get angry do your homework on why the laptop may be acting strange .\n->so before you get angry do your homework on why the laptop may be acting strange .\n[{'aspect': 'laptop', 'opinion': 'angry', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n->", + "output": "{\"text\": \"The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\", \"labels\": \"[{'aspect': 'fillings', 'opinion': 'unconventional', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dosa batter', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n->i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: however , it ' s the service that leaves a bad taste in my mouth .\n->however , it ' s the service that leaves a bad taste in my mouth .\n[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->", + "output": "{\"text\": \"This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Great for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening .\n->Great for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening .\n[{'aspect': 'waiter', 'opinion': 'SUPER HAPPY', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n->", + "output": "{\"text\": \"The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\", \"labels\": \"[{'aspect': 'mussaman curry', 'opinion': 'thin', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fried tofu', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato', 'opinion': 'poorly cooked', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Be careful of portions - they 're HUGE .\n->Be careful of portions - they 're HUGE .\n[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n->and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n[{'aspect': 'google environment', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: Well , this place is so Ghetto its not even funny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWell , this place is so Ghetto its not even funny .\n->", + "output": "{\"text\": \"Well , this place is so Ghetto its not even funny .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'Ghetto', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'not even funny', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ca n ' t believe that it was , but please put the bag down before delivering food !\n->i ca n ' t believe that it was , but please put the bag down before delivering food !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i generally like this place .\n->i generally like this place .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n->", + "output": "{\"text\": \"We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\", \"labels\": \"[{'aspect': 'dining', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer ' s hardware is decent , but the materials are poor .\n->the computer ' s hardware is decent , but the materials are poor .\n[{'aspect': \"computer ' s hardware\", 'opinion': 'decent', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'materials', 'opinion': 'poor', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n->The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n[{'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Found service above average , but that could be because we were 13 of us .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFound service above average , but that could be because we were 13 of us .\n->", + "output": "{\"text\": \"Found service above average , but that could be because we were 13 of us .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i pray it stays open forever .\n->i pray it stays open forever .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'and', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'with', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Bagels are ok , but be sure not to make any special requests !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBagels are ok , but be sure not to make any special requests !\n->", + "output": "{\"text\": \"Bagels are ok , but be sure not to make any special requests !\", \"labels\": \"[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is horrible though .\n->battery life is horrible though .\n[{'aspect': 'battery life', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i also feel like apple hides too much under the hood from the user , but i suppose you can still work around that if you are so inclined .\n->i also feel like apple hides too much under the hood from the user , but i suppose you can still work around that if you are so inclined .\n[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\ntext: fine dining restaurant quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfine dining restaurant quality .\n->", + "output": "{\"text\": \"fine dining restaurant quality .\", \"labels\": \"[{'aspect': 'quality', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i call acer support and after an hour they can not help me .\n->i call acer support and after an hour they can not help me .\n[{'aspect': 'acer support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'place', 'opinion': 'exceeded my expectations', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Overall a disappointing experience for that price category .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOverall a disappointing experience for that price category .\n->", + "output": "{\"text\": \"Overall a disappointing experience for that price category .\", \"labels\": \"[{'aspect': 'price', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: some apps don ' t play well yet , but should with time .\n->some apps don ' t play well yet , but should with time .\n[{'aspect': 'some apps', 'opinion': \"' t play well\", 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: They have a huge selection of different cream cheeses and all of their salads are great .\n->They have a huge selection of different cream cheeses and all of their salads are great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was exceptional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was exceptional .\n->", + "output": "{\"text\": \"The food was exceptional .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what more could you want ?\n->what more could you want ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: third time in 5 months that the touchpad became unresponsive .\n->third time in 5 months that the touchpad became unresponsive .\n[{'aspect': 'touchpad', 'opinion': 'unresponsive', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: The portions are small but being that the food was so good makes up for that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portions are small but being that the food was so good makes up for that .\n->", + "output": "{\"text\": \"The portions are small but being that the food was so good makes up for that .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is tasty and portion sizes are appropriate .\n->The food is tasty and portion sizes are appropriate .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: had purchase this for my child to surf the net ; i ' d noticed ( and i can easily reproduce the problem ) , whenever her game needs to download an update , it seems like no data is coming down , while the harddrive activity ( read and write ) is very high .\n->had purchase this for my child to surf the net ; i ' d noticed ( and i can easily reproduce the problem ) , whenever her game needs to download an update , it seems like no data is coming down , while the harddrive activity ( read and write ) is very high .\n[{'aspect': 'harddrive activity', 'opinion': 'high', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: ( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n->", + "output": "{\"text\": \"( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\", \"labels\": \"[{'aspect': 'serve', 'opinion': 'impresses', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'serve', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and it was a very good price .\n->and it was a very good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: guaranteed excellent customer service !\n->guaranteed excellent customer service !\n[{'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: Sure , the setting is nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSure , the setting is nice .\n->", + "output": "{\"text\": \"Sure , the setting is nice .\", \"labels\": \"[{'aspect': 'setting', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i generally like this place .\n->i generally like this place .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: in practice , the device is heavier than is comfortable for this .\n->in practice , the device is heavier than is comfortable for this .\n[{'aspect': 'device', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'device', 'opinion': 'than is comfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSlightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n->", + "output": "{\"text\": \"Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\", \"labels\": \"[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great item by apple as usual .\n->great item by apple as usual .\n[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it has a very fast 64gb ssd and 4gb on lpddr3 memory .\n->it has a very fast 64gb ssd and 4gb on lpddr3 memory .\n[{'aspect': '64gb ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'lpddr3 memory', 'opinion': 'fast', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: Their whitefish salad is excellent -- all whitefish with a little mayo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir whitefish salad is excellent -- all whitefish with a little mayo .\n->", + "output": "{\"text\": \"Their whitefish salad is excellent -- all whitefish with a little mayo .\", \"labels\": \"[{'aspect': 'whitefish salad', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'whitefish', 'opinion': 'all', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mayo', 'opinion': 'little', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Yellowtail was particularly good as well .\n->The Yellowtail was particularly good as well .\n[{'aspect': 'Yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n->The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n[{'aspect': 'tables', 'opinion': 'crammed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'too close', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The chicken parm was edible but had canned tomato sauce and boxed pasta and the chicken with portobello mushrooms consisted of dry , inedible chicken with terrible sauce .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe chicken parm was edible but had canned tomato sauce and boxed pasta and the chicken with portobello mushrooms consisted of dry , inedible chicken with terrible sauce .\n->", + "output": "{\"text\": \"The chicken parm was edible but had canned tomato sauce and boxed pasta and the chicken with portobello mushrooms consisted of dry , inedible chicken with terrible sauce .\", \"labels\": \"[{'aspect': 'chicken', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tomato sauce', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: run do n ' t walk .\n->run do n ' t walk .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: In an area sadly lacking in decent Thai food , this is one of the best spots .\n->In an area sadly lacking in decent Thai food , this is one of the best spots .\n[{'aspect': 'Thai food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I recommend this place to everyone who asks me where to go for a good meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recommend this place to everyone who asks me where to go for a good meal .\n->", + "output": "{\"text\": \"I recommend this place to everyone who asks me where to go for a good meal .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n->Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n[{'aspect': 'candle-light', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'closely situated', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: go here for the drinks !\n->go here for the drinks !\n[{'aspect': 'drinks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: The waitresses are nice -- also you can just get counter service sit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waitresses are nice -- also you can just get counter service sit .\n->", + "output": "{\"text\": \"The waitresses are nice -- also you can just get counter service sit .\", \"labels\": \"[{'aspect': 'waitresses', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was a bit slow , but they were very friendly .\n->The service was a bit slow , but they were very friendly .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i haven ' t had issues with the track pad as others have .\n->i haven ' t had issues with the track pad as others have .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#USABILITY'}]\ntext: The only friendly staff member was the guy at the bar .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only friendly staff member was the guy at the bar .\n->", + "output": "{\"text\": \"The only friendly staff member was the guy at the bar .\", \"labels\": \"[{'aspect': 'staff member', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The best thing I tasted were the lambchops .\n->The best thing I tasted were the lambchops .\n[{'aspect': 'lambchops', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - great quality build .\n->- great quality build .\n[{'aspect': 'quality build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: You can certainly find restaurants that offer a superior fine dining experience , but for superb food at reasonable prices , La Villa ca n't be beat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou can certainly find restaurants that offer a superior fine dining experience , but for superb food at reasonable prices , La Villa ca n't be beat .\n->", + "output": "{\"text\": \"You can certainly find restaurants that offer a superior fine dining experience , but for superb food at reasonable prices , La Villa ca n't be beat .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they both pick up oils and such pretty easily .\n->they both pick up oils and such pretty easily .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Who has room for Cheesesticks with the best pizza in NYC !\n->Who has room for Cheesesticks with the best pizza in NYC !\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I can not imagine a friendlier staff working in a restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI can not imagine a friendlier staff working in a restaurant .\n->", + "output": "{\"text\": \"I can not imagine a friendlier staff working in a restaurant .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the seafood is amazing , there ' s a good wine list , and the ever - changing menu always offers some great surprises .\n->the seafood is amazing , there ' s a good wine list , and the ever - changing menu always offers some great surprises .\n[{'aspect': 'seafood', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wine list', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'menu', 'opinion': 'ever - changing', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'menu', 'opinion': 'great surprises', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the service was extremely fast and attentive ( thanks to the service button on your table ) but i barely understood 1 word when the waiter took our order .\n->the service was extremely fast and attentive ( thanks to the service button on your table ) but i barely understood 1 word when the waiter took our order .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service button', 'opinion': 'thanks to', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n->", + "output": "{\"text\": \"I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\", \"labels\": \"[{'aspect': 'upstairs', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n->I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n[{'aspect': 'drink', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it is solid , and does a decent job rendering pages , and paired with the extra ram here , it adequately handles streaming content in the background - such as youtube or spotify - and still maintains a decent browsing experience across eight to ten additional tabs ;\n->it is solid , and does a decent job rendering pages , and paired with the extra ram here , it adequately handles streaming content in the background - such as youtube or spotify - and still maintains a decent browsing experience across eight to ten additional tabs ;\n[{'aspect': 'NULL', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The red curry is weak and tasteless , the pad thai is stuck together and lumpy , the rice is often overcooked , and the seafood is pretty sketchy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe red curry is weak and tasteless , the pad thai is stuck together and lumpy , the rice is often overcooked , and the seafood is pretty sketchy .\n->", + "output": "{\"text\": \"The red curry is weak and tasteless , the pad thai is stuck together and lumpy , the rice is often overcooked , and the seafood is pretty sketchy .\", \"labels\": \"[{'aspect': 'red curry', 'opinion': 'weak', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'red curry', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'lumpy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'sketchy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Joe 's Pizza used to have the best slice until this pizzeria opened up .\n->Joe 's Pizza used to have the best slice until this pizzeria opened up .\n[{'aspect': 'slice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n->", + "output": "{\"text\": \"The wine list is extensive and can easily hike up an otherwise reasonably priced meal .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The view is breathtaking the service is top notch ... the ambiance is wonderful .\n->The view is breathtaking the service is top notch ... the ambiance is wonderful .\n[{'aspect': 'view', 'opinion': 'breathtaking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: lots of space , fast , and it will last a long time with ita top shelf technology .\n->lots of space , fast , and it will last a long time with ita top shelf technology .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: Saul is pretty good , but definitely not great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSaul is pretty good , but definitely not great .\n->", + "output": "{\"text\": \"Saul is pretty good , but definitely not great .\", \"labels\": \"[{'aspect': 'Saul', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Saul', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in an area sadly lacking in decent thai food , this is one of the best spots .\n->in an area sadly lacking in decent thai food , this is one of the best spots .\n[{'aspect': 'thai food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the price is right too .\n->the price is right too .\n[{'aspect': 'NULL', 'opinion': 'right', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: I would recommend Roxy 's for that , but not for their food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would recommend Roxy 's for that , but not for their food .\n->", + "output": "{\"text\": \"I would recommend Roxy 's for that , but not for their food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n->downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n[{'aspect': 'appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Best dish is nori-wrapped tuna .\n->Best dish is nori-wrapped tuna .\n[{'aspect': 'nori-wrapped tuna', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n->", + "output": "{\"text\": \"It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'impossible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s hard to navigate and i had to buy a usb mouse and a usb connector .\n->it ' s hard to navigate and i had to buy a usb mouse and a usb connector .\n[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: this unit is a great compromise between powerful cpu and gpu with good battery life .\n->this unit is a great compromise between powerful cpu and gpu with good battery life .\n[{'aspect': 'cpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'gpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}, {'aspect': 'unit', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: Their pad penang is delicious and everything else is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir pad penang is delicious and everything else is fantastic .\n->", + "output": "{\"text\": \"Their pad penang is delicious and everything else is fantastic .\", \"labels\": \"[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu is limited but almost all of the dishes are excellent .\n->The menu is limited but almost all of the dishes are excellent .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n->this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n[{'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'samsung chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The puke green walls leave a lot to be desired , but the food is very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe puke green walls leave a lot to be desired , but the food is very good .\n->", + "output": "{\"text\": \"The puke green walls leave a lot to be desired , but the food is very good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'walls', 'opinion': 'desired', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food now is inconsistent .\n->The food now is inconsistent .\n[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The flavors are very fresh and pretty unobtrusive , nothing flashy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe flavors are very fresh and pretty unobtrusive , nothing flashy .\n->", + "output": "{\"text\": \"The flavors are very fresh and pretty unobtrusive , nothing flashy .\", \"labels\": \"[{'aspect': 'flavors', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavors', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: \u2022 bright screen with good colors\n->\u2022 bright screen with good colors\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->", + "output": "{\"text\": \"I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\", \"labels\": \"[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Pad Thai is excellent here , as well .\n->The Pad Thai is excellent here , as well .\n[{'aspect': 'Pad Thai', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: initially the product lived up to the expectations .\n->initially the product lived up to the expectations .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n->", + "output": "{\"text\": \"The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\", \"labels\": \"[{'aspect': 'hanger steak', 'opinion': 'rubber', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tuna', 'opinion': 'flavorless', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n->there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n[{'aspect': 'screen resolution', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'not working well', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Our son loves pizza and we have a certified Neapolitan pizzaria in our home city ( Seattle ) , we liked this nearly as much - and the differences were more about personal preference than any reflection on either restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur son loves pizza and we have a certified Neapolitan pizzaria in our home city ( Seattle ) , we liked this nearly as much - and the differences were more about personal preference than any reflection on either restaurant .\n->", + "output": "{\"text\": \"Our son loves pizza and we have a certified Neapolitan pizzaria in our home city ( Seattle ) , we liked this nearly as much - and the differences were more about personal preference than any reflection on either restaurant .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'loves', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the food was fantastic .\n->And the food was fantastic .\n[{'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food now is inconsistent .\n->the food now is inconsistent .\n[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: big and soft as well as good lunch food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbig and soft as well as good lunch food .\n->", + "output": "{\"text\": \"big and soft as well as good lunch food .\", \"labels\": \"[{'aspect': 'lunch food', 'opinion': 'big', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch food', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is difficult to get used to due to the placement and spacing of the keys compared to a regular keyboard .\n->the keyboard is difficult to get used to due to the placement and spacing of the keys compared to a regular keyboard .\n[{'aspect': 'keyboard', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: you can get a completely delish martini in a glass ( that ' s about 2 1 / 2 drinks ) for $ 8 . 50 ( i recommend the vanilla shanty , mmmm ! ) in a great homey setting with great music .\n->you can get a completely delish martini in a glass ( that ' s about 2 1 / 2 drinks ) for $ 8 . 50 ( i recommend the vanilla shanty , mmmm ! ) in a great homey setting with great music .\n[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}, {'aspect': 'vanilla shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: No food snobs allowed , this place is for people who appreciate good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNo food snobs allowed , this place is for people who appreciate good food .\n->", + "output": "{\"text\": \"No food snobs allowed , this place is for people who appreciate good food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': \"chef ' s tasting menu\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: I ate clams oreganta and spectacular salad with perfectly marinated cucumbers and tomatoes with lots of shrimp and basil .\n->I ate clams oreganta and spectacular salad with perfectly marinated cucumbers and tomatoes with lots of shrimp and basil .\n[{'aspect': 'salad with perfectly marinated cucumbers and tomatoes with lots of shrimp and basil', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I liked the food at this quasi-thai restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI liked the food at this quasi-thai restaurant .\n->", + "output": "{\"text\": \"I liked the food at this quasi-thai restaurant .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop is pretty lightweight .\n->the laptop is pretty lightweight .\n[{'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the mac works like a new one .\n->the mac works like a new one .\n[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: As for the bar , this is another bad idea .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAs for the bar , this is another bad idea .\n->", + "output": "{\"text\": \"As for the bar , this is another bad idea .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recieved prompt service with a smile .\n->i recieved prompt service with a smile .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the trackpad is the ultimate difference - maker for me .\n->the trackpad is the ultimate difference - maker for me .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\ntext: The barebecued salmon is elegantly spiced and not at all dry .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe barebecued salmon is elegantly spiced and not at all dry .\n->", + "output": "{\"text\": \"The barebecued salmon is elegantly spiced and not at all dry .\", \"labels\": \"[{'aspect': 'barebecued salmon', 'opinion': 'elegantly spiced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'barebecued salmon', 'opinion': 'not at all dry', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard also feels nice and the backlighting is great .\n->the keyboard also feels nice and the backlighting is great .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlighting', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: we were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .\n->we were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: The highly spiced chai tea was great too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe highly spiced chai tea was great too .\n->", + "output": "{\"text\": \"The highly spiced chai tea was great too .\", \"labels\": \"[{'aspect': 'chai tea', 'opinion': 'highly spiced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chai tea', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere is unheralded , the service impecible , and the food magnificant .\n->the atmosphere is unheralded , the service impecible , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'impecible', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n->this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Seriously , this is the best all you can eat in town- As everyone says , the Spicy Tuna hand rolls are the best- have 4 of these , and you 've broken even .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSeriously , this is the best all you can eat in town- As everyone says , the Spicy Tuna hand rolls are the best- have 4 of these , and you 've broken even .\n->", + "output": "{\"text\": \"Seriously , this is the best all you can eat in town- As everyone says , the Spicy Tuna hand rolls are the best- have 4 of these , and you 've broken even .\", \"labels\": \"[{'aspect': 'Spicy Tuna hand rolls', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Bagels are ok , but be sure not to make any special requests !\n->Bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n->The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n[{'aspect': 'bruscetta', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mix of greens', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: While Sapphire is certainly not lacking in ambiance , and probably has the best decor of any Indian restaurant I have been to in New York City , the food was not what I had hoped for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile Sapphire is certainly not lacking in ambiance , and probably has the best decor of any Indian restaurant I have been to in New York City , the food was not what I had hoped for .\n->", + "output": "{\"text\": \"While Sapphire is certainly not lacking in ambiance , and probably has the best decor of any Indian restaurant I have been to in New York City , the food was not what I had hoped for .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'best', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'lacking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This tiny Williamsburg spot is always pleasantly surprising .\n->This tiny Williamsburg spot is always pleasantly surprising .\n[{'aspect': 'Williamsburg spot', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n->Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n[{'aspect': 'Quality of food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n->", + "output": "{\"text\": \"I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of the best\n->one of the best\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Always popular , always full , always a wait .\n->Always popular , always full , always a wait .\n[{'aspect': 'wait', 'opinion': 'always', 'polarity': 'negative', 'category': 'NULL'}]\ntext: His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHis wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n->", + "output": "{\"text\": \"His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'delightfully warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'gracious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'comforting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keeping the msi software is optional , a few of these are useful like burnrecovery to make a backup of your version of windows just in case anything happens .\n->keeping the msi software is optional , a few of these are useful like burnrecovery to make a backup of your version of windows just in case anything happens .\n[{'aspect': 'msi software', 'opinion': 'optional', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'burnrecovery', 'opinion': 'useful', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: this thing is slow !\n->this thing is slow !\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I have been a longtime fan of Holy Basil in the East Village , and while I do believe their food has slightly slipped in quality , I have been hesitant to be disloyal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have been a longtime fan of Holy Basil in the East Village , and while I do believe their food has slightly slipped in quality , I have been hesitant to be disloyal .\n->", + "output": "{\"text\": \"I have been a longtime fan of Holy Basil in the East Village , and while I do believe their food has slightly slipped in quality , I have been hesitant to be disloyal .\", \"labels\": \"[{'aspect': 'quality', 'opinion': 'slipped', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unique apppetizers .\n->unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s definately not a place to go if you want to impress someone .\n->it ' s definately not a place to go if you want to impress someone .\n[{'aspect': 'place', 'opinion': 'impress', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBut the best part about LS is the late night atmosphere , delightfully free of the BTs .\n->", + "output": "{\"text\": \"But the best part about LS is the late night atmosphere , delightfully free of the BTs .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'delightfully', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: next day during a video froze and just kept looping one section as it froze , and then froze and made a horrible loud scratching noise .\n->next day during a video froze and just kept looping one section as it froze , and then froze and made a horrible loud scratching noise .\n[{'aspect': 'NULL', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keyboard and track pad are both quite good , although i always use a real mouse .\n->the keyboard and track pad are both quite good , although i always use a real mouse .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'track pad', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: The staff there is very attentive and down to earth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff there is very attentive and down to earth .\n->", + "output": "{\"text\": \"The staff there is very attentive and down to earth .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n->portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: which , to be sure , is great , i had just hoped for something slightly more agile .\n->which , to be sure , is great , i had just hoped for something slightly more agile .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n->", + "output": "{\"text\": \"You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'amiable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it runs pretty fast but the keyboard is not lit , the speakers are on the bottom and the track pad is a pos .\n->it runs pretty fast but the keyboard is not lit , the speakers are on the bottom and the track pad is a pos .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'not lit', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}, {'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n->original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n[{'aspect': 'screen', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\ntext: The service is decent even when this small place is packed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is decent even when this small place is packed .\n->", + "output": "{\"text\": \"The service is decent even when this small place is packed .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'packed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n->i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i have not fully tested battery life but it seems to last about as long as advertised .\n->i have not fully tested battery life but it seems to last about as long as advertised .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: This is the best sushi in new york city - hands down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the best sushi in new york city - hands down .\n->", + "output": "{\"text\": \"This is the best sushi in new york city - hands down .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n->This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n[{'aspect': 'Jazz', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The staff is also attentive and friendly .\n->The staff is also attentive and friendly .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great value for the quality ingredients .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat value for the quality ingredients .\n->", + "output": "{\"text\": \"Great value for the quality ingredients .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love their drink menu .\n->love their drink menu .\n[{'aspect': 'drink menu', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: bukhara grill , the tagline says it all . . ` ` indian spice rave ' '\n->bukhara grill , the tagline says it all . . ` ` indian spice rave ' '\n[{'aspect': 'bukhara grill', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The Thali was small , thoroughly unremarkable , and $ 14.95 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe Thali was small , thoroughly unremarkable , and $ 14.95 .\n->", + "output": "{\"text\": \"The Thali was small , thoroughly unremarkable , and $ 14.95 .\", \"labels\": \"[{'aspect': 'Thali', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Thali', 'opinion': 'unremarkable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing more wonderful than the food ( which is exceptional ) is the service .\n->the only thing more wonderful than the food ( which is exceptional ) is the service .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: been there , done that , and new york , it ' s not that big a deal .\n->been there , done that , and new york , it ' s not that big a deal .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\ntext: Truly the mark of an attentive waiter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTruly the mark of an attentive waiter .\n->", + "output": "{\"text\": \"Truly the mark of an attentive waiter .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got this for my mother in law and she could not be happier with how it works .\n->i got this for my mother in law and she could not be happier with how it works .\n[{'aspect': 'NULL', 'opinion': 'not be happier', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n->The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n[{'aspect': 'service', 'opinion': 'busy', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The food here is rather good , but only if you like to wait for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food here is rather good , but only if you like to wait for it .\n->", + "output": "{\"text\": \"The food here is rather good , but only if you like to wait for it .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Unique apppetizers .\n->Unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'Unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very good wine choices .\n->very good wine choices .\n[{'aspect': 'wine choices', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: I got an excellent piece of cheesecake and we had several other nice pastries .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI got an excellent piece of cheesecake and we had several other nice pastries .\n->", + "output": "{\"text\": \"I got an excellent piece of cheesecake and we had several other nice pastries .\", \"labels\": \"[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recieved prompt service with a smile .\n->I recieved prompt service with a smile .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very nice i love it it work very well even i instal gta 5 it run but not enoght video card mb but run at all\n->very nice i love it it work very well even i instal gta 5 it run but not enoght video card mb but run at all\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'video card', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\ntext: The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n->", + "output": "{\"text\": \"The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'well trained', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n->Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n[{'aspect': 'lunch', 'opinion': 'busier', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'seldom crowded', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: if you are looking for a good quality , cheap eats - this is the place .\n->if you are looking for a good quality , cheap eats - this is the place .\n[{'aspect': 'eats', 'opinion': 'good quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: The food is inventive but still keeps traditional indian flavoring .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is inventive but still keeps traditional indian flavoring .\n->", + "output": "{\"text\": \"The food is inventive but still keeps traditional indian flavoring .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the freshest , best variety , and the fastest delivery .\n->the freshest , best variety , and the fastest delivery .\n[{'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n->the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n->", + "output": "{\"text\": \"The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'diner-ish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'sparse', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is worth an one - hour drive .\n->this place is worth an one - hour drive .\n[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: but i ' m growing ever more disenchanted with the core m3 processing speed .\n->but i ' m growing ever more disenchanted with the core m3 processing speed .\n[{'aspect': 'core m3', 'opinion': 'disenchanted', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: This is a cute place and could be good but they need to get their act together .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is a cute place and could be good but they need to get their act together .\n->", + "output": "{\"text\": \"This is a cute place and could be good but they need to get their act together .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it can also run modded skyrim with no issues , which is nice bonus for me .\n->it can also run modded skyrim with no issues , which is nice bonus for me .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: ca n ' t argue about that , but they are clearly over priced .\n->ca n ' t argue about that , but they are clearly over priced .\n[{'aspect': 'NULL', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: The lox is always fresh too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe lox is always fresh too .\n->", + "output": "{\"text\": \"The lox is always fresh too .\", \"labels\": \"[{'aspect': 'lox', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen turn black and won ' t turn on within a month rarely use .\n->screen turn black and won ' t turn on within a month rarely use .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: - jstorrent works nicely for any torrenting needs .\n->- jstorrent works nicely for any torrenting needs .\n[{'aspect': 'jstorrent', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSo all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n->", + "output": "{\"text\": \"So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\", \"labels\": \"[{'aspect': 'thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: but $ 1 for each small piece ? ? ?\n->but $ 1 for each small piece ? ? ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: Food was OK - fish was cooked well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was OK - fish was cooked well .\n->", + "output": "{\"text\": \"Food was OK - fish was cooked well .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for me has been worth the $ 500 for the computer .\n->for me has been worth the $ 500 for the computer .\n[{'aspect': 'computer', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n->not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: The duck confit is always amazing and the foie gras terrine with figs was out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe duck confit is always amazing and the foie gras terrine with figs was out of this world .\n->", + "output": "{\"text\": \"The duck confit is always amazing and the foie gras terrine with figs was out of this world .\", \"labels\": \"[{'aspect': 'foie gras terrine with figs', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck confit', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: that is just incredible .\n->that is just incredible .\n[{'aspect': 'NULL', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: you just have to deal with a low battery and that ' s all\n->you just have to deal with a low battery and that ' s all\n[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n->", + "output": "{\"text\": \"The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you must try the shrimp appetizers .\n->you must try the shrimp appetizers .\n[{'aspect': 'shrimp appetizers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it boots up instantaneously .\n->it boots up instantaneously .\n[{'aspect': 'boots up', 'opinion': 'instantaneously', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The staff offers impeccable service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff offers impeccable service .\n->", + "output": "{\"text\": \"The staff offers impeccable service .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i expected quite a bit more from such an expensive menu .\n->i expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: cirspy crust margherita pizza\n->cirspy crust margherita pizza\n[{'aspect': 'margherita pizza', 'opinion': 'cirspy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'crust', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n->", + "output": "{\"text\": \"We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\", \"labels\": \"[{'aspect': 'Gulab Jamun ( dessert )', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but it lost the coil whine roulette - - badly .\n->but it lost the coil whine roulette - - badly .\n[{'aspect': 'NULL', 'opinion': 'badly', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the computer itself looks great .\n->the computer itself looks great .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The place is sleek , modern and playful and i will return again frequently .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is sleek , modern and playful and i will return again frequently .\n->", + "output": "{\"text\": \"The place is sleek , modern and playful and i will return again frequently .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'playful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: update : i have had this computer for about 3 months now , and it is full of problems .\n->update : i have had this computer for about 3 months now , and it is full of problems .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the support website is incompetent .\n->the support website is incompetent .\n[{'aspect': 'support website', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->", + "output": "{\"text\": \"The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\", \"labels\": \"[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But they do n't have a toaster , which is strange .\n->But they do n't have a toaster , which is strange .\n[{'aspect': 'toaster', 'opinion': 'strange', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i packaged it right back up and sent it back the same day i got it .\n->i packaged it right back up and sent it back the same day i got it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The service was attentive , yet unimposing , the food was far better than many notorious restaurants in Midtown and the wine list is extensive and well priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was attentive , yet unimposing , the food was far better than many notorious restaurants in Midtown and the wine list is extensive and well priced .\n->", + "output": "{\"text\": \"The service was attentive , yet unimposing , the food was far better than many notorious restaurants in Midtown and the wine list is extensive and well priced .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'well priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: asus hit all the right notes on this one .\n->asus hit all the right notes on this one .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: Salads were fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSalads were fantastic .\n->", + "output": "{\"text\": \"Salads were fantastic .\", \"labels\": \"[{'aspect': 'Salads', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can watch 1080p video on it and it looks great .\n->you can watch 1080p video on it and it looks great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: they do n ' t concern much of customer ' s health , just want to make money .\n->they do n ' t concern much of customer ' s health , just want to make money .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: The owners and employees are friendly and their pizza is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe owners and employees are friendly and their pizza is fantastic .\n->", + "output": "{\"text\": \"The owners and employees are friendly and their pizza is fantastic .\", \"labels\": \"[{'aspect': 'owners', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'employees', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is excellent , special : that girl behind the bar , european chic .\n->The staff is excellent , special : that girl behind the bar , european chic .\n[{'aspect': 'staff', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'special', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n->The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->", + "output": "{\"text\": \"The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked-to-perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the internal flash memory is like greased lightning .\n->the internal flash memory is like greased lightning .\n[{'aspect': 'flash memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: nice computer that came with a bad fan .\n->nice computer that came with a bad fan .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'fan', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}]\ntext: Fast service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFast service .\n->", + "output": "{\"text\": \"Fast service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n->not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'battery charger', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: - i wish the sound quality was better .\n->- i wish the sound quality was better .\n[{'aspect': 'sound quality', 'opinion': 'better', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: The staff is excellent , special : that girl behind the bar , european chic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff is excellent , special : that girl behind the bar , european chic .\n->", + "output": "{\"text\": \"The staff is excellent , special : that girl behind the bar , european chic .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'special', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All the NYU students love this place so it makes for a fun young atmosphere .\n->All the NYU students love this place so it makes for a fun young atmosphere .\n[{'aspect': 'atmosphere', 'opinion': 'fun young', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: stop working the macbook pro\n->stop working the macbook pro\n[{'aspect': 'macbook pro', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: The service was bad , the food took to forever to come , we sat on the upper level .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was bad , the food took to forever to come , we sat on the upper level .\n->", + "output": "{\"text\": \"The service was bad , the food took to forever to come , we sat on the upper level .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n->at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: They pray to their Food Gods to make them into a good pizza like VT 's .\n->They pray to their Food Gods to make them into a good pizza like VT 's .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n->", + "output": "{\"text\": \"We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\", \"labels\": \"[{'aspect': 'quality', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'care', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first ever macbook and i will never go back .\n->this is my first ever macbook and i will never go back .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n->As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n[{'aspect': 'Lucky Strike', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Stick to dimsum , not super overpriced noodles .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nStick to dimsum , not super overpriced noodles .\n->", + "output": "{\"text\": \"Stick to dimsum , not super overpriced noodles .\", \"labels\": \"[{'aspect': 'noodles', 'opinion': 'not super overpriced', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only downside . . . they only take cash which is ok if you know about it ahead of time .\n->the only downside . . . they only take cash which is ok if you know about it ahead of time .\n[{'aspect': 'NULL', 'opinion': 'downside', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n->It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n[{'aspect': 'food', 'opinion': 'surprisingly fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service is fine and they allow you to enjoy the view .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is fine and they allow you to enjoy the view .\n->", + "output": "{\"text\": \"The service is fine and they allow you to enjoy the view .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'view', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am not a vegetarian but , almost all the dishes were great .\n->i am not a vegetarian but , almost all the dishes were great .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n->graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n[{'aspect': 'graphic', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->", + "output": "{\"text\": \"We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->the brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: having had it for just over a month , i have to say i am thoroughly impressed by its versatility and how stable the os is .\n->having had it for just over a month , i have to say i am thoroughly impressed by its versatility and how stable the os is .\n[{'aspect': 'os', 'opinion': 'versatility', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'stable', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: delicious simple food in nice outdoor atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicious simple food in nice outdoor atmosphere .\n->", + "output": "{\"text\": \"delicious simple food in nice outdoor atmosphere .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n->sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n->Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n[{'aspect': 'waiting area', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seat', 'opinion': 'all taken', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The only fallback on this restaurant is the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only fallback on this restaurant is the prices .\n->", + "output": "{\"text\": \"The only fallback on this restaurant is the prices .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n->( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n[{'aspect': 'serve', 'opinion': 'impresses', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'serve', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is a small , light , powerful device that until 2016 seemed custom fit for content creators , photographers , and other such lines of work or hobbies .\n->it is a small , light , powerful device that until 2016 seemed custom fit for content creators , photographers , and other such lines of work or hobbies .\n[{'aspect': 'device', 'opinion': 'small', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The hot and sour soup was unbearably hot and tasted of only pepper and nothing else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe hot and sour soup was unbearably hot and tasted of only pepper and nothing else .\n->", + "output": "{\"text\": \"The hot and sour soup was unbearably hot and tasted of only pepper and nothing else .\", \"labels\": \"[{'aspect': 'soup', 'opinion': 'unbearably hot', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pro : light , reasonable price , fast .\n->pro : light , reasonable price , fast .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: We had a wonderful meal at Naples 45 a month ago on a visit to NYC .\n->We had a wonderful meal at Naples 45 a month ago on a visit to NYC .\n[{'aspect': 'meal', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is delicious .\n->", + "output": "{\"text\": \"The food is delicious .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fries are yummy .\n->The fries are yummy .\n[{'aspect': 'fries', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Try ordering from the regular menu , then you would not regret !\n->Try ordering from the regular menu , then you would not regret !\n[{'aspect': 'menu', 'opinion': 'regret', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Their wines by the glass are a great accompaniment and you can eat like a king with wine for under $ 30 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir wines by the glass are a great accompaniment and you can eat like a king with wine for under $ 30 .\n->", + "output": "{\"text\": \"Their wines by the glass are a great accompaniment and you can eat like a king with wine for under $ 30 .\", \"labels\": \"[{'aspect': 'wines by the glass', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They were such a rip-off ( $ 8.95 for four small meat patties in steamed buns ) and not worth trying .\n->They were such a rip-off ( $ 8.95 for four small meat patties in steamed buns ) and not worth trying .\n[{'aspect': 'meat patties in steamed buns', 'opinion': 'rip-off', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meat patties in steamed buns', 'opinion': 'not worth trying', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the screen is bright and color spread is good .\n->the screen is bright and color spread is good .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'color spread', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: This place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n->", + "output": "{\"text\": \"This place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'correct', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fried rice is really good too .\n->The fried rice is really good too .\n[{'aspect': 'fried rice', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n->Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n[{'aspect': 'space', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The atmosphere is much better than Sripraphai ( more modern and sleek ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is much better than Sripraphai ( more modern and sleek ) .\n->", + "output": "{\"text\": \"The atmosphere is much better than Sripraphai ( more modern and sleek ) .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s absolutely silent .\n->it ' s absolutely silent .\n[{'aspect': 'NULL', 'opinion': 'silent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: power plug doesn ' t fit well - connection is erratic .\n->power plug doesn ' t fit well - connection is erratic .\n[{'aspect': 'power plug', 'opinion': \"' t fit well\", 'polarity': 'negative', 'category': 'POWER_SUPPLY#CONNECTIVITY'}, {'aspect': 'power plug', 'opinion': 'erratic', 'polarity': 'negative', 'category': 'POWER_SUPPLY#CONNECTIVITY'}]\ntext: The food is outstanding and the service is quick , friendly and very professional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is outstanding and the service is quick , friendly and very professional .\n->", + "output": "{\"text\": \"The food is outstanding and the service is quick , friendly and very professional .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had a party in their private room and they made it truly memorable and were very helpful in the planning .\n->We had a party in their private room and they made it truly memorable and were very helpful in the planning .\n[{'aspect': 'private room', 'opinion': 'truly memorable', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i love this chromebook !\n->i love this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->", + "output": "{\"text\": \"The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i still find the one piece touch pads unreliable to use even after all the tweaking .\n->i still find the one piece touch pads unreliable to use even after all the tweaking .\n[{'aspect': 'touch pads', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: dessert : pure disaster .\n->dessert : pure disaster .\n[{'aspect': 'dessert', 'opinion': 'disaster', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: I had a late dinner at Lucky Stike , a great name for a joint if ever I saw one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had a late dinner at Lucky Stike , a great name for a joint if ever I saw one .\n->", + "output": "{\"text\": \"I had a late dinner at Lucky Stike , a great name for a joint if ever I saw one .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sadly after barely a year old it crashes all the time , the touch screen rarely works , and the track pad stops working until a reboot on occasion .\n->sadly after barely a year old it crashes all the time , the touch screen rarely works , and the track pad stops working until a reboot on occasion .\n[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: Make sure you try this place as often as you can .\n->Make sure you try this place as often as you can .\n[{'aspect': 'place', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Service is fast and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService is fast and friendly .\n->", + "output": "{\"text\": \"Service is fast and friendly .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n->the pizza was delivered cold and the cheese was n ' t even fully melted !\n[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: - excellent keyboard in all aspects - feel , rigidity , and backlight\n->- excellent keyboard in all aspects - feel , rigidity , and backlight\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: And they have these home made potato chips at the bar that are the most delicious things in the world !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd they have these home made potato chips at the bar that are the most delicious things in the world !\n->", + "output": "{\"text\": \"And they have these home made potato chips at the bar that are the most delicious things in the world !\", \"labels\": \"[{'aspect': 'potato chips', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: regardless to the fact that the outside box was damaged , the inside box was fine .\n->regardless to the fact that the outside box was damaged , the inside box was fine .\n[{'aspect': 'outside box', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'inside box', 'opinion': 'fine', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: the screen is pleasantly satisfying with the touch screen and foldability .\n->the screen is pleasantly satisfying with the touch screen and foldability .\n[{'aspect': 'screen', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: My husband and I enjoyed each of the 6 taste size portions and left completely full .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy husband and I enjoyed each of the 6 taste size portions and left completely full .\n->", + "output": "{\"text\": \"My husband and I enjoyed each of the 6 taste size portions and left completely full .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good quality all around hardware + software , of course that is what apple is known for .\n->good quality all around hardware + software , of course that is what apple is known for .\n[{'aspect': 'hardware', 'opinion': 'good', 'polarity': 'positive', 'category': 'HARDWARE#QUALITY'}, {'aspect': 'software', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#QUALITY'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: my girlfriend works from home with it and has no problems at all to do online classes with it .\n->my girlfriend works from home with it and has no problems at all to do online classes with it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: We all ate pasta entre'es , which were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe all ate pasta entre'es , which were great .\n->", + "output": "{\"text\": \"We all ate pasta entre'es , which were great .\", \"labels\": \"[{'aspect': \"pasta entre'es\", 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the small battery capacity is the number one reason i would not buy this product and would recommend with that caveat being disclosed .\n->the small battery capacity is the number one reason i would not buy this product and would recommend with that caveat being disclosed .\n[{'aspect': 'battery capacity', 'opinion': 'small', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: so the audio can easily be muffled .\n->so the audio can easily be muffled .\n[{'aspect': 'audio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: The steak was excellent and one of the best I have had ( I tasted the butter intitally but in no way did it overwhelm the flavor of the meat ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe steak was excellent and one of the best I have had ( I tasted the butter intitally but in no way did it overwhelm the flavor of the meat ) .\n->", + "output": "{\"text\": \"The steak was excellent and one of the best I have had ( I tasted the butter intitally but in no way did it overwhelm the flavor of the meat ) .\", \"labels\": \"[{'aspect': 'steak', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard on this unit is actually quite nice .\n->the keyboard on this unit is actually quite nice .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: super easy to use .\n->super easy to use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: The food was boring and expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was boring and expensive .\n->", + "output": "{\"text\": \"The food was boring and expensive .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'boring', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We always have a delicious meal and always leave feeling satisfied .\n->We always have a delicious meal and always leave feeling satisfied .\n[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n->i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n[{'aspect': 'voltage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->", + "output": "{\"text\": \"The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good quality .\n->good quality .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: * * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n->* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: Terrific menu full of unique rolls and special dishes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTerrific menu full of unique rolls and special dishes .\n->", + "output": "{\"text\": \"Terrific menu full of unique rolls and special dishes .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'Terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n->Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n->received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n[{'aspect': 'cpu', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: The service is ok but could be better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is ok but could be better .\n->", + "output": "{\"text\": \"The service is ok but could be better .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'could be better', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unique apppetizers .\n->unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: and the worst customer service ever !\n->and the worst customer service ever !\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: We love the food , drinks , and atmosphere !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe love the food , drinks , and atmosphere !\n->", + "output": "{\"text\": \"We love the food , drinks , and atmosphere !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Downstairs lounge is always a good attraction\n->Downstairs lounge is always a good attraction\n[{'aspect': 'Downstairs lounge', 'opinion': 'good attraction', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: although the sound is not that good , this device replaced my ipad and i have never missed it !\n->although the sound is not that good , this device replaced my ipad and i have never missed it !\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->", + "output": "{\"text\": \"I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n->i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n[{'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'processor', 'opinion': 'faster', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'google play store', 'opinion': 'compatibility', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTook my mom for Mother 's Day , and the maitre d ' was pretty rude .\n->", + "output": "{\"text\": \"Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\", \"labels\": \"[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was n ' t thrilled to have to wait on line for thirty minutes , but i guess that ' s the price you pay for a popular place .\n->i was n ' t thrilled to have to wait on line for thirty minutes , but i guess that ' s the price you pay for a popular place .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: not a great place for family or general dining .\n->not a great place for family or general dining .\n[{'aspect': 'place', 'opinion': 'not a great', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: The vibe is very relaxed and cozy , service was great and the food was excellent !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe vibe is very relaxed and cozy , service was great and the food was excellent !\n->", + "output": "{\"text\": \"The vibe is very relaxed and cozy , service was great and the food was excellent !\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vibe', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus has not responded to numerous request for an update of the status of repair .\n->asus has not responded to numerous request for an update of the status of repair .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: the battery life is a huge selling point in my opinion : even after multiple shut - downs / start - ups throughout the day , i get about 10 hours total run - time , and charging only takes about an hour .\n->the battery life is a huge selling point in my opinion : even after multiple shut - downs / start - ups throughout the day , i get about 10 hours total run - time , and charging only takes about an hour .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: The service was friendly and the atmosphere was casual .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was friendly and the atmosphere was casual .\n->", + "output": "{\"text\": \"The service was friendly and the atmosphere was casual .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the yellowtail was particularly good as well .\n->the yellowtail was particularly good as well .\n[{'aspect': 'yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: It 's definately not a place to go if you want to impress someone .\n->It 's definately not a place to go if you want to impress someone .\n[{'aspect': 'place', 'opinion': 'impress', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I recommend their Pad See Ew , Pork Chops or Tofu plates .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recommend their Pad See Ew , Pork Chops or Tofu plates .\n->", + "output": "{\"text\": \"I recommend their Pad See Ew , Pork Chops or Tofu plates .\", \"labels\": \"[{'aspect': 'Pad See Ew', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pork Chops', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Tofu plates', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nothing on it feels cheap at all .\n->nothing on it feels cheap at all .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: The location is perfect .\n->The location is perfect .\n[{'aspect': 'location', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is the best secret place in midtown ' , I heard that from the bartender , after having brilliant food ( try steak with portobello mushrooms ) and drinks on the bar last Tuesday .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the best secret place in midtown ' , I heard that from the bartender , after having brilliant food ( try steak with portobello mushrooms ) and drinks on the bar last Tuesday .\n->", + "output": "{\"text\": \"this is the best secret place in midtown ' , I heard that from the bartender , after having brilliant food ( try steak with portobello mushrooms ) and drinks on the bar last Tuesday .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak with portobello mushrooms', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak with portobello mushrooms', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the second issue i have with this machine so far is that if you have several gmail accounts , it gets really confusing to find your files , where they go , where they were saved .\n->the second issue i have with this machine so far is that if you have several gmail accounts , it gets really confusing to find your files , where they go , where they were saved .\n[{'aspect': 'machine', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'gmail accounts', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: however , after having the computer for about 4 months it suddenly died one day and would not turn on .\n->however , after having the computer for about 4 months it suddenly died one day and would not turn on .\n[{'aspect': 'computer', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The service was excellent - friendly and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was excellent - friendly and attentive .\n->", + "output": "{\"text\": \"The service was excellent - friendly and attentive .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The hot dogs were cold in the middle and the buns were stale .\n->The hot dogs were cold in the middle and the buns were stale .\n[{'aspect': 'hot dogs', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'buns', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i liked the fact that lenovo came with the microsoft programs on it .\n->i liked the fact that lenovo came with the microsoft programs on it .\n[{'aspect': 'lenovo', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The dinner menu is diverse and top-notch as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dinner menu is diverse and top-notch as well .\n->", + "output": "{\"text\": \"The dinner menu is diverse and top-notch as well .\", \"labels\": \"[{'aspect': 'dinner menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner menu', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n->he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n[{'aspect': 'uni hand roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n->My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n[{'aspect': 'food', 'opinion': 'opposite', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I do n't like Indian food too much and this was delicious , however you want to factor that into the equation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI do n't like Indian food too much and this was delicious , however you want to factor that into the equation .\n->", + "output": "{\"text\": \"I do n't like Indian food too much and this was delicious , however you want to factor that into the equation .\", \"labels\": \"[{'aspect': 'Indian food', 'opinion': \"do n't like\", 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the staff is absolutely professional ! !\n->all the staff is absolutely professional ! !\n[{'aspect': 'staff', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Seating is always prompt , though the restaurant does fill up in the evening .\n->Seating is always prompt , though the restaurant does fill up in the evening .\n[{'aspect': 'Seating', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\ntext: at taj , vegetarians can rejoice-all the dishes are manna from heaven .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat taj , vegetarians can rejoice-all the dishes are manna from heaven .\n->", + "output": "{\"text\": \"at taj , vegetarians can rejoice-all the dishes are manna from heaven .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n->and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n->battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: Jimmy 's is hands down the hottest night spot in the Bronx .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nJimmy 's is hands down the hottest night spot in the Bronx .\n->", + "output": "{\"text\": \"Jimmy 's is hands down the hottest night spot in the Bronx .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'hottest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n->The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n[{'aspect': 'coat check girls', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n->and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n[{'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'congee', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'noodles', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'rice dishes', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: The $ 300 bill was a bit steep , but the experience was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe $ 300 bill was a bit steep , but the experience was great .\n->", + "output": "{\"text\": \"The $ 300 bill was a bit steep , but the experience was great .\", \"labels\": \"[{'aspect': 'bill', 'opinion': 'steep', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In such a crappy part of town to find a good value for lunch , this place is great .\n->In such a crappy part of town to find a good value for lunch , this place is great .\n[{'aspect': 'value', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: bought this for someone else , can ' t believe how good it is .\n->bought this for someone else , can ' t believe how good it is .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Nice ambiance , nice little bar , good bartender , Francois , and good service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNice ambiance , nice little bar , good bartender , Francois , and good service .\n->", + "output": "{\"text\": \"Nice ambiance , nice little bar , good bartender , Francois , and good service .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bartender', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambiance and music funky , which I enjoy .\n->Ambiance and music funky , which I enjoy .\n[{'aspect': 'Ambiance', 'opinion': 'funky', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'funky', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We walked in on a Wednesday night and were seated promptly .\n->We walked in on a Wednesday night and were seated promptly .\n[{'aspect': 'seated', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n->", + "output": "{\"text\": \"The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\", \"labels\": \"[{'aspect': 'sauce', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck noodles', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n->i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n[{'aspect': 'lenovo ideapad 320', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: trackpad is nice and quiet and responsive .\n->trackpad is nice and quiet and responsive .\n[{'aspect': 'trackpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: I would definitely go back -- if only for some of those exotic martinis on the blackboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would definitely go back -- if only for some of those exotic martinis on the blackboard .\n->", + "output": "{\"text\": \"I would definitely go back -- if only for some of those exotic martinis on the blackboard .\", \"labels\": \"[{'aspect': 'martinis', 'opinion': 'exotic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best pastrami i ever had and great portion without being ridiculous .\n->best pastrami i ever had and great portion without being ridiculous .\n[{'aspect': 'pastrami', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: fast , thin , great battery life .\n->fast , thin , great battery life .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: Simple comfort food and what hot and large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSimple comfort food and what hot and large portions .\n->", + "output": "{\"text\": \"Simple comfort food and what hot and large portions .\", \"labels\": \"[{'aspect': 'comfort food', 'opinion': 'Simple comfort', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Overall , not worth the money .\n->Overall , not worth the money .\n[{'aspect': 'money', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this by far has been the easiest to set up and use .\n->this by far has been the easiest to set up and use .\n[{'aspect': 'set up', 'opinion': 'easiest', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: The bagels are also reasonably priced for NYC .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bagels are also reasonably priced for NYC .\n->", + "output": "{\"text\": \"The bagels are also reasonably priced for NYC .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fans can get loud .\n->fans can get loud .\n[{'aspect': 'fans', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\nExample:\ntext: service was slow , but the people were friendly .\n->service was slow , but the people were friendly .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: Decor leaves something to be desired .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDecor leaves something to be desired .\n->", + "output": "{\"text\": \"Decor leaves something to be desired .\", \"labels\": \"[{'aspect': 'Decor', 'opinion': 'desired', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Moderate prices .\n->Moderate prices .\n[{'aspect': 'prices', 'opinion': 'Moderate', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i was very surprised at how fast it came .\n->i was very surprised at how fast it came .\n[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\ntext: Also , specify if you like your food spicy- its rather bland if you do n't .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlso , specify if you like your food spicy- its rather bland if you do n't .\n->", + "output": "{\"text\": \"Also , specify if you like your food spicy- its rather bland if you do n't .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bland', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she loves this laptop .\n->she loves this laptop .\n[{'aspect': 'laptop', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: other than that it ' s everything i imagined and more .\n->other than that it ' s everything i imagined and more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Our waiter was fine , the place looks nice in that not-trying-too-hard way , but at those prices , a little more should be expected of your food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOur waiter was fine , the place looks nice in that not-trying-too-hard way , but at those prices , a little more should be expected of your food .\n->", + "output": "{\"text\": \"Our waiter was fine , the place looks nice in that not-trying-too-hard way , but at those prices , a little more should be expected of your food .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'more should be expected', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: power turning on failed .\n->power turning on failed .\n[{'aspect': 'power', 'opinion': 'failed', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i love how quick this thing is .\n->i love how quick this thing is .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: I 've been to this restaurant more than a dozen times and when I 'm craving for Pho , Lemon grass chicken or Beef Cube on rice , this is the place to go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've been to this restaurant more than a dozen times and when I 'm craving for Pho , Lemon grass chicken or Beef Cube on rice , this is the place to go .\n->", + "output": "{\"text\": \"I 've been to this restaurant more than a dozen times and when I 'm craving for Pho , Lemon grass chicken or Beef Cube on rice , this is the place to go .\", \"labels\": \"[{'aspect': 'Pho', 'opinion': 'craving', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Lemon grass chicken', 'opinion': 'craving', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Beef Cube on rice', 'opinion': 'craving', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n->it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'insane', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n->not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'battery charger', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Oh yes , and if you are a fan of Indian oldies film stars , there are plenty of portraits of Indian actors and actresses in classic black white that adorn the walls , some of which , I would love to know where they obtained .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOh yes , and if you are a fan of Indian oldies film stars , there are plenty of portraits of Indian actors and actresses in classic black white that adorn the walls , some of which , I would love to know where they obtained .\n->", + "output": "{\"text\": \"Oh yes , and if you are a fan of Indian oldies film stars , there are plenty of portraits of Indian actors and actresses in classic black white that adorn the walls , some of which , I would love to know where they obtained .\", \"labels\": \"[{'aspect': 'portraits', 'opinion': 'plenty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: superb value for money and powerful performance from this quad core computer .\n->superb value for money and powerful performance from this quad core computer .\n[{'aspect': 'quad core computer', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'quad core computer', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: amazing my favorite ! ! !\n->amazing my favorite ! ! !\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The food is spectacular , from the appitizers to the main course , and then of course the desserts , ( WOW ) you 'll need no more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is spectacular , from the appitizers to the main course , and then of course the desserts , ( WOW ) you 'll need no more .\n->", + "output": "{\"text\": \"The food is spectacular , from the appitizers to the main course , and then of course the desserts , ( WOW ) you 'll need no more .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appitizers', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'main course', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall this is a very capable machine , better life is great as well .\n->overall this is a very capable machine , better life is great as well .\n[{'aspect': 'machine ,', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'better life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My fish was delicious in an incredible curry sauce .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy fish was delicious in an incredible curry sauce .\n->", + "output": "{\"text\": \"My fish was delicious in an incredible curry sauce .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'curry sauce', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n->i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n[{'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'inviting', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n->suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n[{'aspect': 'keyboard', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: If you are in search of the most authentic NYC deli experience look no further than the famous and historic Katz 's Deli down on the Lower East Side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you are in search of the most authentic NYC deli experience look no further than the famous and historic Katz 's Deli down on the Lower East Side .\n->", + "output": "{\"text\": \"If you are in search of the most authentic NYC deli experience look no further than the famous and historic Katz 's Deli down on the Lower East Side .\", \"labels\": \"[{'aspect': 'deli', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': \"chef ' s tasting menu\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n->we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n[{'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: Even upon delivery , their juicy pork buns are quite good . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven upon delivery , their juicy pork buns are quite good . .\n->", + "output": "{\"text\": \"Even upon delivery , their juicy pork buns are quite good . .\", \"labels\": \"[{'aspect': 'pork buns', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend visiting this restaurant and having dinner and drinks !\n->i highly recommend visiting this restaurant and having dinner and drinks !\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: internal graphics only ; not recommended for high intensity gaming or 3d modeling .\n->internal graphics only ; not recommended for high intensity gaming or 3d modeling .\n[{'aspect': 'internal graphics', 'opinion': 'not recommended', 'polarity': 'negative', 'category': 'GRAPHICS#DESIGN_FEATURES'}]\ntext: Average to good Thai food , but terrible delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAverage to good Thai food , but terrible delivery .\n->", + "output": "{\"text\": \"Average to good Thai food , but terrible delivery .\", \"labels\": \"[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , worst chromebook ever and i can ' t wait until it dies !\n->overall , worst chromebook ever and i can ' t wait until it dies !\n[{'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: cons : no caps lock key ( still haven ' t found it , help ! )\n->cons : no caps lock key ( still haven ' t found it , help ! )\n[{'aspect': 'caps lock key', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: Joe 's Pizza used to have the best slice until this pizzeria opened up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nJoe 's Pizza used to have the best slice until this pizzeria opened up .\n->", + "output": "{\"text\": \"Joe 's Pizza used to have the best slice until this pizzeria opened up .\", \"labels\": \"[{'aspect': 'slice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: And they provided a delicious dessert on the house !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd they provided a delicious dessert on the house !\n->", + "output": "{\"text\": \"And they provided a delicious dessert on the house !\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n->It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n[{'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the service is awful .\n->the service is awful .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n->", + "output": "{\"text\": \"They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\", \"labels\": \"[{'aspect': 'reservation', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food and the prices are very reasonable .\n->Great food and the prices are very reasonable .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was good too .\n->The food was good too .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food is amazing , rich pastas and fresh doughy pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is amazing , rich pastas and fresh doughy pizza .\n->", + "output": "{\"text\": \"The food is amazing , rich pastas and fresh doughy pizza .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastas', 'opinion': 'rich', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'fresh doughy', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard lighting is primitive and keeps shutting off .\n->keyboard lighting is primitive and keeps shutting off .\n[{'aspect': 'keyboard lighting', 'opinion': 'primitive', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: - windows 10 ( do i really need to list the drawbacks of 10 ?\n->- windows 10 ( do i really need to list the drawbacks of 10 ?\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: Food was very good , but not what I would consider out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood was very good , but not what I would consider out of this world .\n->", + "output": "{\"text\": \"Food was very good , but not what I would consider out of this world .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worth visiting the 1st ave spot because it is the original store .\n->worth visiting the 1st ave spot because it is the original store .\n[{'aspect': '1st ave spot', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Truly the mark of an attentive waiter .\n->Truly the mark of an attentive waiter .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Try the Pad Thai , it 's fabulous and their prices are so cheap !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTry the Pad Thai , it 's fabulous and their prices are so cheap !\n->", + "output": "{\"text\": \"Try the Pad Thai , it 's fabulous and their prices are so cheap !\", \"labels\": \"[{'aspect': 'Pad Thai', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n->The food is decent at best , and the ambience , well , it 's a matter of opinion , some may consider it to be a sweet thing , I thought it was just annoying .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: \u2022 super thin and light weight\n->\u2022 super thin and light weight\n[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Food is great and inexpensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is great and inexpensive .\n->", + "output": "{\"text\": \"Food is great and inexpensive .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the one star is for warranty support .\n->the one star is for warranty support .\n[{'aspect': 'warranty support', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n->the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Dessert - ca n't be missed , so save room ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDessert - ca n't be missed , so save room ! ! !\n->", + "output": "{\"text\": \"Dessert - ca n't be missed , so save room ! ! !\", \"labels\": \"[{'aspect': 'Dessert', 'opinion': \"ca n't be missed\", 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I also ordered the Change Mojito , which was out of this world .\n->I also ordered the Change Mojito , which was out of this world .\n[{'aspect': 'Change Mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: asus support is responsive but ineffective .\n->asus support is responsive but ineffective .\n[{'aspect': 'asus support', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus support', 'opinion': 'ineffective', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\ntext: Ingredients are organic which is a real plus for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIngredients are organic which is a real plus for me .\n->", + "output": "{\"text\": \"Ingredients are organic which is a real plus for me .\", \"labels\": \"[{'aspect': 'Ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will never buy an msi product again , and will tell every person i know to stay far away .\n->will never buy an msi product again , and will tell every person i know to stay far away .\n[{'aspect': 'msi product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: it was as they advertised .\n->it was as they advertised .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: It was so bad I actually refused to pay for my food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt was so bad I actually refused to pay for my food .\n->", + "output": "{\"text\": \"It was so bad I actually refused to pay for my food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I can not imagine better Indian food in all of the city .\n->I can not imagine better Indian food in all of the city .\n[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: however i ' m happy to report that the keyboard is great and i ' ve already gotten use to it .\n->however i ' m happy to report that the keyboard is great and i ' ve already gotten use to it .\n[{'aspect': 'keyboard', 'opinion': 'happy', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: I recommend this spot to anyone who enjoys fine cuisine at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI recommend this spot to anyone who enjoys fine cuisine at reasonable prices .\n->", + "output": "{\"text\": \"I recommend this spot to anyone who enjoys fine cuisine at reasonable prices .\", \"labels\": \"[{'aspect': 'cuisine', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n->this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Two words : Free wine .\n->Two words : Free wine .\n[{'aspect': 'wine', 'opinion': 'Free', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff was very attentive , the ambience lovely , and the food superb .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff was very attentive , the ambience lovely , and the food superb .\n->", + "output": "{\"text\": \"The staff was very attentive , the ambience lovely , and the food superb .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: whenever you need a sushi fix , mizu will be there with quality fish and great service .\n->whenever you need a sushi fix , mizu will be there with quality fish and great service .\n[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the volume and backlit - keyboard brightness controls also do not work properly .\n->the volume and backlit - keyboard brightness controls also do not work properly .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: I love when restaurants think using fancy expensive ingrediants makes the food fine cuisine , even with no idea how to use them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI love when restaurants think using fancy expensive ingrediants makes the food fine cuisine , even with no idea how to use them .\n->", + "output": "{\"text\": \"I love when restaurants think using fancy expensive ingrediants makes the food fine cuisine , even with no idea how to use them .\", \"labels\": \"[{'aspect': 'ingrediants', 'opinion': 'expensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n->the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'plus', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i have to say , this is a very nice product .\n->i have to say , this is a very nice product .\n[{'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: THe Pizza and wine were excellent -the service too -- but what really MADE this place was the backyard dining area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTHe Pizza and wine were excellent -the service too -- but what really MADE this place was the backyard dining area .\n->", + "output": "{\"text\": \"THe Pizza and wine were excellent -the service too -- but what really MADE this place was the backyard dining area .\", \"labels\": \"[{'aspect': 'Pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n->To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: highly recommended to all !\n->highly recommended to all !\n[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The service was superb , they treat you like family .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was superb , they treat you like family .\n->", + "output": "{\"text\": \"The service was superb , they treat you like family .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is one of the best comfort food places in the city .\n->this is one of the best comfort food places in the city .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'comfort', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: A cool bar with great food , and tons of excellent beer .\n->A cool bar with great food , and tons of excellent beer .\n[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We all had the tasting menu and unlike some of the other reviews , I felt there was more than enough food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe all had the tasting menu and unlike some of the other reviews , I felt there was more than enough food .\n->", + "output": "{\"text\": \"We all had the tasting menu and unlike some of the other reviews , I felt there was more than enough food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the spicy shrimp appetizer ( again , not the greatest value in the world but worth the price ) and the lamb vindaloo is great .\n->try the spicy shrimp appetizer ( again , not the greatest value in the world but worth the price ) and the lamb vindaloo is great .\n[{'aspect': 'shrimp appetizer', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shrimp appetizer', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shrimp appetizer', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb vindaloo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: how can a brand new computer not charge properly ?\n->how can a brand new computer not charge properly ?\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The menu may be small , but everything on it is delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe menu may be small , but everything on it is delicious .\n->", + "output": "{\"text\": \"The menu may be small , but everything on it is delicious .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Simple comfort food and what hot and large portions .\n->Simple comfort food and what hot and large portions .\n[{'aspect': 'comfort food', 'opinion': 'Simple comfort', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it keeps disconnecting .\n->it keeps disconnecting .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n->", + "output": "{\"text\": \"They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\", \"labels\": \"[{'aspect': 'RICE', 'opinion': 'BURNT', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n->Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n[{'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: That is a problem since we paid about 20 bucks a dish , and had to order 5 dishes to get a decent taste .\n->That is a problem since we paid about 20 bucks a dish , and had to order 5 dishes to get a decent taste .\n[{'aspect': 'taste', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Warm , comfortable surroundings , nice appointments ( witness the etched glass and brickwork separating the dining rooms ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWarm , comfortable surroundings , nice appointments ( witness the etched glass and brickwork separating the dining rooms ) .\n->", + "output": "{\"text\": \"Warm , comfortable surroundings , nice appointments ( witness the etched glass and brickwork separating the dining rooms ) .\", \"labels\": \"[{'aspect': 'surroundings', 'opinion': 'Warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'surroundings', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining rooms', 'opinion': 'nice', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the second the screen did not rotate .\n->the second the screen did not rotate .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: this bios is horrible .\n->this bios is horrible .\n[{'aspect': 'bios', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: The well mannered , pleasant staff that Tony has in his employ .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe well mannered , pleasant staff that Tony has in his employ .\n->", + "output": "{\"text\": \"The well mannered , pleasant staff that Tony has in his employ .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we had pam ' s special fried fish and it was amazing .\n->we had pam ' s special fried fish and it was amazing .\n[{'aspect': \"pam ' s special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: We parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas .\n->We parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizzas', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\ntext: All I can say is $ 2 pints during happy hour and the some of the cheapest oysters you 'll find in the city , though the quality is some of the best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll I can say is $ 2 pints during happy hour and the some of the cheapest oysters you 'll find in the city , though the quality is some of the best .\n->", + "output": "{\"text\": \"All I can say is $ 2 pints during happy hour and the some of the cheapest oysters you 'll find in the city , though the quality is some of the best .\", \"labels\": \"[{'aspect': 'oysters', 'opinion': 'cheapest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n->- backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n[{'aspect': 'backlit keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit keyboard', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: also , because it is so thin , it gets cold very quickly and its not that filling .\n->also , because it is so thin , it gets cold very quickly and its not that filling .\n[{'aspect': 'NULL', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: I would never wait for a table to eat , it just is not THAT great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would never wait for a table to eat , it just is not THAT great .\n->", + "output": "{\"text\": \"I would never wait for a table to eat , it just is not THAT great .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'never wait', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so before you get angry do your homework on why the laptop may be acting strange .\n->so before you get angry do your homework on why the laptop may be acting strange .\n[{'aspect': 'laptop', 'opinion': 'angry', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: LOVE the atmosphere - felt like I was in Paris .\n->LOVE the atmosphere - felt like I was in Paris .\n[{'aspect': 'atmosphere', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We concluded with tiramisu chocolate cake , both were delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe concluded with tiramisu chocolate cake , both were delicious .\n->", + "output": "{\"text\": \"We concluded with tiramisu chocolate cake , both were delicious .\", \"labels\": \"[{'aspect': 'tiramisu chocolate cake', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Have frequented 'ino for several years and the food remains excellent .\n->Have frequented 'ino for several years and the food remains excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what this tells me is that the hdmi port on my chromebook is defective .\n->what this tells me is that the hdmi port on my chromebook is defective .\n[{'aspect': 'hdmi port on my chromebook', 'opinion': 'defective', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: The counter service is bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe counter service is bad .\n->", + "output": "{\"text\": \"The counter service is bad .\", \"labels\": \"[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and even with it ' s pub atmosphere they were great to my kids too !\n->and even with it ' s pub atmosphere they were great to my kids too !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great indian food\n->great indian food\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Service here was great , food was fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService here was great , food was fantastic .\n->", + "output": "{\"text\": \"Service here was great , food was fantastic .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen looks fantastic and movies look great .\n->the screen looks fantastic and movies look great .\n[{'aspect': 'screen', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: The sauce is delicious and the crust is perfect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sauce is delicious and the crust is perfect .\n->", + "output": "{\"text\": \"The sauce is delicious and the crust is perfect .\", \"labels\": \"[{'aspect': 'sauce', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fast boot up ( 3 seconds )\n->- fast boot up ( 3 seconds )\n[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The only problem is that the manager is a complete incompetent .\n->The only problem is that the manager is a complete incompetent .\n[{'aspect': 'manager', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: You will pay a lot for the decore , but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYou will pay a lot for the decore , but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY .\n->", + "output": "{\"text\": \"You will pay a lot for the decore , but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY .\", \"labels\": \"[{'aspect': 'decore', 'opinion': 'pay a lot', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'no better or worse', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 15 ` ` screen , full sized keyboard and speed fast enough for some low quality games such as lol .\n->15 ` ` screen , full sized keyboard and speed fast enough for some low quality games such as lol .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'speed fast', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: short charging cable .\n->short charging cable .\n[{'aspect': 'charging cable', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\ntext: They have some great entrees here as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey have some great entrees here as well .\n->", + "output": "{\"text\": \"They have some great entrees here as well .\", \"labels\": \"[{'aspect': 'entrees', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the veal and the mushrooms were cooked perfectly .\n->the veal and the mushrooms were cooked perfectly .\n[{'aspect': 'veal', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mushrooms', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n->the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot sauce', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The service is not consistently excellent -- just decent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is not consistently excellent -- just decent .\n->", + "output": "{\"text\": \"The service is not consistently excellent -- just decent .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'not consistently excellent', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its fast , light weight , quiet , and looks easy to add additional ram and hdd ' s .\n->its fast , light weight , quiet , and looks easy to add additional ram and hdd ' s .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: * * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n->* * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n[{'aspect': 'laptop', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n->", + "output": "{\"text\": \"The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: toons has recently been redone , so it ' s now a very attractive space .\n->toons has recently been redone , so it ' s now a very attractive space .\n[{'aspect': 'toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The lava cake dessert was incredible and I recommend it .\n->The lava cake dessert was incredible and I recommend it .\n[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I had the mango chicken and i ca n't go on to tell you how delicious that was and the presentation was beautiful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had the mango chicken and i ca n't go on to tell you how delicious that was and the presentation was beautiful .\n->", + "output": "{\"text\": \"I had the mango chicken and i ca n't go on to tell you how delicious that was and the presentation was beautiful .\", \"labels\": \"[{'aspect': 'mango chicken', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'presentation', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the lobster sandwich is good and the spaghetti with scallops and shrimp is great .\n->the lobster sandwich is good and the spaghetti with scallops and shrimp is great .\n[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spaghetti with scallops and shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: do not buy this .\n->do not buy this .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: Food and service was okay .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood and service was okay .\n->", + "output": "{\"text\": \"Food and service was okay .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: which of course is not real kobe but wagyu beef .\n->which of course is not real kobe but wagyu beef .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: fast , thin , great battery life .\n->fast , thin , great battery life .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: The best pad thai i 've ever had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe best pad thai i 've ever had .\n->", + "output": "{\"text\": \"The best pad thai i 've ever had .\", \"labels\": \"[{'aspect': 'pad thai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n->Although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'water', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: * * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n->* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: Great pizza and fantastic service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat pizza and fantastic service .\n->", + "output": "{\"text\": \"Great pizza and fantastic service .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery life is terrible .\n->the battery life is terrible .\n[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: this laptop was delivered with the screen broken , it was the christmas gift and when we removed the gift paper se found a laptop thant not worked , that ' s unfair and that should not be done yo any people on christmas night\n->this laptop was delivered with the screen broken , it was the christmas gift and when we removed the gift paper se found a laptop thant not worked , that ' s unfair and that should not be done yo any people on christmas night\n[{'aspect': 'NULL', 'opinion': 'unfair', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: While this is a pretty place in that overly cute French way , the food was insultingly horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile this is a pretty place in that overly cute French way , the food was insultingly horrible .\n->", + "output": "{\"text\": \"While this is a pretty place in that overly cute French way , the food was insultingly horrible .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'insultingly horrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Truly the mark of an attentive waiter .\n->Truly the mark of an attentive waiter .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: My boyfriend had Prime Rib it was good .\n->My boyfriend had Prime Rib it was good .\n[{'aspect': 'Prime Rib', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I went there for lunch and it was not as good as I expected from the reviews I read .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI went there for lunch and it was not as good as I expected from the reviews I read .\n->", + "output": "{\"text\": \"I went there for lunch and it was not as good as I expected from the reviews I read .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'not as good as I expected', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recommend it , definitely\n->i recommend it , definitely\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: if it seemed possible to do so while there i would have fought my bill since my dinner portion of my meal was inedible !\n->if it seemed possible to do so while there i would have fought my bill since my dinner portion of my meal was inedible !\n[{'aspect': 'meal', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n->", + "output": "{\"text\": \"The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': \"is n't great\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'not as good', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not only is the cuisine the best around , the service has always been attentive and charming .\n->not only is the cuisine the best around , the service has always been attentive and charming .\n[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i love this laptop so far .\n->i love this laptop so far .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Their coffee is quite good too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir coffee is quite good too !\n->", + "output": "{\"text\": \"Their coffee is quite good too !\", \"labels\": \"[{'aspect': 'coffee', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keys type nicely .\n->keys type nicely .\n[{'aspect': 'keys', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n->I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great wine list ( italian ) , good food , service was INITIALLY fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat wine list ( italian ) , good food , service was INITIALLY fine .\n->", + "output": "{\"text\": \"great wine list ( italian ) , good food , service was INITIALLY fine .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touch screen is nice , and i like to use it for free handing things when i need to .\n->the touch screen is nice , and i like to use it for free handing things when i need to .\n[{'aspect': 'touch screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\n->Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\n[{'aspect': 'burgers', 'opinion': 'fell apart', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Despite the fact that the space is large , they 've overcrowded the floor with tables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDespite the fact that the space is large , they 've overcrowded the floor with tables .\n->", + "output": "{\"text\": \"Despite the fact that the space is large , they 've overcrowded the floor with tables .\", \"labels\": \"[{'aspect': 'space', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'overcrowded', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n->the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n[{'aspect': 'ram', 'opinion': 'expandable', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: i wouldn ' t want to do any extensive typing on it .\n->i wouldn ' t want to do any extensive typing on it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: They are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n->", + "output": "{\"text\": \"They are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'hostile', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as for actual device it is completely gorgeous and ( now ) works flawlessly .\n->as for actual device it is completely gorgeous and ( now ) works flawlessly .\n[{'aspect': 'device', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Every course was better than the next .\n->Every course was better than the next .\n[{'aspect': 'course', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Normally that would be improper , however they were all delicious and my host did not complain .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nNormally that would be improper , however they were all delicious and my host did not complain .\n->", + "output": "{\"text\": \"Normally that would be improper , however they were all delicious and my host did not complain .\", \"labels\": \"[{'aspect': 'host', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: La Rosa waltzes in , and I think they are doing it the best .\n->La Rosa waltzes in , and I think they are doing it the best .\n[{'aspect': 'La Rosa', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The only fallback on this restaurant is the prices .\n->The only fallback on this restaurant is the prices .\n[{'aspect': 'restaurant', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Taxan delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTaxan delicious !\n->", + "output": "{\"text\": \"Taxan delicious !\", \"labels\": \"[{'aspect': 'Taxan', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They have it all -- great price , food , and service .\n->They have it all -- great price , food , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was worried about the battery life because of the reviews .\n->i was worried about the battery life because of the reviews .\n[{'aspect': 'battery life', 'opinion': 'worried', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: Unlike other places in NYC where the sandwiches you want only come as a triple-decker , here you can get what you want in a reasonably-sized portion ( and price ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nUnlike other places in NYC where the sandwiches you want only come as a triple-decker , here you can get what you want in a reasonably-sized portion ( and price ) .\n->", + "output": "{\"text\": \"Unlike other places in NYC where the sandwiches you want only come as a triple-decker , here you can get what you want in a reasonably-sized portion ( and price ) .\", \"labels\": \"[{'aspect': 'portion', 'opinion': 'reasonably-sized', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very cozy and warm inside . . . . .\n->very cozy and warm inside . . . . .\n[{'aspect': 'NULL', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: - backlit keyboard rocks\n->- backlit keyboard rocks\n[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: Restaurant snobs need not bother , this is a small , neighborhood kind of place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nRestaurant snobs need not bother , this is a small , neighborhood kind of place .\n->", + "output": "{\"text\": \"Restaurant snobs need not bother , this is a small , neighborhood kind of place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: im actually wondering if there is an issue with the speakers , it ' s so bad .\n->im actually wondering if there is an issue with the speakers , it ' s so bad .\n[{'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: The service is awful .\n->The service is awful .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\ntext: They could n't even make a salad that was appealing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey could n't even make a salad that was appealing .\n->", + "output": "{\"text\": \"They could n't even make a salad that was appealing .\", \"labels\": \"[{'aspect': 'salad', 'opinion': 'appealing', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Highly recommend this as great value for excellent sushi and service .\n->Highly recommend this as great value for excellent sushi and service .\n[{'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i sit with it in my lap all day long and it never gets hot .\n->i sit with it in my lap all day long and it never gets hot .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: Of course the reason its so packed is because the food is so delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOf course the reason its so packed is because the food is so delicious !\n->", + "output": "{\"text\": \"Of course the reason its so packed is because the food is so delicious !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was not fresh , the sauces were bland and very oily .\n->The food was not fresh , the sauces were bland and very oily .\n[{'aspect': 'food', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n->Raga stands out with an interesting fusion of French and Indian cooking .\n[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Staffs are not that friendly , but the taste covers all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nStaffs are not that friendly , but the taste covers all .\n->", + "output": "{\"text\": \"Staffs are not that friendly , but the taste covers all .\", \"labels\": \"[{'aspect': 'Staffs', 'opinion': 'not that friendly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'taste', 'opinion': 'covers all', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + nice , large screen\n->+ nice , large screen\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The waiter was attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waiter was attentive .\n->", + "output": "{\"text\": \"The waiter was attentive .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: authentic pakistani food .\n->authentic pakistani food .\n[{'aspect': 'pakistani food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i work with an it company and we ' re testing an all android environment and it ' s working out pretty well so far .\n->i work with an it company and we ' re testing an all android environment and it ' s working out pretty well so far .\n[{'aspect': 'android environment', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Their Margarita is best I 've had since I 've returned from Naples !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir Margarita is best I 've had since I 've returned from Naples !\n->", + "output": "{\"text\": \"Their Margarita is best I 've had since I 've returned from Naples !\", \"labels\": \"[{'aspect': 'Margarita', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: twice in 10 days i had an issue , pointer , where they said turn it over and put a pin in it .\n->twice in 10 days i had an issue , pointer , where they said turn it over and put a pin in it .\n[{'aspect': 'pointer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'Out_Of_Scope#OPERATION_PERFORMANCE'}]\nExample:\ntext: yes , this chromebook comes with the android app store pre - installed .\n->yes , this chromebook comes with the android app store pre - installed .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n->", + "output": "{\"text\": \"Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\", \"labels\": \"[{'aspect': 'sake list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service -- friendly and attentive .\n->Service -- friendly and attentive .\n[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: definitely recommend this chromebook , it ' s a beautiful machine .\n->definitely recommend this chromebook , it ' s a beautiful machine .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n->", + "output": "{\"text\": \"The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\", \"labels\": \"[{'aspect': 'tables', 'opinion': 'crammed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'too close', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stick with the chicken , beef , and lamb dishes .\n->stick with the chicken , beef , and lamb dishes .\n[{'aspect': 'chicken', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb dishes', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: On a hot day it was fabulous to stop in and enjoy lunch .\n->On a hot day it was fabulous to stop in and enjoy lunch .\n[{'aspect': 'lunch', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was actually awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was actually awful .\n->", + "output": "{\"text\": \"The food was actually awful .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , service was as plain as sesame crusted Salmon I had .\n->However , service was as plain as sesame crusted Salmon I had .\n[{'aspect': 'service', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'sesame crusted Salmon', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i can ' t get it to work at all .\n->i can ' t get it to work at all .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: They are served with a free appetizer and the portions are perfect for lunch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey are served with a free appetizer and the portions are perfect for lunch .\n->", + "output": "{\"text\": \"They are served with a free appetizer and the portions are perfect for lunch .\", \"labels\": \"[{'aspect': 'appetizer', 'opinion': 'free', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s all about the food ! !\n->it ' s all about the food ! !\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i ' m pretty sure i ordered the biggest size , and i got the smaller one , but whatever it shipped fast and it works great .\n->i ' m pretty sure i ordered the biggest size , and i got the smaller one , but whatever it shipped fast and it works great .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: There is actually space to breathe and the decor sets the tone for an intimate dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere is actually space to breathe and the decor sets the tone for an intimate dinner .\n->", + "output": "{\"text\": \"There is actually space to breathe and the decor sets the tone for an intimate dinner .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'intimate', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: second where the heck is my other 8 gigs of ram ?\n->second where the heck is my other 8 gigs of ram ?\n[{'aspect': '8 gigs of ram', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: customer service is difficult .\n->customer service is difficult .\n[{'aspect': 'customer service', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n->", + "output": "{\"text\": \"We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this has worked great to overcome that problem .\n->this has worked great to overcome that problem .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Lahore is a great place to duck into late-night when you need some really tasty food on the cheap -- you 'll likely have trouble finishing the amount of food you get for FOUR DOLLARS .\n->Lahore is a great place to duck into late-night when you need some really tasty food on the cheap -- you 'll likely have trouble finishing the amount of food you get for FOUR DOLLARS .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My husband and I both ordered the Steak , medium .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy husband and I both ordered the Steak , medium .\n->", + "output": "{\"text\": \"My husband and I both ordered the Steak , medium .\", \"labels\": \"[{'aspect': 'Steak', 'opinion': 'medium', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n->I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: with the power of the internet and all the online productivity products the simplicity of this is fantastic .\n->with the power of the internet and all the online productivity products the simplicity of this is fantastic .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: This place has realy fresh sushi and a nice large menu of Japanese classic cuisine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place has realy fresh sushi and a nice large menu of Japanese classic cuisine .\n->", + "output": "{\"text\": \"This place has realy fresh sushi and a nice large menu of Japanese classic cuisine .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: october 12 , 2017 - - started having trouble maintaining connection to wifi ( spectrum service ) , but usually after several loops re - entering password , connection would be re - established .\n->october 12 , 2017 - - started having trouble maintaining connection to wifi ( spectrum service ) , but usually after several loops re - entering password , connection would be re - established .\n[{'aspect': 'wifi', 'opinion': 'trouble', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\nExample:\ntext: ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n->ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: The sushi is average and the prices are anything but .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sushi is average and the prices are anything but .\n->", + "output": "{\"text\": \"The sushi is average and the prices are anything but .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hard drive is definitely slow .\n->the hard drive is definitely slow .\n[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n->While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n->", + "output": "{\"text\": \"My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\", \"labels\": \"[{'aspect': 'spinach', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shanghai low mein', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n->certain apps ( especially flash based apps ) will get the machine very hot .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is a light - use business laptop that we ' ve had for a month .\n->this is a light - use business laptop that we ' ve had for a month .\n[{'aspect': 'laptop', 'opinion': 'light - use', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: Turned out there was full service upstairs and sat down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTurned out there was full service upstairs and sat down .\n->", + "output": "{\"text\": \"Turned out there was full service upstairs and sat down .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: up until this point , asus chromebooks have been my favorite .\n->up until this point , asus chromebooks have been my favorite .\n[{'aspect': 'asus chromebooks', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - fingerprint reader is working well\n->- fingerprint reader is working well\n[{'aspect': 'fingerprint reader', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: If you have a dumpling fetish i suggest you try some here !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you have a dumpling fetish i suggest you try some here !\n->", + "output": "{\"text\": \"If you have a dumpling fetish i suggest you try some here !\", \"labels\": \"[{'aspect': 'dumpling', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was able to download all of my games in a quick amount of time too .\n->i was able to download all of my games in a quick amount of time too .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n->bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n[{'aspect': 'specific unit', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'specific unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: The food is consistently wonderful - I 've been coming here for years , and the owner has always been accomodating and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is consistently wonderful - I 've been coming here for years , and the owner has always been accomodating and friendly .\n->", + "output": "{\"text\": \"The food is consistently wonderful - I 've been coming here for years , and the owner has always been accomodating and friendly .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: $ 20 for all you can eat sushi can not be beaten .\n->$ 20 for all you can eat sushi can not be beaten .\n[{'aspect': 'all you can eat sushi', 'opinion': 'beaten', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: The waiters and owners were nonchalant about this and promised to call the exterminator but were n't as dismayed or apologetic as I would have expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe waiters and owners were nonchalant about this and promised to call the exterminator but were n't as dismayed or apologetic as I would have expected .\n->", + "output": "{\"text\": \"The waiters and owners were nonchalant about this and promised to call the exterminator but were n't as dismayed or apologetic as I would have expected .\", \"labels\": \"[{'aspect': 'waiters', 'opinion': 'nonchalant', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'owners', 'opinion': 'nonchalant', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n->His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n[{'aspect': 'hostess', 'opinion': 'delightfully warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'gracious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'comforting', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Try the crunchy tuna , it is to die for .\n->Try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\ntext: And the food was fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd the food was fantastic .\n->", + "output": "{\"text\": \"And the food was fantastic .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great laptop .\n->this is a great laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i can eat here every day of the week really lol love this place . . . )\n->i can eat here every day of the week really lol love this place . . . )\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Aside from the Sea Urchin , the chef recommended an assortment of fish including Fatty Yellow Tail , Boton Shrimp , Blue Fin Torro ( Fatty Tuna ) , Sea Eel , etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAside from the Sea Urchin , the chef recommended an assortment of fish including Fatty Yellow Tail , Boton Shrimp , Blue Fin Torro ( Fatty Tuna ) , Sea Eel , etc .\n->", + "output": "{\"text\": \"Aside from the Sea Urchin , the chef recommended an assortment of fish including Fatty Yellow Tail , Boton Shrimp , Blue Fin Torro ( Fatty Tuna ) , Sea Eel , etc .\", \"labels\": \"[{'aspect': 'assortment of fish', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Fatty Yellow Tail', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Boton Shrimp', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Sea Eel', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Sea Urchin', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Blue Fin Torro ( Fatty Tuna )', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: really happy with this laptop !\n->really happy with this laptop !\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I found the food , service and value exceptional everytime I have been there .\n->I found the food , service and value exceptional everytime I have been there .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: They 're also friendlier here , especially the owner , Kenny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey 're also friendlier here , especially the owner , Kenny .\n->", + "output": "{\"text\": \"They 're also friendlier here , especially the owner , Kenny .\", \"labels\": \"[{'aspect': 'owner', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their Margarita is best I 've had since I 've returned from Naples !\n->Their Margarita is best I 've had since I 've returned from Naples !\n[{'aspect': 'Margarita', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: whenever you need a sushi fix , mizu will be there with quality fish and great service .\n->whenever you need a sushi fix , mizu will be there with quality fish and great service .\n[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n->", + "output": "{\"text\": \"While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\", \"labels\": \"[{'aspect': 'room', 'opinion': 'not particularly comfortable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Yellowtail was particularly good as well .\n->The Yellowtail was particularly good as well .\n[{'aspect': 'Yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it just freezes up on you .\n->it just freezes up on you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The ambiance is minimal the food is not phenomenal , but some dishes are quite good , such as the eggplant parmesan , veal in carozza chicken saltimbocca .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ambiance is minimal the food is not phenomenal , but some dishes are quite good , such as the eggplant parmesan , veal in carozza chicken saltimbocca .\n->", + "output": "{\"text\": \"The ambiance is minimal the food is not phenomenal , but some dishes are quite good , such as the eggplant parmesan , veal in carozza chicken saltimbocca .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'minimal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not phenomenal', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'eggplant parmesan', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'veal in carozza chicken saltimbocca', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for my daughter for school and she loves it .\n->i bought this for my daughter for school and she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Good bagels and good cream cheese .\n->Good bagels and good cream cheese .\n[{'aspect': 'bagels', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheese', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The bar has various selections and the mixed drink special is a catcher ! 2 for 1 's .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bar has various selections and the mixed drink special is a catcher ! 2 for 1 's .\n->", + "output": "{\"text\": \"The bar has various selections and the mixed drink special is a catcher ! 2 for 1 's .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'various', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mixed drink special', 'opinion': 'catcher', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: tech support is useless .\n->tech support is useless .\n[{'aspect': 'tech support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDuring the course of the past 3 months , the chef and staff changed and it was not for the better .\n->", + "output": "{\"text\": \"During the course of the past 3 months , the chef and staff changed and it was not for the better .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i thought i had died and gone to heaven .\n->i thought i had died and gone to heaven .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: I like the ambience , it 's very dark and original .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI like the ambience , it 's very dark and original .\n->", + "output": "{\"text\": \"I like the ambience , it 's very dark and original .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it integrates perfectly with my google account !\n->it integrates perfectly with my google account !\n[{'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: If you want good authentic Thai this place is not the place to go .\n->If you want good authentic Thai this place is not the place to go .\n[{'aspect': 'Thai', 'opinion': 'good authentic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Overall , not worth the money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOverall , not worth the money .\n->", + "output": "{\"text\": \"Overall , not worth the money .\", \"labels\": \"[{'aspect': 'money', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first of all , this is a physically beautiful machine .\n->first of all , this is a physically beautiful machine .\n[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n->However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n[{'aspect': 'ambiance', 'opinion': 'drawn', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This place is not worth the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is not worth the prices .\n->", + "output": "{\"text\": \"This place is not worth the prices .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my screen stayed black more than it was on .\n->my screen stayed black more than it was on .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The octopus eaters were floored by the Octopus salad .\n->The octopus eaters were floored by the Octopus salad .\n[{'aspect': 'Octopus salad', 'opinion': 'floored', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Pick a bagel has the best bagels in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nPick a bagel has the best bagels in the city .\n->", + "output": "{\"text\": \"Pick a bagel has the best bagels in the city .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n->the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n[{'aspect': 'number pad', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: edit : just shy of owning 2 for 9 months now and both are experiencing touch screen failure problems .\n->edit : just shy of owning 2 for 9 months now and both are experiencing touch screen failure problems .\n[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nYes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n->", + "output": "{\"text\": \"Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\", \"labels\": \"[{'aspect': 'waiting area', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seat', 'opinion': 'all taken', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it boots in seconds , and i get ~ 10 hours out of the battery .\n->it boots in seconds , and i get ~ 10 hours out of the battery .\n[{'aspect': 'boots', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: - timeout on keyboard backlight not adjustable .\n->- timeout on keyboard backlight not adjustable .\n[{'aspect': 'keyboard', 'opinion': 'not adjustable', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\ntext: The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n->", + "output": "{\"text\": \"The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'barely', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spice', 'opinion': 'took away', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n->Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n[{'aspect': 'waiters', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'busy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i really loved the different and inovated touch that ' s the cheff gives to the food .\n->i really loved the different and inovated touch that ' s the cheff gives to the food .\n[{'aspect': 'cheff', 'opinion': 'loved', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheff', 'opinion': 'inovated', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The fried dumplings are GREAT !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe fried dumplings are GREAT !\n->", + "output": "{\"text\": \"The fried dumplings are GREAT !\", \"labels\": \"[{'aspect': 'fried dumplings', 'opinion': 'GREAT', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine . . . but not the grunt that we received from the al di la staff .\n->mistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine . . . but not the grunt that we received from the al di la staff .\n[{'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Jimmy 's is hands down the hottest night spot in the Bronx .\n->Jimmy 's is hands down the hottest night spot in the Bronx .\n[{'aspect': 'spot', 'opinion': 'hottest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We figured we never had Argentinian Pizza before so we grabbed our lunch there , sharing a large Pelligrino , a pizza of two of their specials , one was goat cheese the other blue cheese , and both were excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe figured we never had Argentinian Pizza before so we grabbed our lunch there , sharing a large Pelligrino , a pizza of two of their specials , one was goat cheese the other blue cheese , and both were excellent .\n->", + "output": "{\"text\": \"We figured we never had Argentinian Pizza before so we grabbed our lunch there , sharing a large Pelligrino , a pizza of two of their specials , one was goat cheese the other blue cheese , and both were excellent .\", \"labels\": \"[{'aspect': 'Pelligrino', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'goat cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'blue cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great little computer .\n->great little computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n->your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n[{'aspect': 'retina screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: The ' kamasutra ' and ' bombay cosmopolitan ' are excellent and will have you tipsy in no time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ' kamasutra ' and ' bombay cosmopolitan ' are excellent and will have you tipsy in no time .\n->", + "output": "{\"text\": \"The ' kamasutra ' and ' bombay cosmopolitan ' are excellent and will have you tipsy in no time .\", \"labels\": \"[{'aspect': 'kamasutra', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bombay cosmopolitan', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food , great lay out and awesome service .\n->Great food , great lay out and awesome service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lay out', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n->I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n[{'aspect': 'drink', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: This is the perfect date spot for Williamsburg couples .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the perfect date spot for Williamsburg couples .\n->", + "output": "{\"text\": \"This is the perfect date spot for Williamsburg couples .\", \"labels\": \"[{'aspect': 'date spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n->we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: stepping into casa la femme last night was a true experience unlike any other in new york !\n->stepping into casa la femme last night was a true experience unlike any other in new york !\n[{'aspect': 'casa la femme', 'opinion': 'true', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: The dim sum here is only so-so .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe dim sum here is only so-so .\n->", + "output": "{\"text\": \"The dim sum here is only so-so .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s possible i got bad hardware by chance , but all of the issues being directly traceable to drivers suggests the issues were all driver - related .\n->it ' s possible i got bad hardware by chance , but all of the issues being directly traceable to drivers suggests the issues were all driver - related .\n[{'aspect': 'hardware', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}, {'aspect': 'drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n->i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Service was slow , but the people were friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was slow , but the people were friendly .\n->", + "output": "{\"text\": \"Service was slow , but the people were friendly .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food 's dazzling flavors overwhelm the palate , truly embracing the beauty of authentic Thai cuisine .\n->The food 's dazzling flavors overwhelm the palate , truly embracing the beauty of authentic Thai cuisine .\n[{'aspect': 'food', 'opinion': 'overwhelm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai cuisine', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavors', 'opinion': 'overwhelm', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The hostess and the waitress were incredibly rude and did everything they could to rush us out .\n->The hostess and the waitress were incredibly rude and did everything they could to rush us out .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The steak was very fatty and the sauce was overpowering and not very tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe steak was very fatty and the sauce was overpowering and not very tasty .\n->", + "output": "{\"text\": \"The steak was very fatty and the sauce was overpowering and not very tasty .\", \"labels\": \"[{'aspect': 'steak', 'opinion': 'fatty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'overpowering', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not a small feat for good french food in the area .\n->Not a small feat for good french food in the area .\n[{'aspect': 'french food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Pizza is terrific , as is homemade pasta .\n->Pizza is terrific , as is homemade pasta .\n[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n->", + "output": "{\"text\": \"The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n->The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'long', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dim sum atmosphere', 'opinion': 'typical raucous', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n->while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n[{'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The food is alright - some stuff is good - some is not ( like the steak dish which tends to be dry ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is alright - some stuff is good - some is not ( like the steak dish which tends to be dry ) .\n->", + "output": "{\"text\": \"The food is alright - some stuff is good - some is not ( like the steak dish which tends to be dry ) .\", \"labels\": \"[{'aspect': 'steak dish', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touch screen broke four months after i purchased it .\n->the touch screen broke four months after i purchased it .\n[{'aspect': 'touch screen', 'opinion': 'broke', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: The staff are attentive , and have smiles on their faces .\n->The staff are attentive , and have smiles on their faces .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: They have a very diverse menu so its something for everybody .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey have a very diverse menu so its something for everybody .\n->", + "output": "{\"text\": \"They have a very diverse menu so its something for everybody .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not only is the touchpad not great in use but it also feels poorly made .\n->not only is the touchpad not great in use but it also feels poorly made .\n[{'aspect': 'touchpad', 'opinion': 'not great', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: Service is great , takeout is good too .\n->Service is great , takeout is good too .\n[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'takeout', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The staff was too busy ordering sushi for dinner and then laying it out to eat on the bar to even bring me my check .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff was too busy ordering sushi for dinner and then laying it out to eat on the bar to even bring me my check .\n->", + "output": "{\"text\": \"The staff was too busy ordering sushi for dinner and then laying it out to eat on the bar to even bring me my check .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price , you can not eat this well in manhattan .\n->for the price , you can not eat this well in manhattan .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Okay service .\n->Okay service .\n[{'aspect': 'service', 'opinion': 'Okay', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The bagel was huge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe bagel was huge .\n->", + "output": "{\"text\": \"The bagel was huge .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Light , refreshing summer rolls ( not fried ) remind me of Vietnamese places in Paris .\n->Light , refreshing summer rolls ( not fried ) remind me of Vietnamese places in Paris .\n[{'aspect': 'summer rolls', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i tired every remedy found online , and even got in touch with asus tech support who just said ` ` send it back ` ` .\n->i tired every remedy found online , and even got in touch with asus tech support who just said ` ` send it back ` ` .\n[{'aspect': 'asus tech support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->", + "output": "{\"text\": \"The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\", \"labels\": \"[{'aspect': 'bathroom', 'opinion': 'needs to be cleaned', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n->The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'semi-private boths', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'semi-private boths', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but as it stands still makes a great mobile device with excellent battery life to boot .\n->but as it stands still makes a great mobile device with excellent battery life to boot .\n[{'aspect': 'mobile device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: The service was the only thing good about this restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was the only thing good about this restaurant .\n->", + "output": "{\"text\": \"The service was the only thing good about this restaurant .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff is incredibly helpful and attentive .\n->the staff is incredibly helpful and attentive .\n[{'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n->downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n[{'aspect': 'appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Service -- friendly and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService -- friendly and attentive .\n->", + "output": "{\"text\": \"Service -- friendly and attentive .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n->and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: Every course was better than the next .\n->Every course was better than the next .\n[{'aspect': 'course', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great food , great decor , great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat food , great decor , great service .\n->", + "output": "{\"text\": \"Great food , great decor , great service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: up date per may 13 / 2018 about two months ago , the charger wont work .\n->up date per may 13 / 2018 about two months ago , the charger wont work .\n[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: Great food , good size menu , great service and an unpretensious setting .\n->Great food , good size menu , great service and an unpretensious setting .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'good size', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'unpretensious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: And , atlhough tables opened up next to us and we ASKED for a slightly larger space , they left us awkardly seated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnd , atlhough tables opened up next to us and we ASKED for a slightly larger space , they left us awkardly seated .\n->", + "output": "{\"text\": \"And , atlhough tables opened up next to us and we ASKED for a slightly larger space , they left us awkardly seated .\", \"labels\": \"[{'aspect': 'space', 'opinion': 'larger', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything is very smooth and fast .\n->everything is very smooth and fast .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i use this laptop for work .\n->i use this laptop for work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: The palak paneer was standard , and I was not a fan of the malai kofta .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe palak paneer was standard , and I was not a fan of the malai kofta .\n->", + "output": "{\"text\": \"The palak paneer was standard , and I was not a fan of the malai kofta .\", \"labels\": \"[{'aspect': 'palak paneer', 'opinion': 'standard', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice little notebook !\n->nice little notebook !\n[{'aspect': 'notebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is junk .\n->this is junk .\n[{'aspect': 'NULL', 'opinion': 'junk', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: I 've dined at Alain Ducasse 's restaurant in Monte Carlo for half the price for the same excellent dining experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've dined at Alain Ducasse 's restaurant in Monte Carlo for half the price for the same excellent dining experience .\n->", + "output": "{\"text\": \"I 've dined at Alain Ducasse 's restaurant in Monte Carlo for half the price for the same excellent dining experience .\", \"labels\": \"[{'aspect': 'dining', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were worried we would have trouble getting in , but somehow managed to have a short wait .\n->we were worried we would have trouble getting in , but somehow managed to have a short wait .\n[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'NULL'}]\ntext: There was no ambiance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere was no ambiance .\n->", + "output": "{\"text\": \"There was no ambiance .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n->i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n[{'aspect': 'size', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: well . . . they can run but they ca n ' t hide .\n->well . . . they can run but they ca n ' t hide .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: Save your money and do n't waste your calories , go to Margharita 's on Washington Street instead , they have amazing food and the BEST service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSave your money and do n't waste your calories , go to Margharita 's on Washington Street instead , they have amazing food and the BEST service .\n->", + "output": "{\"text\": \"Save your money and do n't waste your calories , go to Margharita 's on Washington Street instead , they have amazing food and the BEST service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list is also really nice .\n->the wine list is also really nice .\n[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: decent wine at reasonable prices .\n->decent wine at reasonable prices .\n[{'aspect': 'wine', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\ntext: The only weird thing was if we got a bottle , the waitress would have simply multiplied the glass price X4 , which makes no sense whatsoever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe only weird thing was if we got a bottle , the waitress would have simply multiplied the glass price X4 , which makes no sense whatsoever .\n->", + "output": "{\"text\": \"The only weird thing was if we got a bottle , the waitress would have simply multiplied the glass price X4 , which makes no sense whatsoever .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'weird', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: long battery life .\n->long battery life .\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: everything lagged and the screen flickered .\n->everything lagged and the screen flickered .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: They sell special sushi , everything have a topping , sauce and etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey sell special sushi , everything have a topping , sauce and etc .\n->", + "output": "{\"text\": \"They sell special sushi , everything have a topping , sauce and etc .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n->i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n[{'aspect': 'keyboard', 'opinion': 'worried', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: i ' ve had this device for 5 days so i ' ll keep my review simple .\n->i ' ve had this device for 5 days so i ' ll keep my review simple .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\n->", + "output": "{\"text\": \"The place is small and intimate and you may feel a little crowded , but the service is excellent and it 's great for friends out , a romantic date , or a special occassion .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is great for reviewing adobe forms and web surfing , pretty good for youtube videos .\n->battery life is great for reviewing adobe forms and web surfing , pretty good for youtube videos .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: track pad is a little spongy , but definitely not a showstopper .\n->track pad is a little spongy , but definitely not a showstopper .\n[{'aspect': 'track pad', 'opinion': 'spongy', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: The service was excellent and the food was delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was excellent and the food was delicious .\n->", + "output": "{\"text\": \"The service was excellent and the food was delicious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Compared to Ess-a , Tal offers a less doughy bagel !\n->Compared to Ess-a , Tal offers a less doughy bagel !\n[{'aspect': 'bagel', 'opinion': 'less doughy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: good experience\n->good experience\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Service was also very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nService was also very good .\n->", + "output": "{\"text\": \"Service was also very good .\", \"labels\": \"[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I/we will never go back to this place again .\n->I/we will never go back to this place again .\n[{'aspect': 'place', 'opinion': 'never go back', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i really wanted to like this chromebook .\n->i really wanted to like this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'wanted to like', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: I took my girlfriend there for her birthday last night and we had a relaxing , really good meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI took my girlfriend there for her birthday last night and we had a relaxing , really good meal .\n->", + "output": "{\"text\": \"I took my girlfriend there for her birthday last night and we had a relaxing , really good meal .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when you ' re sitting in their main dining room ( which has a spectacular , hand - painted high ceiling ) you ' d never know there was a world outside .\n->when you ' re sitting in their main dining room ( which has a spectacular , hand - painted high ceiling ) you ' d never know there was a world outside .\n[{'aspect': 'main dining room', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ceiling', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ceiling', 'opinion': 'hand - painted high', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: never has it run out of power while on battery .\n->never has it run out of power while on battery .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: I 'm happy to have Nosh in the neighborhood and the food is very comforting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 'm happy to have Nosh in the neighborhood and the food is very comforting .\n->", + "output": "{\"text\": \"I 'm happy to have Nosh in the neighborhood and the food is very comforting .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'comforting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend to anyone to give this place a try .\n->i highly recommend to anyone to give this place a try .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: nicely sized , thin and portable , the works .\n->nicely sized , thin and portable , the works .\n[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: Good atmosphere , combination of all the hottest music dress code is relatively strict except on Fridays .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood atmosphere , combination of all the hottest music dress code is relatively strict except on Fridays .\n->", + "output": "{\"text\": \"Good atmosphere , combination of all the hottest music dress code is relatively strict except on Fridays .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'hottest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Normally that would be improper , however they were all delicious and my host did not complain .\n->Normally that would be improper , however they were all delicious and my host did not complain .\n[{'aspect': 'host', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: barely have it for 6 months and everything ' s going haywire .\n->barely have it for 6 months and everything ' s going haywire .\n[{'aspect': 'NULL', 'opinion': 'haywire', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: love the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the food .\n->", + "output": "{\"text\": \"love the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen resolution is good .\n->screen resolution is good .\n[{'aspect': 'screen resolution', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: The food now is inconsistent .\n->The food now is inconsistent .\n[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n->", + "output": "{\"text\": \"I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'surrounding', 'opinion': 'heart warming', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n->we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n[{'aspect': 'voss bottles of water', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: The location is perfect .\n->The location is perfect .\n[{'aspect': 'location', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: There 's something smooth about sipping sake upper east side style .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere 's something smooth about sipping sake upper east side style .\n->", + "output": "{\"text\": \"There 's something smooth about sipping sake upper east side style .\", \"labels\": \"[{'aspect': 'sake', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has a weird smell that ' s why i ' m giving it 3 stars .\n->it has a weird smell that ' s why i ' m giving it 3 stars .\n[{'aspect': 'NULL', 'opinion': 'weird', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n->From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caviar', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: It 's a nice place to relax and have conversation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIt 's a nice place to relax and have conversation .\n->", + "output": "{\"text\": \"It 's a nice place to relax and have conversation .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this mac .\n->i love this mac .\n[{'aspect': 'mac', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s much smoother with web pages and android apps , and the touch screen is more responsive .\n->it ' s much smoother with web pages and android apps , and the touch screen is more responsive .\n[{'aspect': 'web pages', 'opinion': 'smoother', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'smoother', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: Best Reuben sandwich ever !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBest Reuben sandwich ever !\n->", + "output": "{\"text\": \"Best Reuben sandwich ever !\", \"labels\": \"[{'aspect': 'Reuben sandwich', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We concluded with tiramisu chocolate cake , both were delicious .\n->We concluded with tiramisu chocolate cake , both were delicious .\n[{'aspect': 'tiramisu chocolate cake', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n->Decent Thai food in cute - though a bit dank - little Nolita hangout , BUT service terrible .\n[{'aspect': 'Thai food', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Lucky Strike is a great casual place to just grab a bite to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLucky Strike is a great casual place to just grab a bite to eat .\n->", + "output": "{\"text\": \"Lucky Strike is a great casual place to just grab a bite to eat .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great casual', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sushi was awful !\n->The sushi was awful !\n[{'aspect': 'sushi', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it performs good for the price tag attached but it will have a booting problem atlest once every week and you have to format the machine .\n->it performs good for the price tag attached but it will have a booting problem atlest once every week and you have to format the machine .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: Going to Volare is like going to your favorite aunt 's house for dinner , assuming that your aunt is a great Italian cook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGoing to Volare is like going to your favorite aunt 's house for dinner , assuming that your aunt is a great Italian cook .\n->", + "output": "{\"text\": \"Going to Volare is like going to your favorite aunt 's house for dinner , assuming that your aunt is a great Italian cook .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however there are many issues with this computer on start up that really bothered me and made my experience with a mac not that great\n->however there are many issues with this computer on start up that really bothered me and made my experience with a mac not that great\n[{'aspect': 'computer', 'opinion': 'bothered', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'mac', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - boot time , sleep time and wake time are crazy fast .\n->- boot time , sleep time and wake time are crazy fast .\n[{'aspect': 'boot time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'boot time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\ntext: The sushi has been from average to below average , the wait service has always been subpar the atmosphere goes from nice to really irritating ( if you sit in the area beyond the kitchen , the acousitcs are horrid , everything echoes is extremely loud ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sushi has been from average to below average , the wait service has always been subpar the atmosphere goes from nice to really irritating ( if you sit in the area beyond the kitchen , the acousitcs are horrid , everything echoes is extremely loud ) .\n->", + "output": "{\"text\": \"The sushi has been from average to below average , the wait service has always been subpar the atmosphere goes from nice to really irritating ( if you sit in the area beyond the kitchen , the acousitcs are horrid , everything echoes is extremely loud ) .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait service', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'area', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All in all the food was above average and I would return to see how they operate with four or less dinners .\n->All in all the food was above average and I would return to see how they operate with four or less dinners .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i can not imagine you not rushing out to eat there .\n->i can not imagine you not rushing out to eat there .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Frites were delicious if a bit on the thick side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrites were delicious if a bit on the thick side .\n->", + "output": "{\"text\": \"Frites were delicious if a bit on the thick side .\", \"labels\": \"[{'aspect': 'Frites', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: laptop was working fine until just under 3 months of use when it bsod ' d and wouldn ' t turn back on .\n->laptop was working fine until just under 3 months of use when it bsod ' d and wouldn ' t turn back on .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: keeps disconnecting from my wifi at work .\n->keeps disconnecting from my wifi at work .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n->", + "output": "{\"text\": \"I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\", \"labels\": \"[{'aspect': 'drink', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a great place to meet up for some food and drinks . . .\n->a great place to meet up for some food and drinks . . .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: first time user for windows 10 and it ' s pretty good .\n->first time user for windows 10 and it ' s pretty good .\n[{'aspect': 'windows 10', 'opinion': 'good', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\ntext: I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n->", + "output": "{\"text\": \"I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\", \"labels\": \"[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sauces used are also not that exciting .\n->The sauces used are also not that exciting .\n[{'aspect': 'sauces', 'opinion': 'not that exciting', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i love it .\n->i love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Overall , the best bagel in town .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOverall , the best bagel in town .\n->", + "output": "{\"text\": \"Overall , the best bagel in town .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he wanted something that could browse the internet fast - and this chromebook does just that !\n->he wanted something that could browse the internet fast - and this chromebook does just that !\n[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: cheese plate is a varied delight and great bargain at $ 10 .\n->cheese plate is a varied delight and great bargain at $ 10 .\n[{'aspect': 'cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: Light , refreshing summer rolls ( not fried ) remind me of Vietnamese places in Paris .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nLight , refreshing summer rolls ( not fried ) remind me of Vietnamese places in Paris .\n->", + "output": "{\"text\": \"Light , refreshing summer rolls ( not fried ) remind me of Vietnamese places in Paris .\", \"labels\": \"[{'aspect': 'summer rolls', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice screen and keyboard , touch pad is great .\n->nice screen and keyboard , touch pad is great .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: after running through the setup wizard , the laptop failed to boot .\n->after running through the setup wizard , the laptop failed to boot .\n[{'aspect': 'laptop', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: The sushi seemed pretty fresh and was adequately proportioned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sushi seemed pretty fresh and was adequately proportioned .\n->", + "output": "{\"text\": \"The sushi seemed pretty fresh and was adequately proportioned .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'proportioned', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best of all is the warm vibe , the owner is super friendly and service is fast .\n->Best of all is the warm vibe , the owner is super friendly and service is fast .\n[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Sure , the setting is nice .\n->Sure , the setting is nice .\n[{'aspect': 'setting', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but the service was a bit slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the service was a bit slow .\n->", + "output": "{\"text\": \"but the service was a bit slow .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n->An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n[{'aspect': 'chef', 'opinion': 'passion', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish dishes', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soups', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kitchen', 'opinion': 'precise execution', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i heard the lobster roll was excellent .\n->i heard the lobster roll was excellent .\n[{'aspect': 'lobster roll', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Spreads and toppings are great - though a bit pricey .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSpreads and toppings are great - though a bit pricey .\n->", + "output": "{\"text\": \"Spreads and toppings are great - though a bit pricey .\", \"labels\": \"[{'aspect': 'Spreads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Spreads', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is a consistently great place to dine for lunch or dinner .\n->This is a consistently great place to dine for lunch or dinner .\n[{'aspect': 'dine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food looked very appetizing and delicious since it came on a variety of fancy plates .\n->The food looked very appetizing and delicious since it came on a variety of fancy plates .\n[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'plates', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Indoor was very cozy and cute .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIndoor was very cozy and cute .\n->", + "output": "{\"text\": \"Indoor was very cozy and cute .\", \"labels\": \"[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n->While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery last 2 1 / 2 hours\n->battery last 2 1 / 2 hours\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: We had a 3 hour brunch- they definitely do not rush you- and they kept the unlimited mimosas flowing the whole time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe had a 3 hour brunch- they definitely do not rush you- and they kept the unlimited mimosas flowing the whole time .\n->", + "output": "{\"text\": \"We had a 3 hour brunch- they definitely do not rush you- and they kept the unlimited mimosas flowing the whole time .\", \"labels\": \"[{'aspect': 'mimosas', 'opinion': 'unlimited', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really wanted to like this chromebook .\n->i really wanted to like this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'wanted to like', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n->The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n[{'aspect': 'tables', 'opinion': 'crammed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'too close', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The service was excellent , the food was excellent , but the entire experience was very cool .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was excellent , the food was excellent , but the entire experience was very cool .\n->", + "output": "{\"text\": \"The service was excellent , the food was excellent , but the entire experience was very cool .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n->when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n[{'aspect': 'NULL', 'opinion': 'okay', 'polarity': 'positive', 'category': 'FANS&COOLING#GENERAL'}]\nExample:\ntext: I am not a vegetarian but , almost all the dishes were great .\n->I am not a vegetarian but , almost all the dishes were great .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service is good and the resturant is clean .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is good and the resturant is clean .\n->", + "output": "{\"text\": \"The service is good and the resturant is clean .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very impressive design .\n->very impressive design .\n[{'aspect': 'design', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: system shutdown problems every month .\n->system shutdown problems every month .\n[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: The cafe itself was really nice with comfortable outdoor chairs and tables , but the service could have been better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe cafe itself was really nice with comfortable outdoor chairs and tables , but the service could have been better .\n->", + "output": "{\"text\": \"The cafe itself was really nice with comfortable outdoor chairs and tables , but the service could have been better .\", \"labels\": \"[{'aspect': 'cafe', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor chairs', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We wo n't go to this place again for a good meal .\n->We wo n't go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: ambiance - relaxed and stylish .\n->ambiance - relaxed and stylish .\n[{'aspect': 'ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: I 've rarely had a problem with slow staff in the 10 years I 've been going .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've rarely had a problem with slow staff in the 10 years I 've been going .\n->", + "output": "{\"text\": \"I 've rarely had a problem with slow staff in the 10 years I 've been going .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'slow', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a classic !\n->a classic !\n[{'aspect': 'NULL', 'opinion': 'classic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the manager was rude and handled the situation extremely poorly .\n->the manager was rude and handled the situation extremely poorly .\n[{'aspect': 'manager', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'manager', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: Food is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFood is excellent .\n->", + "output": "{\"text\": \"Food is excellent .\", \"labels\": \"[{'aspect': 'Food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: their bagels are fine , but they are a little overcooked , and not really a ' special ' bagel experience .\n->their bagels are fine , but they are a little overcooked , and not really a ' special ' bagel experience .\n[{'aspect': 'bagels', 'opinion': 'fine', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: solid inexpensive computer for our 10 year old\n->solid inexpensive computer for our 10 year old\n[{'aspect': 'computer', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: Although we were looking for regular lettuce and some walnuts the salads we got were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAlthough we were looking for regular lettuce and some walnuts the salads we got were great .\n->", + "output": "{\"text\": \"Although we were looking for regular lettuce and some walnuts the salads we got were great .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lettuce', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'walnuts', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was due to upgrade and this product seemed perfect for me .\n->i was due to upgrade and this product seemed perfect for me .\n[{'aspect': 'product', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - the battery life is at least 8 hours .\n->- the battery life is at least 8 hours .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Excellent lunch buffet for only $ 6.95 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nExcellent lunch buffet for only $ 6.95 .\n->", + "output": "{\"text\": \"Excellent lunch buffet for only $ 6.95 .\", \"labels\": \"[{'aspect': 'lunch buffet', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was mediocre at best but it was the horrible service that made me vow never to go back .\n->the food was mediocre at best but it was the horrible service that made me vow never to go back .\n[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Great selection of wine , and seafood .\n->Great selection of wine , and seafood .\n[{'aspect': 'selection of wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAmbience is so cute and quaint , good for business although we were there on vacation .\n->", + "output": "{\"text\": \"Ambience is so cute and quaint , good for business although we were there on vacation .\", \"labels\": \"[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so what you really end up paying for is the restaurant not the food .\n->so what you really end up paying for is the restaurant not the food .\n[{'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: overall , not worth the money .\n->overall , not worth the money .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: The portion sizes here are huge , and the sushi is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe portion sizes here are huge , and the sushi is good .\n->", + "output": "{\"text\": \"The portion sizes here are huge , and the sushi is good .\", \"labels\": \"[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: an awesome organic dog , and a conscious eco friendly establishment .\n->an awesome organic dog , and a conscious eco friendly establishment .\n[{'aspect': 'dog', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dog', 'opinion': 'organic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'establishment', 'opinion': 'eco friendly', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: try the sea bass .\n->try the sea bass .\n[{'aspect': 'sea bass', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Good food at the restaurant ( a bit expensive , but great if you want to impress your date ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood food at the restaurant ( a bit expensive , but great if you want to impress your date ) .\n->", + "output": "{\"text\": \"Good food at the restaurant ( a bit expensive , but great if you want to impress your date ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n->We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n[{'aspect': 'scenery', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner reservations', 'opinion': 'early', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: on a side note , there is a slight defect on my chromebook as there is some creaking and loose feeling when pressing on the bottom left side of my screen , this can be especially annoying in tablet mode .\n->on a side note , there is a slight defect on my chromebook as there is some creaking and loose feeling when pressing on the bottom left side of my screen , this can be especially annoying in tablet mode .\n[{'aspect': 'chromebook', 'opinion': 'defect', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->", + "output": "{\"text\": \"I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\", \"labels\": \"[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n->The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n[{'aspect': 'coat check girls', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the touch screen is super responsive and the keyboard is excellent .\n->the touch screen is super responsive and the keyboard is excellent .\n[{'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: I have eaten there 3-4 times and the food was always good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have eaten there 3-4 times and the food was always good .\n->", + "output": "{\"text\": \"I have eaten there 3-4 times and the food was always good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fresh , authentic , french cuisine in substantial portions .\n->Fresh , authentic , french cuisine in substantial portions .\n[{'aspect': 'french cuisine', 'opinion': 'Fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french cuisine', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'substantial', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Service is average .\n->Service is average .\n[{'aspect': 'Service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Grilled whole fish wonderful , great spicing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGrilled whole fish wonderful , great spicing .\n->", + "output": "{\"text\": \"Grilled whole fish wonderful , great spicing .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well , this place is so ghetto its not even funny .\n->well , this place is so ghetto its not even funny .\n[{'aspect': 'place', 'opinion': 'ghetto', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'not even funny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: this is the ultimate tablet .\n->this is the ultimate tablet .\n[{'aspect': 'tablet', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: For years , I thought Tuscan cuisine was the best , but Salvatore converted me to the hearty Neapolitan fare on my first visit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor years , I thought Tuscan cuisine was the best , but Salvatore converted me to the hearty Neapolitan fare on my first visit .\n->", + "output": "{\"text\": \"For years , I thought Tuscan cuisine was the best , but Salvatore converted me to the hearty Neapolitan fare on my first visit .\", \"labels\": \"[{'aspect': 'Neapolitan fare', 'opinion': 'hearty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fish was not fresh and the rice tasted old and stale .\n->The fish was not fresh and the rice tasted old and stale .\n[{'aspect': 'fish', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The sauce is delicious and the crust is perfect .\n->The sauce is delicious and the crust is perfect .\n[{'aspect': 'sauce', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service was great as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was great as well .\n->", + "output": "{\"text\": \"The service was great as well .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: awsome pizza especially the margheritta slice .\n->awsome pizza especially the margheritta slice .\n[{'aspect': 'pizza', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margheritta slice', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the food here does a great service to the name ( cantonese that is . . . ) .\n->the food here does a great service to the name ( cantonese that is . . . ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: Another plus is most of the entrees are approx .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAnother plus is most of the entrees are approx .\n->", + "output": "{\"text\": \"Another plus is most of the entrees are approx .\", \"labels\": \"[{'aspect': 'entrees', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was efficient courteous .\n->service was efficient courteous .\n[{'aspect': 'service', 'opinion': 'efficient courteous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery life is excellent , and i can get several days use before needing to plug in .\n->battery life is excellent , and i can get several days use before needing to plug in .\n[{'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: From the moment we walked in they were more than accomodating even though the place was packed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFrom the moment we walked in they were more than accomodating even though the place was packed .\n->", + "output": "{\"text\": \"From the moment we walked in they were more than accomodating even though the place was packed .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so i decide to report back to the waitress because it was completely inedible .\n->so i decide to report back to the waitress because it was completely inedible .\n[{'aspect': 'NULL', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i loved how fast it was .\n->i loved how fast it was .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: I found the food , service and value exceptional everytime I have been there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI found the food , service and value exceptional everytime I have been there .\n->", + "output": "{\"text\": \"I found the food , service and value exceptional everytime I have been there .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not imagine a friendlier staff working in a restaurant .\n->i can not imagine a friendlier staff working in a restaurant .\n[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Do n't expect to sit down inside though , there are only a few tables and they are always full .\n->Do n't expect to sit down inside though , there are only a few tables and they are always full .\n[{'aspect': 'tables', 'opinion': 'few', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'full', 'polarity': 'negative', 'category': 'NULL'}]\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe ate at this Thai place following the reviews but very unhappy with the foods .\n->", + "output": "{\"text\": \"We ate at this Thai place following the reviews but very unhappy with the foods .\", \"labels\": \"[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n->i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: too bad the food was n ' t of the same heritage .\n->too bad the food was n ' t of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The food is definitely good , but I left a bit disappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food is definitely good , but I left a bit disappointed .\n->", + "output": "{\"text\": \"The food is definitely good , but I left a bit disappointed .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'disappointed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mermaid inn is an overall good restaurant with really good seafood .\n->mermaid inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mermaid inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n->the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n[{'aspect': 'keyboard', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: The drinks are a saving grace , but service staff , please , get over yourselves .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe drinks are a saving grace , but service staff , please , get over yourselves .\n->", + "output": "{\"text\": \"The drinks are a saving grace , but service staff , please , get over yourselves .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'saving grace', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We were looking forward to nice glass of Sangria when we arrived .\n->We were looking forward to nice glass of Sangria when we arrived .\n[{'aspect': 'glass of Sangria', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a wonderful device with extremely clear display .\n->a wonderful device with extremely clear display .\n[{'aspect': 'device', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThere is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n->", + "output": "{\"text\": \"There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\", \"labels\": \"[{'aspect': 'delivery guys', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the jukebox plays everything from italian opera to the strokes .\n->the jukebox plays everything from italian opera to the strokes .\n[{'aspect': 'jukebox', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it has the specs but that ' s it ' s main downfall .\n->it has the specs but that ' s it ' s main downfall .\n[{'aspect': 'specs', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The all-Italian staff is warm and engaging from the start .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe all-Italian staff is warm and engaging from the start .\n->", + "output": "{\"text\": \"The all-Italian staff is warm and engaging from the start .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'engaging', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n->Excellent atmosphere , delicious dishes good and friendly service .\n[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n->The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The prices are exceptionally reasonable for food of this caliber .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe prices are exceptionally reasonable for food of this caliber .\n->", + "output": "{\"text\": \"The prices are exceptionally reasonable for food of this caliber .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n->Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n[{'aspect': 'meal', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: our waitress was n ' t mean , but not especially warm or attentive either .\n->our waitress was n ' t mean , but not especially warm or attentive either .\n[{'aspect': 'waitress', 'opinion': \"n ' t mean\", 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitress', 'opinion': 'not especially warm or attentive', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: The filet mignon dish was superb !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe filet mignon dish was superb !\n->", + "output": "{\"text\": \"The filet mignon dish was superb !\", \"labels\": \"[{'aspect': 'filet mignon dish', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n->the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n[{'aspect': 'space', 'opinion': 'limited', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'indo - chinese food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Odd for Ave B , not just odd , The place attracts an eclectic crowd to say the least .\n->Odd for Ave B , not just odd , The place attracts an eclectic crowd to say the least .\n[{'aspect': 'place', 'opinion': 'odd', 'polarity': 'positive', 'category': 'NULL'}]\ntext: I had been a regular due to the consistently good food and ease of getting a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had been a regular due to the consistently good food and ease of getting a table .\n->", + "output": "{\"text\": \"I had been a regular due to the consistently good food and ease of getting a table .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'ease', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Salads were fantastic .\n->Salads were fantastic .\n[{'aspect': 'Salads', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n->everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n->", + "output": "{\"text\": \"I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\", \"labels\": \"[{'aspect': 'pastrami on challah sandwich', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the negatives , the battery will need charging what feels like every 6ish hours depending on use and the speakers are insufficient .\n->the negatives , the battery will need charging what feels like every 6ish hours depending on use and the speakers are insufficient .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'insufficient', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: everything we had was good or ok . . . . but definitely nothing great .\n->everything we had was good or ok . . . . but definitely nothing great .\n[{'aspect': 'NULL', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->", + "output": "{\"text\": \"The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the backlit keyboard looks nice .\n->- the backlit keyboard looks nice .\n[{'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: that seemed to correct problem , but problem returned next day and the battery would only charge up to 1 % with charger plugged in .\n->that seemed to correct problem , but problem returned next day and the battery would only charge up to 1 % with charger plugged in .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: Cozy romantic atomosphere with only around 15 tables at most .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nCozy romantic atomosphere with only around 15 tables at most .\n->", + "output": "{\"text\": \"Cozy romantic atomosphere with only around 15 tables at most .\", \"labels\": \"[{'aspect': 'atomosphere', 'opinion': 'Cozy romantic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will see if samsung honors their warrnty , if so , i will probably change / update this review .\n->i will see if samsung honors their warrnty , if so , i will probably change / update this review .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: the nakgi - bokum was horrible .\n->the nakgi - bokum was horrible .\n[{'aspect': 'nakgi - bokum', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: This place is worth going even if only for their beer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is worth going even if only for their beer .\n->", + "output": "{\"text\": \"This place is worth going even if only for their beer .\", \"labels\": \"[{'aspect': 'beer', 'opinion': 'worth going', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s basically a useless brick , with shoddy hardware .\n->it ' s basically a useless brick , with shoddy hardware .\n[{'aspect': 'NULL', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'shoddy', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: now there are pixels on the screen not working , and they are multiplying .\n->now there are pixels on the screen not working , and they are multiplying .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n->", + "output": "{\"text\": \"The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you do n ' t like it , i do n ' t know what to tell you .\n->if you do n ' t like it , i do n ' t know what to tell you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n->my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n[{'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: The sandwhiches are out of this world !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe sandwhiches are out of this world !\n->", + "output": "{\"text\": \"The sandwhiches are out of this world !\", \"labels\": \"[{'aspect': 'sandwhiches', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bar drinks were eh , ok to say the least .\n->the bar drinks were eh , ok to say the least .\n[{'aspect': 'bar drinks', 'opinion': 'ok', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: i literally just got back home after visiting casa la femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .\n->i literally just got back home after visiting casa la femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .\n[{'aspect': 'casa la femme', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'casa la femme', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: Decor is charming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDecor is charming .\n->", + "output": "{\"text\": \"Decor is charming .\", \"labels\": \"[{'aspect': 'Decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n->i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n[{'aspect': 'dining garden', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'jazz bar', 'opinion': 'new', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'thin crust pizzas', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lasagna menu', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i expected quite a bit more from such an expensive menu .\n->i expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: The food was very well prepared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was very well prepared .\n->", + "output": "{\"text\": \"The food was very well prepared .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bottles of wine are cheap and good .\n->bottles of wine are cheap and good .\n[{'aspect': 'bottles of wine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bottles of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Lucky Strike is a great casual place to just grab a bite to eat .\n->Lucky Strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'place', 'opinion': 'great casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGood , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n->", + "output": "{\"text\": \"Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n->the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n->thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n[{'aspect': 'windows 10', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n->", + "output": "{\"text\": \"While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n->it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n[{'aspect': 'screen', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'bizarre', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n->don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n[{'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'ugly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Complimentary stuff kept coming , and when the waiter saw me opening a gift , I received my dessert on a plate that had Happy Birthday written on it , with a candlevery nice touch , and attentive staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nComplimentary stuff kept coming , and when the waiter saw me opening a gift , I received my dessert on a plate that had Happy Birthday written on it , with a candlevery nice touch , and attentive staff .\n->", + "output": "{\"text\": \"Complimentary stuff kept coming , and when the waiter saw me opening a gift , I received my dessert on a plate that had Happy Birthday written on it , with a candlevery nice touch , and attentive staff .\", \"labels\": \"[{'aspect': 'stuff', 'opinion': 'Complimentary', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not leave now because i need my computer fixed .\n->i can not leave now because i need my computer fixed .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i bought this as a gift and was incredibly disappointed as it did n ' t even turn on after the initial charging .\n->i bought this as a gift and was incredibly disappointed as it did n ' t even turn on after the initial charging .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The food arrived 20 minutes after I called , cold and soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food arrived 20 minutes after I called , cold and soggy .\n->", + "output": "{\"text\": \"The food arrived 20 minutes after I called , cold and soggy .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they do a ton of online homework and this is perfect for them .\n->they do a ton of online homework and this is perfect for them .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i led with those first five points for a reason : being able to purchase a decently - performing chromebook with aluminum build , a 1080p ips display , 4gb of ram , 32gb of local storage , and an intel quad - core processor for $ 299 is basically what the entirely of the chromeos subreddit has been asking for over the past few years .\n->i led with those first five points for a reason : being able to purchase a decently - performing chromebook with aluminum build , a 1080p ips display , 4gb of ram , 32gb of local storage , and an intel quad - core processor for $ 299 is basically what the entirely of the chromeos subreddit has been asking for over the past few years .\n[{'aspect': 'NULL', 'opinion': 'decently', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: If you go for the pre-theatre menu , it 's an even greater deal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you go for the pre-theatre menu , it 's an even greater deal .\n->", + "output": "{\"text\": \"If you go for the pre-theatre menu , it 's an even greater deal .\", \"labels\": \"[{'aspect': 'pre-theatre menu', 'opinion': 'greater', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a nice perk to save battery life and for folks who like to read / / browse late at night in bed and do n ' t want the backlighting on .\n->this is a nice perk to save battery life and for folks who like to read / / browse late at night in bed and do n ' t want the backlighting on .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i ' m loving this thing .\n->i ' m loving this thing .\n[{'aspect': 'NULL', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: If you are someone who appreciates simplicity , elegance , and wonderfully presented and tasting seafood and vegetables regardless of portion size , Kai is your place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIf you are someone who appreciates simplicity , elegance , and wonderfully presented and tasting seafood and vegetables regardless of portion size , Kai is your place .\n->", + "output": "{\"text\": \"If you are someone who appreciates simplicity , elegance , and wonderfully presented and tasting seafood and vegetables regardless of portion size , Kai is your place .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'wonderfully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'tasting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'wonderfully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'tasting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far , it all works well .\n->so far , it all works well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n->the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n[{'aspect': 'decor', 'opinion': 'diner - ish', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\ntext: We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n->", + "output": "{\"text\": \"We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\", \"labels\": \"[{'aspect': 'dinner specials', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner specials', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have not fully tested battery life but it seems to last about as long as advertised .\n->i have not fully tested battery life but it seems to last about as long as advertised .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: fast delivery , brand new as expected .\n->fast delivery , brand new as expected .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\ntext: Horrible food and horrible service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHorrible food and horrible service .\n->", + "output": "{\"text\": \"Horrible food and horrible service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After we got our sashimi order , I could not believe how small the portions were !\n->After we got our sashimi order , I could not believe how small the portions were !\n[{'aspect': 'sashimi', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: even though the place is not beautiful , the food speaks for itself .\n->even though the place is not beautiful , the food speaks for itself .\n[{'aspect': 'place', 'opinion': 'not beautiful', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'speaks for itself', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: They treated us well and the food was extremely fresh and well-prepared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey treated us well and the food was extremely fresh and well-prepared .\n->", + "output": "{\"text\": \"They treated us well and the food was extremely fresh and well-prepared .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'well-prepared', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m glad i was introduced to this place and this is a rare gem in ny .\n->i ' m glad i was introduced to this place and this is a rare gem in ny .\n[{'aspect': 'place', 'opinion': 'glad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: overpriced low quality product .\n->overpriced low quality product .\n[{'aspect': 'product', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'low', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: Their exotic salad is basic ly a delicious little green salad with a peanut sauce that is perfect before their sweet basil fried tofu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTheir exotic salad is basic ly a delicious little green salad with a peanut sauce that is perfect before their sweet basil fried tofu .\n->", + "output": "{\"text\": \"Their exotic salad is basic ly a delicious little green salad with a peanut sauce that is perfect before their sweet basil fried tofu .\", \"labels\": \"[{'aspect': 'exotic salad', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'green salad', 'opinion': 'delicious little', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'peanut sauce', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of my favorite things i own\n->one of my favorite things i own\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The pizza was great .\n->The pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Even if the food was n't this good , the garden is a great place to sit outside and relax .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEven if the food was n't this good , the garden is a great place to sit outside and relax .\n->", + "output": "{\"text\": \"Even if the food was n't this good , the garden is a great place to sit outside and relax .\", \"labels\": \"[{'aspect': 'food', 'opinion': \"was n't this good\", 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'garden', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: if you want good authentic thai this place is not the place to go .\n->if you want good authentic thai this place is not the place to go .\n[{'aspect': 'thai', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nVolare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly .\n->", + "output": "{\"text\": \"Volare virgins or weekly regulars , everyone gets treated the same and you ca n't ask for more than that when the service is this friendly .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: priced well .\n->priced well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Do n't waste money on decor .\n->Do n't waste money on decor .\n[{'aspect': 'decor', 'opinion': 'waste', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: The food came out wrong , the waiter was no where to be found and the wine showed up at the end of the meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food came out wrong , the waiter was no where to be found and the wine showed up at the end of the meal .\n->", + "output": "{\"text\": \"The food came out wrong , the waiter was no where to be found and the wine showed up at the end of the meal .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pro : light , reasonable price , fast .\n->pro : light , reasonable price , fast .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Best Pastrami I ever had and great portion without being ridiculous .\n->Best Pastrami I ever had and great portion without being ridiculous .\n[{'aspect': 'Pastrami', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The chicken and steak were seasoned and cooked to perfection , and the lamb sandwhich is great for heartier appetites .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe chicken and steak were seasoned and cooked to perfection , and the lamb sandwhich is great for heartier appetites .\n->", + "output": "{\"text\": \"The chicken and steak were seasoned and cooked to perfection , and the lamb sandwhich is great for heartier appetites .\", \"labels\": \"[{'aspect': 'chicken', 'opinion': 'seasoned', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'seasoned', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb sandwhich', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first went here to enjoy their garden terrace .\n->first went here to enjoy their garden terrace .\n[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n->Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The food was great and the service was even better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food was great and the service was even better .\n->", + "output": "{\"text\": \"The food was great and the service was even better .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Rao 's has the best service and atmosphere in NYC .\n->Rao 's has the best service and atmosphere in NYC .\n[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: in many ways , the computer has lived up to my expectations .\n->in many ways , the computer has lived up to my expectations .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: While this can hardly be called a restaurant , it is possibly the best deal in Manhatten : $ 4 for a plate heaped with rice and 2-3 entrees .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhile this can hardly be called a restaurant , it is possibly the best deal in Manhatten : $ 4 for a plate heaped with rice and 2-3 entrees .\n->", + "output": "{\"text\": \"While this can hardly be called a restaurant , it is possibly the best deal in Manhatten : $ 4 for a plate heaped with rice and 2-3 entrees .\", \"labels\": \"[{'aspect': 'rice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just made the move from pc to macbook !\n->just made the move from pc to macbook !\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n->the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTraditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n->", + "output": "{\"text\": \"Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\", \"labels\": \"[{'aspect': 'Traditional French decour', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hall', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this device is mainly used for web browsing and pages load quickly , animations are swift and not laggy .\n->this device is mainly used for web browsing and pages load quickly , animations are swift and not laggy .\n[{'aspect': 'device', 'opinion': 'swift', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'not laggy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - good ram\n->- good ram\n[{'aspect': 'ram', 'opinion': 'good', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\ntext: My son and his girlfriend both wanted cheeseburgers and they were huge !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nMy son and his girlfriend both wanted cheeseburgers and they were huge !\n->", + "output": "{\"text\": \"My son and his girlfriend both wanted cheeseburgers and they were huge !\", \"labels\": \"[{'aspect': 'cheeseburgers', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n->the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n[{'aspect': 'keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'back light', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: however , it does feel like a sturdy hinge .\n->however , it does feel like a sturdy hinge .\n[{'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: I really recommend the very simple Unda ( Egg ) rolls .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI really recommend the very simple Unda ( Egg ) rolls .\n->", + "output": "{\"text\": \"I really recommend the very simple Unda ( Egg ) rolls .\", \"labels\": \"[{'aspect': 'Unda ( Egg ) rolls', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Unda ( Egg ) rolls', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the machine looks amazing doesn ' t it !\n->the machine looks amazing doesn ' t it !\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n->Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n[{'aspect': 'Appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Very good wine choices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nVery good wine choices .\n->", + "output": "{\"text\": \"Very good wine choices .\", \"labels\": \"[{'aspect': 'wine choices', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: though it is competitively priced for the specs , the laptop felt cheap .\n->though it is competitively priced for the specs , the laptop felt cheap .\n[{'aspect': 'specs', 'opinion': 'competitively', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'laptop', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The freshest , best variety , and the fastest delivery .\n->The freshest , best variety , and the fastest delivery .\n[{'aspect': 'variety', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n->", + "output": "{\"text\": \"This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\", \"labels\": \"[{'aspect': 'Jazz', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the seafood dynamite is also otherworldly .\n->the seafood dynamite is also otherworldly .\n[{'aspect': 'seafood dynamite', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: within two days of receiving this item a line appeared on my screen ( while i was using it , previously fine ) and below it the image / screen flickered .\n->within two days of receiving this item a line appeared on my screen ( while i was using it , previously fine ) and below it the image / screen flickered .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: We were seated and ignored by waitstaff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were seated and ignored by waitstaff .\n->", + "output": "{\"text\": \"We were seated and ignored by waitstaff .\", \"labels\": \"[{'aspect': 'waitstaff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The counter service is bad .\n->The counter service is bad .\n[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: mermaid inn is an overall good restaurant with really good seafood .\n->mermaid inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mermaid inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: Tasty steak , pork loin , the works .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nTasty steak , pork loin , the works .\n->", + "output": "{\"text\": \"Tasty steak , pork loin , the works .\", \"labels\": \"[{'aspect': 'steak', 'opinion': 'Tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork loin', 'opinion': 'Tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n->Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n[{'aspect': 'Thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ll give the chromebook pro an extra star for its best asset , the screen .\n->i ' ll give the chromebook pro an extra star for its best asset , the screen .\n[{'aspect': 'screen', 'opinion': 'best', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n->", + "output": "{\"text\": \"I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\", \"labels\": \"[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality on this laptop is awesome for the price .\n->the build quality on this laptop is awesome for the price .\n[{'aspect': 'build quality', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Fish was overdone .\n->Fish was overdone .\n[{'aspect': 'Fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The burger was great , also .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe burger was great , also .\n->", + "output": "{\"text\": \"The burger was great , also .\", \"labels\": \"[{'aspect': 'burger', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service was friendly and the atmosphere was casual .\n->the service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: food is great and inexpensive .\n->food is great and inexpensive .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n->", + "output": "{\"text\": \"they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\", \"labels\": \"[{'aspect': 'discount', 'opinion': \"was n't enough\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we waited at the bar and had martinis that were just right .\n->we waited at the bar and had martinis that were just right .\n[{'aspect': 'martinis', 'opinion': 'right', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: we did n ' t want a bottle of bubbly on a weekday so we each got little bottles of korbett it was just enough .\n->we did n ' t want a bottle of bubbly on a weekday so we each got little bottles of korbett it was just enough .\n[{'aspect': 'bottles of korbett', 'opinion': 'enough', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: The pizza was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe pizza was great .\n->", + "output": "{\"text\": \"The pizza was great .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Dim Sum was so-so , but not spectacular .\n->The Dim Sum was so-so , but not spectacular .\n[{'aspect': 'Dim Sum', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Dim Sum', 'opinion': 'not spectacular', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it is also very gnu + linux friendly if you want to replace the os entirely .\n->it is also very gnu + linux friendly if you want to replace the os entirely .\n[{'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n->", + "output": "{\"text\": \"I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\", \"labels\": \"[{'aspect': 'Edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The entree was bland and small , dessert was not inspired .\n->The entree was bland and small , dessert was not inspired .\n[{'aspect': 'entree', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'entree', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'not inspired', 'polarity': 'negative', 'category': 'NULL'}]\ntext: however , it 's the service that leaves a bad taste in my mouth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , it 's the service that leaves a bad taste in my mouth .\n->", + "output": "{\"text\": \"however , it 's the service that leaves a bad taste in my mouth .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love love love this laptop !\n->love love love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: what a hassle !\n->what a hassle !\n[{'aspect': 'NULL', 'opinion': 'hassle', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: The price was extremely reasonable for the appetizers and food we ate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe price was extremely reasonable for the appetizers and food we ate .\n->", + "output": "{\"text\": \"The price was extremely reasonable for the appetizers and food we ate .\", \"labels\": \"[{'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try green curry with vegetables .\n->Try green curry with vegetables .\n[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Although we were looking for regular lettuce and some walnuts the salads we got were great .\n->Although we were looking for regular lettuce and some walnuts the salads we got were great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lettuce', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'walnuts', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDo n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\n->", + "output": "{\"text\": \"Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\", \"labels\": \"[{'aspect': 'vegetarian dishes', 'opinion': 'not up to par', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n->Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is my first macbook pro i ' m impress !\n->this is my first macbook pro i ' m impress !\n[{'aspect': 'macbook pro', 'opinion': 'impress', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: They need a kick out of it but until then the sushi is pretty good and the place is consistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThey need a kick out of it but until then the sushi is pretty good and the place is consistent .\n->", + "output": "{\"text\": \"They need a kick out of it but until then the sushi is pretty good and the place is consistent .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'consistent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer has identified the chromebook market gaps and solved almost everything in this extremely solid offering - a thin , metal , almost mac - like build , armed with 4gb of ram , a full 14 ` ` hd display and a quad - core cpu .\n->acer has identified the chromebook market gaps and solved almost everything in this extremely solid offering - a thin , metal , almost mac - like build , armed with 4gb of ram , a full 14 ` ` hd display and a quad - core cpu .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: great device for browsing the internet .\n->great device for browsing the internet .\n[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: I 've had to wait only a few times during lunch but this place is definitely worth the wait .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI 've had to wait only a few times during lunch but this place is definitely worth the wait .\n->", + "output": "{\"text\": \"I 've had to wait only a few times during lunch but this place is definitely worth the wait .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'worth the wait', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Late nite omelletes are not good here , there is no variety !\n->Late nite omelletes are not good here , there is no variety !\n[{'aspect': 'omelletes', 'opinion': 'not good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: keyboard key fragile .\n->keyboard key fragile .\n[{'aspect': 'keyboard', 'opinion': 'fragile', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: All in all the food was above average and I would return to see how they operate with four or less dinners .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAll in all the food was above average and I would return to see how they operate with four or less dinners .\n->", + "output": "{\"text\": \"All in all the food was above average and I would return to see how they operate with four or less dinners .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n->However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n[{'aspect': 'pizza', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: fresh restaurant was amazing ... ... .. food was delicious and of course fresh .\n->fresh restaurant was amazing ... ... .. food was delicious and of course fresh .\n[{'aspect': 'fresh restaurant', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh restaurant', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDelicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n->", + "output": "{\"text\": \"Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch buffet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: oh , and i never write reviews - - i just was so moved by how bad this place was , i felt it was my duty to spread the word .\n->oh , and i never write reviews - - i just was so moved by how bad this place was , i felt it was my duty to spread the word .\n[{'aspect': 'place', 'opinion': 'bad', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: But they do n't have a toaster , which is strange .\n->But they do n't have a toaster , which is strange .\n[{'aspect': 'toaster', 'opinion': 'strange', 'polarity': 'negative', 'category': 'NULL'}]\ntext: For appetizers , I recommend the shrimp fritters and dumplings .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nFor appetizers , I recommend the shrimp fritters and dumplings .\n->", + "output": "{\"text\": \"For appetizers , I recommend the shrimp fritters and dumplings .\", \"labels\": \"[{'aspect': 'shrimp fritters', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus has not responded to numerous request for an update of the status of repair .\n->asus has not responded to numerous request for an update of the status of repair .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n->certain apps ( especially flash based apps ) will get the machine very hot .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: Such nice people working here - but I have to review the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nSuch nice people working here - but I have to review the food .\n->", + "output": "{\"text\": \"Such nice people working here - but I have to review the food .\", \"labels\": \"[{'aspect': 'people', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they both pick up oils and such pretty easily .\n->they both pick up oils and such pretty easily .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food is outstanding and the service is quick , friendly and very professional .\n->the food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n->", + "output": "{\"text\": \"The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\", \"labels\": \"[{'aspect': 'spot lights', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They treated us well and the food was extremely fresh and well-prepared .\n->They treated us well and the food was extremely fresh and well-prepared .\n[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'well-prepared', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We were very surprised by how good the food was on our first visit here on a Sunday night .\n->We were very surprised by how good the food was on our first visit here on a Sunday night .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Great wine , great food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nGreat wine , great food .\n->", + "output": "{\"text\": \"Great wine , great food .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n->What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n[{'aspect': 'sichuan cooking', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chongqing hotpot', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: while their kitchen food is delicious , their sushi is out of this world .\n->while their kitchen food is delicious , their sushi is out of this world .\n[{'aspect': 'kitchen food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: The service was mediocre , and the lack of air conditioning made for a less than comfortable meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service was mediocre , and the lack of air conditioning made for a less than comfortable meal .\n->", + "output": "{\"text\": \"The service was mediocre , and the lack of air conditioning made for a less than comfortable meal .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'air conditioning', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'comfortable', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n->The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n[{'aspect': 'cuisine', 'opinion': 'different', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n->it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n[{'aspect': 'tablet', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: The food and staff always surprise me with the new heights they are taken to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe food and staff always surprise me with the new heights they are taken to .\n->", + "output": "{\"text\": \"The food and staff always surprise me with the new heights they are taken to .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n->The menu is very limited - i think we counted 4 or 5 entrees .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n->I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n[{'aspect': 'all you can eat deal', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\ntext: honestly the worst sushi my husband and i had in our entire lives .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhonestly the worst sushi my husband and i had in our entire lives .\n->", + "output": "{\"text\": \"honestly the worst sushi my husband and i had in our entire lives .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I got an excellent piece of cheesecake and we had several other nice pastries .\n->I got an excellent piece of cheesecake and we had several other nice pastries .\n[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nHowever , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n->", + "output": "{\"text\": \"However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': \"do n't mind\", 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n->i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n[{'aspect': 'keyboards', 'opinion': 'worst', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: the build quality is fantastic .\n->the build quality is fantastic .\n[{'aspect': 'build quality', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWhat makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n->", + "output": "{\"text\": \"What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\", \"labels\": \"[{'aspect': 'sichuan cooking', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chongqing hotpot', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve been to at cafe spice probably 5 - 8 times , it is probably still the best indian restaurant around union square .\n->i ' ve been to at cafe spice probably 5 - 8 times , it is probably still the best indian restaurant around union square .\n[{'aspect': 'cafe spice', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: If you want good authentic Thai this place is not the place to go .\n->If you want good authentic Thai this place is not the place to go .\n[{'aspect': 'Thai', 'opinion': 'good authentic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nInteresting other dishes for a change include chicken in curry sauce and salmon caserole .\n->", + "output": "{\"text\": \"Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon caserole', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love how fast it is , love that it can do everything i ' ve asked it to do so far in the two weeks i ' ve owned it , and i love how compact and easy it is to carry around .\n->love how fast it is , love that it can do everything i ' ve asked it to do so far in the two weeks i ' ve owned it , and i love how compact and easy it is to carry around .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: May , the owner always has a smile on her and will warmly greet you .\n->May , the owner always has a smile on her and will warmly greet you .\n[{'aspect': 'owner', 'opinion': 'warmly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: One of my favorites though was the Angry Lobster , a cold lobster salad that was magnificent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nOne of my favorites though was the Angry Lobster , a cold lobster salad that was magnificent .\n->", + "output": "{\"text\": \"One of my favorites though was the Angry Lobster , a cold lobster salad that was magnificent .\", \"labels\": \"[{'aspect': 'Angry Lobster', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cold lobster salad', 'opinion': 'magnificent', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They have some great entrees here as well .\n->They have some great entrees here as well .\n[{'aspect': 'entrees', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n->update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n[{'aspect': 'keyboard cover', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: Ask for Usha , the nicest bartender in manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAsk for Usha , the nicest bartender in manhattan .\n->", + "output": "{\"text\": \"Ask for Usha , the nicest bartender in manhattan .\", \"labels\": \"[{'aspect': 'bartender', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this place !\n->i love this place !\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The lox is always fresh too .\n->The lox is always fresh too .\n[{'aspect': 'lox', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: The service is outstanding and my crab-cake eggs benedict could not have been better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe service is outstanding and my crab-cake eggs benedict could not have been better .\n->", + "output": "{\"text\": \"The service is outstanding and my crab-cake eggs benedict could not have been better .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab-cake eggs benedict', 'opinion': 'could not have been better', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not impressed with the food .\n->not impressed with the food .\n[{'aspect': 'food', 'opinion': 'not impressed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: less than 90 days and the screen stopped working .\n->less than 90 days and the screen stopped working .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: The place 's decor and hidden bathrooms made for a good laugh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe place 's decor and hidden bathrooms made for a good laugh .\n->", + "output": "{\"text\": \"The place 's decor and hidden bathrooms made for a good laugh .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bathrooms', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall i ' m happy with it for what i use it for .\n->overall i ' m happy with it for what i use it for .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n->don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n[{'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'ugly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nIts location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->", + "output": "{\"text\": \"Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This dish is my favorite and I always get it when I go there and never get tired of it .\n->This dish is my favorite and I always get it when I go there and never get tired of it .\n[{'aspect': 'dish', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Also good for client lunch meetings , esp .\n->Also good for client lunch meetings , esp .\n[{'aspect': 'lunch meetings', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: Deliveries often take up to an hour and the prices are higher than most other pizzerias in the area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nDeliveries often take up to an hour and the prices are higher than most other pizzerias in the area .\n->", + "output": "{\"text\": \"Deliveries often take up to an hour and the prices are higher than most other pizzerias in the area .\", \"labels\": \"[{'aspect': 'prices', 'opinion': 'higher', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as other reviews have mentioned - its a bit heavier than expected for such a slim frame .\n->as other reviews have mentioned - its a bit heavier than expected for such a slim frame .\n[{'aspect': 'NULL', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Indoor was very cozy and cute .\n->Indoor was very cozy and cute .\n[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\ntext: Because of the delicate thin crust , take-out pies get soggy in their boxes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nBecause of the delicate thin crust , take-out pies get soggy in their boxes .\n->", + "output": "{\"text\": \"Because of the delicate thin crust , take-out pies get soggy in their boxes .\", \"labels\": \"[{'aspect': 'take-out pies', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'delicate', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Acceptable prices .\n->Acceptable prices .\n[{'aspect': 'prices', 'opinion': 'Acceptable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: overall , i am happy with it and would purchase it again .\n->overall , i am happy with it and would purchase it again .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: This place is always very crowded and popular .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThis place is always very crowded and popular .\n->", + "output": "{\"text\": \"This place is always very crowded and popular .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'crowded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'popular', 'polarity': 'positive', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n->I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n[{'aspect': 'quality of food', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The only weird thing was if we got a bottle , the waitress would have simply multiplied the glass price X4 , which makes no sense whatsoever .\n->The only weird thing was if we got a bottle , the waitress would have simply multiplied the glass price X4 , which makes no sense whatsoever .\n[{'aspect': 'waitress', 'opinion': 'weird', 'polarity': 'negative', 'category': 'NULL'}]\ntext: The selection changes frequently but the basic dishes are always available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nThe selection changes frequently but the basic dishes are always available .\n->", + "output": "{\"text\": \"The selection changes frequently but the basic dishes are always available .\", \"labels\": \"[{'aspect': 'selection', 'opinion': 'changes frequently', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'basic dishes', 'opinion': 'available', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they do a ton of online homework and this is perfect for them .\n->they do a ton of online homework and this is perfect for them .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: what really makes it shine is the food , which is aggressively seasoned with cyrpriot spices , and all made in - house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .\n->what really makes it shine is the food , which is aggressively seasoned with cyrpriot spices , and all made in - house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .\n[{'aspect': 'food', 'opinion': 'shine', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'gyro meat', 'opinion': 'in - house', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sausages', 'opinion': 'in - house', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'higher quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: I have to say that if this what makes it easier to get a saet a lunch -- I dont mind .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nI have to say that if this what makes it easier to get a saet a lunch -- I dont mind .\n->", + "output": "{\"text\": \"I have to say that if this what makes it easier to get a saet a lunch -- I dont mind .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'easier', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s already bricked and i did n ' t even use it for more than one day .\n->it ' s already bricked and i did n ' t even use it for more than one day .\n[{'aspect': 'NULL', 'opinion': 'bricked', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The place itself is beautiful the bar scene seems to be happening .\n->The place itself is beautiful the bar scene seems to be happening .\n[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar scene', 'opinion': 'happening', 'polarity': 'positive', 'category': 'NULL'}]\ntext: We were told that they were booked solid and no other table was available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nWe were told that they were booked solid and no other table was available .\n->", + "output": "{\"text\": \"We were told that they were booked solid and no other table was available .\", \"labels\": \"[{'aspect': 'table', 'opinion': 'available', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never swaying , never a bad meal , never bad service . . .\n->never swaying , never a bad meal , never bad service . . .\n[{'aspect': 'meal', 'opinion': 'never a bad', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'never bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: however it can happen at random , for example when signing into gmail .\n->however it can happen at random , for example when signing into gmail .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: After dealing with subpar pizza all over the Kensington neighborhood - I 've found little toninos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nAfter dealing with subpar pizza all over the Kensington neighborhood - I 've found little toninos .\n->", + "output": "{\"text\": \"After dealing with subpar pizza all over the Kensington neighborhood - I 've found little toninos .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n->The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n[{'aspect': 'service', 'opinion': 'busy', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: light weight , very convenient to use .\n->light weight , very convenient to use .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nEach table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n->", + "output": "{\"text\": \"Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\", \"labels\": \"[{'aspect': 'meats', 'opinion': 'thin', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'various', 'polarity': 'neutral', 'category': 'NULL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The owner truly caters to all your needs .\n->The owner truly caters to all your needs .\n[{'aspect': 'owner', 'opinion': 'caters', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: judging from previous posts this used to be a good place , but not any longer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njudging from previous posts this used to be a good place , but not any longer .\n->", + "output": "{\"text\": \"judging from previous posts this used to be a good place , but not any longer .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'not any longer', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is among the best .\n->this is among the best .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: eating in , the atmosphere saves it , but at your desk , it ' s a very disappointing experience .\n->eating in , the atmosphere saves it , but at your desk , it ' s a very disappointing experience .\n[{'aspect': 'atmosphere', 'opinion': 'saves', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: we , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->", + "output": "{\"text\": \"we , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n->still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n[{'aspect': 'measures of liquers', 'opinion': 'pour - your - own', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'measures of liquers', 'opinion': 'courtesey', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: only con would be that display is not that bright , although i would say at the brightest setting is probably where it should be .\n->only con would be that display is not that bright , although i would say at the brightest setting is probably where it should be .\n[{'aspect': 'display', 'opinion': 'con', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: they never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\n->", + "output": "{\"text\": \"they never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my 3rd chromebook and it is , by far , the flakiest one i ' ve had .\n->this is my 3rd chromebook and it is , by far , the flakiest one i ' ve had .\n[{'aspect': 'chromebook', 'opinion': 'flakiest', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i absolutely love this chromebook .\n->i absolutely love this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the food was lousy - too sweet or too salty and the portions tiny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was lousy - too sweet or too salty and the portions tiny .\n->", + "output": "{\"text\": \"the food was lousy - too sweet or too salty and the portions tiny .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'too salty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best pc bang for this level of buck .\n->best pc bang for this level of buck .\n[{'aspect': 'pc bang', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n->The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n[{'aspect': 'spot lights', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: after all that , they complained to me about the small tip .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter all that , they complained to me about the small tip .\n->", + "output": "{\"text\": \"after all that , they complained to me about the small tip .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'complained', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now a few weeks later monitor has died again .\n->now a few weeks later monitor has died again .\n[{'aspect': 'monitor', 'opinion': 'died', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: I recommend this place to everyone who asks me where to go for a good meal .\n->I recommend this place to everyone who asks me where to go for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: avoid this place !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \navoid this place !\n->", + "output": "{\"text\": \"avoid this place !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'avoid', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like the ease of connecting to the internet wi - fi .\n->i like the ease of connecting to the internet wi - fi .\n[{'aspect': 'wi - fi', 'opinion': 'ease', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: 11 months after the purchase , it died .\n->11 months after the purchase , it died .\n[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i have eaten at saul , many times , the food is always consistently , outrageously good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have eaten at saul , many times , the food is always consistently , outrageously good .\n->", + "output": "{\"text\": \"i have eaten at saul , many times , the food is always consistently , outrageously good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'outrageously good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish i could give it away at this point .\n->i wish i could give it away at this point .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: if you need a decent computer that runs quality this is it , especially if you are starting out .\n->if you need a decent computer that runs quality this is it , especially if you are starting out .\n[{'aspect': 'computer', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: saul is the best restaurant on smith street and in brooklyn .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsaul is the best restaurant on smith street and in brooklyn .\n->", + "output": "{\"text\": \"saul is the best restaurant on smith street and in brooklyn .\", \"labels\": \"[{'aspect': 'saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google is very concerned about arc to chromeos connections for security , etc etc .\n->google is very concerned about arc to chromeos connections for security , etc etc .\n[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n->overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n[{'aspect': 'computer', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the duck confit is always amazing and the foie gras terrine with figs was out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe duck confit is always amazing and the foie gras terrine with figs was out of this world .\n->", + "output": "{\"text\": \"the duck confit is always amazing and the foie gras terrine with figs was out of this world .\", \"labels\": \"[{'aspect': 'foie gras terrine with figs', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'duck confit', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the side dishes were passable , and i did get a refill upon request .\n->the side dishes were passable , and i did get a refill upon request .\n[{'aspect': 'side dishes', 'opinion': 'passable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this place is not inviting and the food is totally weird .\n->this place is not inviting and the food is totally weird .\n[{'aspect': 'place', 'opinion': 'not inviting', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the wine list is interesting and has many good values .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wine list is interesting and has many good values .\n->", + "output": "{\"text\": \"the wine list is interesting and has many good values .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine list', 'opinion': 'good values', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n->i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The music is the best among all the Indian restaurants I have visited .\n->The music is the best among all the Indian restaurants I have visited .\n[{'aspect': 'music', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: for the price , you can not eat this well in manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the price , you can not eat this well in manhattan .\n->", + "output": "{\"text\": \"for the price , you can not eat this well in manhattan .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the jukebox plays everything from italian opera to the strokes .\n->the jukebox plays everything from italian opera to the strokes .\n[{'aspect': 'jukebox', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Nice ambiance , nice little bar , good bartender , Francois , and good service .\n->Nice ambiance , nice little bar , good bartender , Francois , and good service .\n[{'aspect': 'bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bartender', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i was very disappointed with this restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was very disappointed with this restaurant .\n->", + "output": "{\"text\": \"i was very disappointed with this restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sauces used are also not that exciting .\n->The sauces used are also not that exciting .\n[{'aspect': 'sauces', 'opinion': 'not that exciting', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n->i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n[{'aspect': 'waiter', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .\n->", + "output": "{\"text\": \"ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .\", \"labels\": \"[{'aspect': 'cart attendant', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n->the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the keyboard is clicky , has decent amount of travel , and is backlit .\n->the keyboard is clicky , has decent amount of travel , and is backlit .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i had to ask her three times before she finally came back with the dish ive requested .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had to ask her three times before she finally came back with the dish ive requested .\n->", + "output": "{\"text\": \"i had to ask her three times before she finally came back with the dish ive requested .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: please be aware that it ' s cash or amex only !\n->please be aware that it ' s cash or amex only !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: superb quality , looks and feels like apple , keyboard is great , the touchpad is flawless and the screen is brilliant , battery life is great too .\n->superb quality , looks and feels like apple , keyboard is great , the touchpad is flawless and the screen is brilliant , battery life is great too .\n[{'aspect': 'quality', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'flawless', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: food was okay , nothing great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was okay , nothing great .\n->", + "output": "{\"text\": \"food was okay , nothing great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place does n ' t make any sense\n->this place does n ' t make any sense\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we had a very nice time .\n->we had a very nice time .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n->", + "output": "{\"text\": \"chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\", \"labels\": \"[{'aspect': 'chow fun', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork shu mai', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'loud', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n->i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: ordered this computer to use in college and also for gaming .\n->ordered this computer to use in college and also for gaming .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: i / we will never go back to this place again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni / we will never go back to this place again .\n->", + "output": "{\"text\": \"i / we will never go back to this place again .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'never go back', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is fantastic for the things that i need a computer for .\n->it is fantastic for the things that i need a computer for .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I am amazed by the poor reviews- I find this place to be standout Italian in an area flooded with Italian- great prices , great atmosphere , good service and a wonderful wine list .\n->I am amazed by the poor reviews- I find this place to be standout Italian in an area flooded with Italian- great prices , great atmosphere , good service and a wonderful wine list .\n[{'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: went on a 3 day oyster binge , with fish bringing up the closing , and i am so glad this was the place it o trip ended , because it was so great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwent on a 3 day oyster binge , with fish bringing up the closing , and i am so glad this was the place it o trip ended , because it was so great !\n->", + "output": "{\"text\": \"went on a 3 day oyster binge , with fish bringing up the closing , and i am so glad this was the place it o trip ended , because it was so great !\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ca n ' t go wrong here .\n->you ca n ' t go wrong here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: gpu wasn ' t drawing a lot of power because i was playing world of warcraft on the recommended settings .\n->gpu wasn ' t drawing a lot of power because i was playing world of warcraft on the recommended settings .\n[{'aspect': 'gpu', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: service was devine , oysters where a sensual as they come , and the price ca n ' t be beat ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was devine , oysters where a sensual as they come , and the price ca n ' t be beat ! ! !\n->", + "output": "{\"text\": \"service was devine , oysters where a sensual as they come , and the price ca n ' t be beat ! ! !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice build quality , very fast and beautiful display .\n->nice build quality , very fast and beautiful display .\n[{'aspect': 'build quality', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'display', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: Service was good and food is wonderful .\n->Service was good and food is wonderful .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you ca n ' t go wrong here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou ca n ' t go wrong here .\n->", + "output": "{\"text\": \"you ca n ' t go wrong here .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is so much fun .\n->This place is so much fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n->If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n[{'aspect': 'nori', 'opinion': 'not-so-fresh', 'polarity': 'negative', 'category': 'NULL'}]\ntext: every time in new york i make it a point to visit restaurant saul on smith street .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nevery time in new york i make it a point to visit restaurant saul on smith street .\n->", + "output": "{\"text\": \"every time in new york i make it a point to visit restaurant saul on smith street .\", \"labels\": \"[{'aspect': 'restaurant saul', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A cool place to hang with your friends for a couple of healthy drinks and desserts .\n->A cool place to hang with your friends for a couple of healthy drinks and desserts .\n[{'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve waited over one hour for food .\n->i ' ve waited over one hour for food .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything is always cooked to perfection , the service is excellent , the decor cool and understated .\n->", + "output": "{\"text\": \"everything is always cooked to perfection , the service is excellent , the decor cool and understated .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'understated', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n->At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n[{'aspect': 'broth with noodles', 'opinion': 'mild', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the power _ supply is awesome .\n->the power _ supply is awesome .\n[{'aspect': 'power _ supply is', 'opinion': '.', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: i had the duck breast special on my last visit and it was incredible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had the duck breast special on my last visit and it was incredible .\n->", + "output": "{\"text\": \"i had the duck breast special on my last visit and it was incredible .\", \"labels\": \"[{'aspect': 'duck breast special', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great cheap tool for web development ( using linux ) and everyday internet usage .\n->great cheap tool for web development ( using linux ) and everyday internet usage .\n[{'aspect': 'tool', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Average to good Thai food , but terrible delivery .\n->Average to good Thai food , but terrible delivery .\n[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: and i hate to say this but i doubt i ' ll ever go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand i hate to say this but i doubt i ' ll ever go back .\n->", + "output": "{\"text\": \"and i hate to say this but i doubt i ' ll ever go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'doubt', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fantastic computer .\n->fantastic computer .\n[{'aspect': 'computer', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we both opted for a pasta dish and they were served timely and fresh .\n->we both opted for a pasta dish and they were served timely and fresh .\n[{'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the food is very average . . . the thai fusion stuff is a bit too sweet , every thing they serve is too sweet here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is very average . . . the thai fusion stuff is a bit too sweet , every thing they serve is too sweet here .\n->", + "output": "{\"text\": \"the food is very average . . . the thai fusion stuff is a bit too sweet , every thing they serve is too sweet here .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai fusion stuff', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n->the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n[{'aspect': 'NULL', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n->and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n[{'aspect': 'system', 'opinion': 'not worry', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: the only thing i moderately enjoyed was their grilled chicken special with edamame puree .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing i moderately enjoyed was their grilled chicken special with edamame puree .\n->", + "output": "{\"text\": \"the only thing i moderately enjoyed was their grilled chicken special with edamame puree .\", \"labels\": \"[{'aspect': 'grilled chicken special with edamame puree', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i went with the asus vivobook based on the good specs , better hdd and ram , reviews , and because i could pay for it over several months .\n->i went with the asus vivobook based on the good specs , better hdd and ram , reviews , and because i could pay for it over several months .\n[{'aspect': 'asus vivobook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'hdd', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'ram', 'opinion': 'better', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n->My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n[{'aspect': 'dinner', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i had never had edamame pureed before but i thought it was innovative and tasty ( could ' ve used a bit more salt ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had never had edamame pureed before but i thought it was innovative and tasty ( could ' ve used a bit more salt ) .\n->", + "output": "{\"text\": \"i had never had edamame pureed before but i thought it was innovative and tasty ( could ' ve used a bit more salt ) .\", \"labels\": \"[{'aspect': 'edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try it !\n->try it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: : p ) , and the machine is definitely zippy .\n->: p ) , and the machine is definitely zippy .\n[{'aspect': 'machine', 'opinion': 'zippy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the decor is night tho . . . but they really need to clean that vent in the ceiling . . . its quite un - appetizing , and kills your effort to make this place look sleek and modern .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe decor is night tho . . . but they really need to clean that vent in the ceiling . . . its quite un - appetizing , and kills your effort to make this place look sleek and modern .\n->", + "output": "{\"text\": \"the decor is night tho . . . but they really need to clean that vent in the ceiling . . . its quite un - appetizing , and kills your effort to make this place look sleek and modern .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'sleek', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'modern', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'night', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'vent', 'opinion': 'un - appetizing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stable , long battery life , and great build .\n->stable , long battery life , and great build .\n[{'aspect': 'battery life', 'opinion': 'stable', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n->", + "output": "{\"text\": \"we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s complementary , not revolutionary , which is much more intuitive and useful .\n->it ' s complementary , not revolutionary , which is much more intuitive and useful .\n[{'aspect': 'NULL', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: This big draw is the all you can sushi here for $ 19.95 !\n->This big draw is the all you can sushi here for $ 19.95 !\n[{'aspect': 'sushi', 'opinion': 'draw', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the perfect spot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe perfect spot .\n->", + "output": "{\"text\": \"the perfect spot .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the keyboard is great and has a nice gentle / quiet click to it .\n->- the keyboard is great and has a nice gentle / quiet click to it .\n[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: i wish i could be refunded !\n->i wish i could be refunded !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: food - awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood - awesome .\n->", + "output": "{\"text\": \"food - awesome .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen display isn ' t bright at all !\n->the screen display isn ' t bright at all !\n[{'aspect': 'screen display', 'opinion': \"' t bright at all\", 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i would definitely recommend this laptop to anyone who is looking for a cheap and nice computer .\n->i would definitely recommend this laptop to anyone who is looking for a cheap and nice computer .\n[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: service - friendly and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice - friendly and attentive .\n->", + "output": "{\"text\": \"service - friendly and attentive .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall i love this machine , and all my computers will probably be chromebooks in the future .\n->overall i love this machine , and all my computers will probably be chromebooks in the future .\n[{'aspect': 'machine', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: only used it a short while , but it seems like a sturdy nice little laptop .\n->only used it a short while , but it seems like a sturdy nice little laptop .\n[{'aspect': 'laptop', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: ambiance - relaxed and stylish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nambiance - relaxed and stylish .\n->", + "output": "{\"text\": \"ambiance - relaxed and stylish .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is horrible though .\n->battery life is horrible though .\n[{'aspect': 'battery life', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food was exceptional .\n->The food was exceptional .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: do n ' t judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo n ' t judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\n->", + "output": "{\"text\": \"do n ' t judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n->if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n->i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: the food is decent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is decent .\n->", + "output": "{\"text\": \"the food is decent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: highly recommended .\n->highly recommended .\n[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i can not imagine you not rushing out to eat there .\n->i can not imagine you not rushing out to eat there .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: however , it ' s the service that leaves a bad taste in my mouth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , it ' s the service that leaves a bad taste in my mouth .\n->", + "output": "{\"text\": \"however , it ' s the service that leaves a bad taste in my mouth .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is so easy to get a reservation at a top place in NYC with a week 's notice .\n->It is so easy to get a reservation at a top place in NYC with a week 's notice .\n[{'aspect': 'reservation', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this small astoria souvlaki spot makes what many consider the best gyros in new york .\n->this small astoria souvlaki spot makes what many consider the best gyros in new york .\n[{'aspect': 'gyros', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\n->", + "output": "{\"text\": \"i happen to have a policy that goes along with a little bit of self - respect , which includes not letting a waiter intimidate me , i . e . make me feel bad asking for trivialities like water , or the check .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our family never expected such incredible entertainment in a restaurant .\n->Our family never expected such incredible entertainment in a restaurant .\n[{'aspect': 'entertainment', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen wasn ' t as clear or bright as i hoped .\n->the screen wasn ' t as clear or bright as i hoped .\n[{'aspect': 'screen', 'opinion': \"' t as clear or bright\", 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: i know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i ' m here to eat and enjoy the company of friends , seeking a pleasant experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i ' m here to eat and enjoy the company of friends , seeking a pleasant experience .\n->", + "output": "{\"text\": \"i know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i ' m here to eat and enjoy the company of friends , seeking a pleasant experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Highly recommend this as great value for excellent sushi and service .\n->Highly recommend this as great value for excellent sushi and service .\n[{'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i will return it .\n->i will return it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwell , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n->", + "output": "{\"text\": \"well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: over the years the host , vittorio , and his crew , have always treated me as family - - although with all the business this not - so - little gem does , it amazing he ' s even able to remember a consistent but not - so - frequent visitor .\n->over the years the host , vittorio , and his crew , have always treated me as family - - although with all the business this not - so - little gem does , it amazing he ' s even able to remember a consistent but not - so - frequent visitor .\n[{'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Finally a reliable Chinese restaurant !\n->Finally a reliable Chinese restaurant !\n[{'aspect': 'Chinese restaurant', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the last time i walked by it looked pretty empty . hmmm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe last time i walked by it looked pretty empty . hmmm .\n->", + "output": "{\"text\": \"the last time i walked by it looked pretty empty . hmmm .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'empty', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: don ' t recommend it .\n->don ' t recommend it .\n[{'aspect': 'NULL', 'opinion': \"' t recommend\", 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is a great place to take out - of - towners , and perfect for watching the sunset .\n->this is a great place to take out - of - towners , and perfect for watching the sunset .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: i had a great experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had a great experience .\n->", + "output": "{\"text\": \"i had a great experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is amazing , rich pastas and fresh doughy pizza .\n->The food is amazing , rich pastas and fresh doughy pizza .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastas', 'opinion': 'rich', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'fresh doughy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: go here .\n->go here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood is great .\n->", + "output": "{\"text\": \"food is great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this item 7 months ago and i love it .\n->i purchased this item 7 months ago and i love it .\n[{'aspect': 'item', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i really loved the different and inovated touch that ' s the cheff gives to the food .\n->i really loved the different and inovated touch that ' s the cheff gives to the food .\n[{'aspect': 'cheff', 'opinion': 'loved', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheff', 'opinion': 'inovated', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: service is top notch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is top notch .\n->", + "output": "{\"text\": \"service is top notch .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good looking laptop but hardware has several major problems .\n->good looking laptop but hardware has several major problems .\n[{'aspect': 'laptop', 'opinion': 'good looking', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'hardware', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: the keyboard and os takes some getting used to .\n->the keyboard and os takes some getting used to .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\ntext: i have been going back again and again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been going back again and again .\n->", + "output": "{\"text\": \"i have been going back again and again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My friend got the mushroom pizza which tasted better .\n->My friend got the mushroom pizza which tasted better .\n[{'aspect': 'mushroom pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 2 ssd as it will not fit the slot available .\n->2 ssd as it will not fit the slot available .\n[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: it melted in my little mouth and the perfect consistency - not too fishy , creamy , and slightly buttery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit melted in my little mouth and the perfect consistency - not too fishy , creamy , and slightly buttery .\n->", + "output": "{\"text\": \"it melted in my little mouth and the perfect consistency - not too fishy , creamy , and slightly buttery .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect consistency', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: give it a try and enjoy .\n->give it a try and enjoy .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n->if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: the sushi seemed pretty fresh and was adequately proportioned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sushi seemed pretty fresh and was adequately proportioned .\n->", + "output": "{\"text\": \"the sushi seemed pretty fresh and was adequately proportioned .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'proportioned', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is awesome - definitely try the striped bass .\n->The food is awesome - definitely try the striped bass .\n[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'striped bass', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: excellent lapto , just as they show it .\n->excellent lapto , just as they show it .\n[{'aspect': 'lapto', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the rice to fish ration was also good - - they did n ' t try to overpack the rice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe rice to fish ration was also good - - they did n ' t try to overpack the rice .\n->", + "output": "{\"text\": \"the rice to fish ration was also good - - they did n ' t try to overpack the rice .\", \"labels\": \"[{'aspect': 'rice to fish ration', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do n ' t miss bloom ' s on your next trip to manhatten .\n->do n ' t miss bloom ' s on your next trip to manhatten .\n[{'aspect': \"bloom ' s\", 'opinion': \"n ' t miss\", 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i don ' t really have anywhere to rest my hand when i use it because it ' s so large .\n->i don ' t really have anywhere to rest my hand when i use it because it ' s so large .\n[{'aspect': 'NULL', 'opinion': 'large', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: we took advanatage of the half price sushi deal on saturday so it was well worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe took advanatage of the half price sushi deal on saturday so it was well worth it .\n->", + "output": "{\"text\": \"we took advanatage of the half price sushi deal on saturday so it was well worth it .\", \"labels\": \"[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is an amazing place to try some roti rolls .\n->this is an amazing place to try some roti rolls .\n[{'aspect': 'roti rolls', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n->The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: surprisingly nothing could be further from the truth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsurprisingly nothing could be further from the truth .\n->", + "output": "{\"text\": \"surprisingly nothing could be further from the truth .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'surprisingly', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n->its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n[{'aspect': 'laptop', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: If you are in search of the most authentic NYC deli experience look no further than the famous and historic Katz 's Deli down on the Lower East Side .\n->If you are in search of the most authentic NYC deli experience look no further than the famous and historic Katz 's Deli down on the Lower East Side .\n[{'aspect': 'deli', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: in the evening , this place attracted a well dressed , with it , ny crowd .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin the evening , this place attracted a well dressed , with it , ny crowd .\n->", + "output": "{\"text\": \"in the evening , this place attracted a well dressed , with it , ny crowd .\", \"labels\": \"[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n->From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n[{'aspect': 'food', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the build quality is good ( all aluminum body ) .\n->the build quality is good ( all aluminum body ) .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: the food was well prepared and the service impecable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was well prepared and the service impecable .\n->", + "output": "{\"text\": \"the food was well prepared and the service impecable .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'impecable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i found the food to be outstanding , particulary the salmon dish i had .\n->i found the food to be outstanding , particulary the salmon dish i had .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon dish', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i was supposed to use this to write !\n->i was supposed to use this to write !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' m going back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m going back .\n->", + "output": "{\"text\": \"i ' m going back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i knocked off a star for build quality control .\n->i knocked off a star for build quality control .\n[{'aspect': 'build quality control', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The noise level was unbearable , conversation impossible .\n->The noise level was unbearable , conversation impossible .\n[{'aspect': 'noise level', 'opinion': 'unbearable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nquite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n->", + "output": "{\"text\": \"quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'tranquility', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the c302 is a great machine .\n->the c302 is a great machine .\n[{'aspect': 'c302', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: ravioli was good . . . but i have to say that i found everything a bit overpriced .\n->ravioli was good . . . but i have to say that i found everything a bit overpriced .\n[{'aspect': 'ravioli', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: i will be going back and heartily recommend it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will be going back and heartily recommend it !\n->", + "output": "{\"text\": \"i will be going back and heartily recommend it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but they didn ' t make it $ 300 by wasting money , there are compromises .\n->but they didn ' t make it $ 300 by wasting money , there are compromises .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i got this for my mother in law and she could not be happier with how it works .\n->i got this for my mother in law and she could not be happier with how it works .\n[{'aspect': 'NULL', 'opinion': 'not be happier', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it is terrific , as is the value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is terrific , as is the value .\n->", + "output": "{\"text\": \"it is terrific , as is the value .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is excellent .\n->The wine list is excellent .\n[{'aspect': 'wine list', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it does have an ips screen , battery life is going strong , and no touch pad issues .\n->it does have an ips screen , battery life is going strong , and no touch pad issues .\n[{'aspect': 'ips screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'strong', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n$ 6 and there is much tasty food , all of it fresh and continually refilled .\n->", + "output": "{\"text\": \"$ 6 and there is much tasty food , all of it fresh and continually refilled .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i love it at first , but today my laptop wasn ' t not charging any more .\n->i love it at first , but today my laptop wasn ' t not charging any more .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i am not a vegetarian but , almost all the dishes were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am not a vegetarian but , almost all the dishes were great .\n->", + "output": "{\"text\": \"i am not a vegetarian but , almost all the dishes were great .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was there for brunch recently , and we were tag teamed by a waitress and a waiter .\n->i was there for brunch recently , and we were tag teamed by a waitress and a waiter .\n[{'aspect': 'waitress', 'opinion': 'teamed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'teamed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n->lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n[{'aspect': 'NULL', 'opinion': 'rudeness', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: go hungry and enjoy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngo hungry and enjoy .\n->", + "output": "{\"text\": \"go hungry and enjoy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but this one was a piece of trash .\n->but this one was a piece of trash .\n[{'aspect': 'trash', 'opinion': 'trash', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n->as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n[{'aspect': 'chromebook device', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\n->", + "output": "{\"text\": \"i like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\", \"labels\": \"[{'aspect': 'somosas', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chai', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chole', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dhosas', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dhal', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but , when software compatibility began stacking up , it became a nogo for me .\n->but , when software compatibility began stacking up , it became a nogo for me .\n[{'aspect': 'software', 'opinion': 'nogo', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: apps start very fast , graphics are much more responsive and capable when not being shared with chromeos and there are a number of ways you can tweak the ui / ux to your own liking .\n->apps start very fast , graphics are much more responsive and capable when not being shared with chromeos and there are a number of ways you can tweak the ui / ux to your own liking .\n[{'aspect': 'graphics', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'GRAPHICS#USABILITY'}, {'aspect': 'graphics', 'opinion': 'capable', 'polarity': 'positive', 'category': 'GRAPHICS#USABILITY'}]\ntext: the service varys from day to day - sometimes they ' re very nice , and sometimes not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service varys from day to day - sometimes they ' re very nice , and sometimes not .\n->", + "output": "{\"text\": \"the service varys from day to day - sometimes they ' re very nice , and sometimes not .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'varys', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n->that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n[{'aspect': 'bathroom', 'opinion': 'needs to be cleaned', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the kitchen however , is almost always slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe kitchen however , is almost always slow .\n->", + "output": "{\"text\": \"the kitchen however , is almost always slow .\", \"labels\": \"[{'aspect': 'kitchen', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: greatest thing i ' ve bought myself in a long time .\n->greatest thing i ' ve bought myself in a long time .\n[{'aspect': 'NULL', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n->slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\ntext: if you ' ve ever been along the river in weehawken you have an idea of the top of view the chart house has to offer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' ve ever been along the river in weehawken you have an idea of the top of view the chart house has to offer .\n->", + "output": "{\"text\": \"if you ' ve ever been along the river in weehawken you have an idea of the top of view the chart house has to offer .\", \"labels\": \"[{'aspect': 'view', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n->i loved this chromebook but i had to return it bevause it had sound issues .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: when i put it into tablet mode , everything is great .\n->when i put it into tablet mode , everything is great .\n[{'aspect': 'tablet mode', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nadd to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n->", + "output": "{\"text\": \"add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has non existent boot times and updates are easy .\n->it has non existent boot times and updates are easy .\n[{'aspect': 'boot times', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'updates', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\nExample:\ntext: screen not aligned perfectly .\n->screen not aligned perfectly .\n[{'aspect': 'screen', 'opinion': 'not aligned perfectly', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the lava cake dessert was incredible and i recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe lava cake dessert was incredible and i recommend it .\n->", + "output": "{\"text\": \"the lava cake dessert was incredible and i recommend it .\", \"labels\": \"[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook was one of the best gift to my grandauther .\n->this chromebook was one of the best gift to my grandauther .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonce you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n->", + "output": "{\"text\": \"once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\", \"labels\": \"[{'aspect': 'cosette', 'opinion': 'off - the - beaten', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n->downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n[{'aspect': 'downloading android apps', 'opinion': 'easy', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this tiny restaurant is as cozy as it gets , with that certain parisian flair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis tiny restaurant is as cozy as it gets , with that certain parisian flair .\n->", + "output": "{\"text\": \"this tiny restaurant is as cozy as it gets , with that certain parisian flair .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n->Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: bad battery , speaker and touchpad\n->bad battery , speaker and touchpad\n[{'aspect': 'battery', 'opinion': 'bad', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'speaker', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: the food was average to above - average ; the french onion soup filling yet not overly impressive , and the desserts not brilliant in any way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was average to above - average ; the french onion soup filling yet not overly impressive , and the desserts not brilliant in any way .\n->", + "output": "{\"text\": \"the food was average to above - average ; the french onion soup filling yet not overly impressive , and the desserts not brilliant in any way .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'average to above - average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'french onion soup', 'opinion': 'not overly impressive', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'desserts', 'opinion': 'not brilliant', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was a great surprise .\n->this was a great surprise .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n->it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: however , go for the ambience , and consider the food just a companion for a trip across the world !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , go for the ambience , and consider the food just a companion for a trip across the world !\n->", + "output": "{\"text\": \"however , go for the ambience , and consider the food just a companion for a trip across the world !\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the lava cake dessert was incredible and i recommend it .\n->the lava cake dessert was incredible and i recommend it .\n[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: cirspy crust margherita pizza\n->cirspy crust margherita pizza\n[{'aspect': 'margherita pizza', 'opinion': 'cirspy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'crust', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pizza was delivered cold and the cheese was n ' t even fully melted !\n->", + "output": "{\"text\": \"the pizza was delivered cold and the cheese was n ' t even fully melted !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , I think Jeckll and Hydes t is one of those places that is fun to do once .\n->However , I think Jeckll and Hydes t is one of those places that is fun to do once .\n[{'aspect': 'Jeckll and Hydes', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: pizza - the only pizza in nyc that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->pizza - the only pizza in nyc that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'pizza', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'freshly baked', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it looked like shredded cheese partly done - still in strips .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit looked like shredded cheese partly done - still in strips .\n->", + "output": "{\"text\": \"it looked like shredded cheese partly done - still in strips .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good computer good memory\n->good computer good memory\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: the macbook was delivered soon and it is exactly as described\n->the macbook was delivered soon and it is exactly as described\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this has got to be one of the most overrated restaurants in brooklyn .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis has got to be one of the most overrated restaurants in brooklyn .\n->", + "output": "{\"text\": \"this has got to be one of the most overrated restaurants in brooklyn .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even though its good seafood , the prices are too high .\n->Even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Average to good Thai food , but terrible delivery .\n->Average to good Thai food , but terrible delivery .\n[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the pizza is overpriced and soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pizza is overpriced and soggy .\n->", + "output": "{\"text\": \"the pizza is overpriced and soggy .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great for a romantic evening , or a fun evening with friends . . .\n->great for a romantic evening , or a fun evening with friends . . .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i thought this place was totally overrated .\n->i thought this place was totally overrated .\n[{'aspect': 'place', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: yes , they use fancy ingredients , but even fancy ingredients do n ' t make for good pizza unless someone knows how to get the crust right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyes , they use fancy ingredients , but even fancy ingredients do n ' t make for good pizza unless someone knows how to get the crust right .\n->", + "output": "{\"text\": \"yes , they use fancy ingredients , but even fancy ingredients do n ' t make for good pizza unless someone knows how to get the crust right .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The buffet had a nice selection .\n->The buffet had a nice selection .\n[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: * the screen is more than adequate for me , although i have not used it outside much yet .\n->* the screen is more than adequate for me , although i have not used it outside much yet .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: a big disappointment , all around .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na big disappointment , all around .\n->", + "output": "{\"text\": \"a big disappointment , all around .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was bland oily .\n->The food was bland oily .\n[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it hits the spot every time\n->it hits the spot every time\n[{'aspect': 'NULL', 'opinion': 'hits', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i think i ' ve had some the best meals of my life at minnow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think i ' ve had some the best meals of my life at minnow .\n->", + "output": "{\"text\": \"i think i ' ve had some the best meals of my life at minnow .\", \"labels\": \"[{'aspect': 'meals', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great hot dogs !\n->great hot dogs !\n[{'aspect': 'hot dogs', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: love their drink menu .\n->love their drink menu .\n[{'aspect': 'drink menu', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: the seafood is amazing , there ' s a good wine list , and the ever - changing menu always offers some great surprises .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe seafood is amazing , there ' s a good wine list , and the ever - changing menu always offers some great surprises .\n->", + "output": "{\"text\": \"the seafood is amazing , there ' s a good wine list , and the ever - changing menu always offers some great surprises .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wine list', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'menu', 'opinion': 'ever - changing', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'menu', 'opinion': 'great surprises', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: possibly the most romantic restaurant in the city\n->possibly the most romantic restaurant in the city\n[{'aspect': 'restaurant', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n->it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the combination of super - fresh ingredients in the dishes are unusual but really delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe combination of super - fresh ingredients in the dishes are unusual but really delicious .\n->", + "output": "{\"text\": \"the combination of super - fresh ingredients in the dishes are unusual but really delicious .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'super - fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'unusual', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i haven ' t had issues with the track pad as others have .\n->i haven ' t had issues with the track pad as others have .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: i ' ve been using it for almost three weeks now and it has not let me down .\n->i ' ve been using it for almost three weeks now and it has not let me down .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: worth the trip from manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworth the trip from manhattan .\n->", + "output": "{\"text\": \"worth the trip from manhattan .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t have need for the backlit keyboard .\n->i don ' t have need for the backlit keyboard .\n[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: another problem i had was when i awakened my computer from sleeping , the wifi would not work .\n->another problem i had was when i awakened my computer from sleeping , the wifi would not work .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: best pastrami i ever had and great portion without being ridiculous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest pastrami i ever had and great portion without being ridiculous .\n->", + "output": "{\"text\": \"best pastrami i ever had and great portion without being ridiculous .\", \"labels\": \"[{'aspect': 'pastrami', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i lived upstate for a while i would buy freeze the bagels and they would still be better than any else .\n->when i lived upstate for a while i would buy freeze the bagels and they would still be better than any else .\n[{'aspect': 'bagels', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n->i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'boot speed', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'OS#GENERAL'}, {'aspect': 'cooling fan', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\ntext: my wife had the fried shrimp which are huge and loved it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy wife had the fried shrimp which are huge and loved it .\n->", + "output": "{\"text\": \"my wife had the fried shrimp which are huge and loved it .\", \"labels\": \"[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The crust is thin , the ingredients are fresh and the staff is friendly .\n->The crust is thin , the ingredients are fresh and the staff is friendly .\n[{'aspect': 'crust', 'opinion': 'thin', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: backlit keyboard is great ; feels sturdy ; fast processing .\n->backlit keyboard is great ; feels sturdy ; fast processing .\n[{'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n->", + "output": "{\"text\": \"as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is not a new unit .\n->this is not a new unit .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it starts up fast .\n->it starts up fast .\n[{'aspect': 'starts up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: this place is the most japanese it can ever get .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is the most japanese it can ever get .\n->", + "output": "{\"text\": \"this place is the most japanese it can ever get .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'japanese', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' ve ever been along the river in weehawken you have an idea of the top of view the chart house has to offer .\n->if you ' ve ever been along the river in weehawken you have an idea of the top of view the chart house has to offer .\n[{'aspect': 'view', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\nExample:\ntext: keeps disconnecting from my wifi at work .\n->keeps disconnecting from my wifi at work .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: the signs , the specials menus , food , and even all the waitstaff are all totally japanese .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe signs , the specials menus , food , and even all the waitstaff are all totally japanese .\n->", + "output": "{\"text\": \"the signs , the specials menus , food , and even all the waitstaff are all totally japanese .\", \"labels\": \"[{'aspect': 'signs', 'opinion': 'japanese', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'specials menus', 'opinion': 'japanese', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'japanese', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'waitstaff', 'opinion': 'japanese', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only weird thing was if we got a bottle , the waitress would have simply multiplied the glass price X4 , which makes no sense whatsoever .\n->The only weird thing was if we got a bottle , the waitress would have simply multiplied the glass price X4 , which makes no sense whatsoever .\n[{'aspect': 'waitress', 'opinion': 'weird', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the food was pretty tradional but it was hot and good with large portions .\n->the food was pretty tradional but it was hot and good with large portions .\n[{'aspect': 'food', 'opinion': 'tradional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'hot', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: this place is worth an one - hour drive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is worth an one - hour drive .\n->", + "output": "{\"text\": \"this place is worth an one - hour drive .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n->i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'software', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i am so coming back here again , as much as i can .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am so coming back here again , as much as i can .\n->", + "output": "{\"text\": \"i am so coming back here again , as much as i can .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's not mind-blowing , but to me , thai food never is and never will be .\n->It 's not mind-blowing , but to me , thai food never is and never will be .\n[{'aspect': 'thai food', 'opinion': 'mind-blowing', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: give it a try and enjoy .\n->give it a try and enjoy .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: leon is an east village gem : casual but hip , with well prepared basic french bistro fare , good specials , a warm and lively atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nleon is an east village gem : casual but hip , with well prepared basic french bistro fare , good specials , a warm and lively atmosphere .\n->", + "output": "{\"text\": \"leon is an east village gem : casual but hip , with well prepared basic french bistro fare , good specials , a warm and lively atmosphere .\", \"labels\": \"[{'aspect': 'leon', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'leon', 'opinion': 'hip', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'lively', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'french bistro fare', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s delicious !\n->it ' s delicious !\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i thought this place was totally overrated .\n->i thought this place was totally overrated .\n[{'aspect': 'place', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: my wife and i always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy wife and i always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n->", + "output": "{\"text\": \"my wife and i always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was good , the place was clean and affordable .\n->the food was good , the place was clean and affordable .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: i got this for my mother in law and she could not be happier with how it works .\n->i got this for my mother in law and she could not be happier with how it works .\n[{'aspect': 'NULL', 'opinion': 'not be happier', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: decent wine at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndecent wine at reasonable prices .\n->", + "output": "{\"text\": \"decent wine at reasonable prices .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is authentic Italian - delicious !\n->The food is authentic Italian - delicious !\n[{'aspect': 'food', 'opinion': 'authentic Italian', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: microphone is really low .\n->microphone is really low .\n[{'aspect': 'microphone', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: our teenage kids love it , too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour teenage kids love it , too .\n->", + "output": "{\"text\": \"our teenage kids love it , too .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re looking for a good chromebook , this is the one for you .\n->if you ' re looking for a good chromebook , this is the one for you .\n[{'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it works really well with my art programs and runs a lot better !\n->it works really well with my art programs and runs a lot better !\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n->", + "output": "{\"text\": \"everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\", \"labels\": \"[{'aspect': 'zucchero pomodori', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but this one was a piece of trash .\n->but this one was a piece of trash .\n[{'aspect': 'trash', 'opinion': 'trash', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' m partial to the gnocchi .\n->i ' m partial to the gnocchi .\n[{'aspect': 'gnocchi', 'opinion': 'partial', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: this is by far my favorite place in the neighborhood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is by far my favorite place in the neighborhood .\n->", + "output": "{\"text\": \"this is by far my favorite place in the neighborhood .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n->today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n->the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n[{'aspect': 'touch screen', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the service is excellent , the decor is great , and the food is delicious and comes in large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->", + "output": "{\"text\": \"the service is excellent , the decor is great , and the food is delicious and comes in large portions .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was average or above including some surprising tasty dishes .\n->The food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i could not be happier with this computer because i am not the best person with technology .\n->i could not be happier with this computer because i am not the best person with technology .\n[{'aspect': 'computer', 'opinion': 'happier', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: i ' m partial to the gnocchi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m partial to the gnocchi .\n->", + "output": "{\"text\": \"i ' m partial to the gnocchi .\", \"labels\": \"[{'aspect': 'gnocchi', 'opinion': 'partial', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n->to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Sauce was watery and the food did n't have much flavor .\n->Sauce was watery and the food did n't have much flavor .\n[{'aspect': 'Sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this place is incredibly tiny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is incredibly tiny .\n->", + "output": "{\"text\": \"this place is incredibly tiny .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n->the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my husband said he could ' ve eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n->my husband said he could ' ve eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n[{'aspect': 'portion', 'opinion': 'fine', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'french fries', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: they refuse to seat parties of 3 or more on weekends .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey refuse to seat parties of 3 or more on weekends .\n->", + "output": "{\"text\": \"they refuse to seat parties of 3 or more on weekends .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'refuse', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: go hungry and enjoy .\n->go hungry and enjoy .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i also ordered the change mojito , which was out of this world .\n->i also ordered the change mojito , which was out of this world .\n[{'aspect': 'change mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: the hostess is rude to the point of being offensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hostess is rude to the point of being offensive .\n->", + "output": "{\"text\": \"the hostess is rude to the point of being offensive .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they seemed to do nothing : fixing it was apparently my job .\n->they seemed to do nothing : fixing it was apparently my job .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: game performance was fantastic .\n->game performance was fantastic .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the food was bland oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was bland oily .\n->", + "output": "{\"text\": \"the food was bland oily .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n->i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n->i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n[{'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'pita', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hummus', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled octopus', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i just do n ' t understand all the hype . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just do n ' t understand all the hype . . .\n->", + "output": "{\"text\": \"i just do n ' t understand all the hype . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'hype', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n->An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n[{'aspect': 'chef', 'opinion': 'passion', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish dishes', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soups', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kitchen', 'opinion': 'precise execution', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you need a decent computer that runs quality this is it , especially if you are starting out .\n->if you need a decent computer that runs quality this is it , especially if you are starting out .\n[{'aspect': 'computer', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: we have been to this place many times , and always have great food , wine , and service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe have been to this place many times , and always have great food , wine , and service .\n->", + "output": "{\"text\": \"we have been to this place many times , and always have great food , wine , and service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is one of the best comfort food places in the city .\n->this is one of the best comfort food places in the city .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'comfort', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n->i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: we were worried we would have trouble getting in , but somehow managed to have a short wait .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were worried we would have trouble getting in , but somehow managed to have a short wait .\n->", + "output": "{\"text\": \"we were worried we would have trouble getting in , but somehow managed to have a short wait .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n->The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n[{'aspect': 'seafood', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'ever-changing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'great surprises', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n->Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n[{'aspect': 'wine selection', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Gigondas', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'worth', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: as always we had a great glass of wine while we waited .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas always we had a great glass of wine while we waited .\n->", + "output": "{\"text\": \"as always we had a great glass of wine while we waited .\", \"labels\": \"[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we could have made a meal of the yummy dumplings from the dumpling menu .\n->we could have made a meal of the yummy dumplings from the dumpling menu .\n[{'aspect': 'dumplings', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: we contacted both acer and amazon , and they both informed us that it has to be sent back for repairs again .\n->we contacted both acer and amazon , and they both informed us that it has to be sent back for repairs again .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: when we sat , we got great and fast service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen we sat , we got great and fast service .\n->", + "output": "{\"text\": \"when we sat , we got great and fast service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very impressive design .\n->very impressive design .\n[{'aspect': 'design', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the food was absolutely amazing ! !\n->the food was absolutely amazing ! !\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the people that work there are always so friendly you forget you are in new york sometimes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe people that work there are always so friendly you forget you are in new york sometimes .\n->", + "output": "{\"text\": \"the people that work there are always so friendly you forget you are in new york sometimes .\", \"labels\": \"[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is reliable and the price is moderate .\n->The food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it randomly shuts down all programs running and goes back to the desktop like nothing was going on .\n->it randomly shuts down all programs running and goes back to the desktop like nothing was going on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: this is a fun restaurant to go to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a fun restaurant to go to .\n->", + "output": "{\"text\": \"this is a fun restaurant to go to .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'fun', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n->its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n[{'aspect': 'machine', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: Made my dining experience uncomfortable .\n->Made my dining experience uncomfortable .\n[{'aspect': 'dining experience', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the pizza is yummy and i like the atmoshpere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pizza is yummy and i like the atmoshpere .\n->", + "output": "{\"text\": \"the pizza is yummy and i like the atmoshpere .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the menu has so many fish items and oysters .\n->the menu has so many fish items and oysters .\n[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n->i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n[{'aspect': 'pizza', 'opinion': 'crave', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: but the pizza is way to expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the pizza is way to expensive .\n->", + "output": "{\"text\": \"but the pizza is way to expensive .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: inside is a little cramped , but to be expected .\n->inside is a little cramped , but to be expected .\n[{'aspect': 'NULL', 'opinion': 'cramped', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n->it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n[{'aspect': 'NULL', 'opinion': 'limited', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'operating system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: a large is $ 20 , and toppings are about $ 3 each .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na large is $ 20 , and toppings are about $ 3 each .\n->", + "output": "{\"text\": \"a large is $ 20 , and toppings are about $ 3 each .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'toppings', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n->update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n[{'aspect': 'laptop', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i went there for lunch and it was not as good as i expected from the reviews i read .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni went there for lunch and it was not as good as i expected from the reviews i read .\n->", + "output": "{\"text\": \"i went there for lunch and it was not as good as i expected from the reviews i read .\", \"labels\": \"[{'aspect': 'lunch', 'opinion': 'not as good as i expected', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: highly recommend it !\n->highly recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: you ca n ' t go wrong with this place .\n->you ca n ' t go wrong with this place .\n[{'aspect': 'place', 'opinion': \"ca n ' t go wrong\", 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: sauce was watery and the food did n ' t have much flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsauce was watery and the food did n ' t have much flavor .\n->", + "output": "{\"text\": \"sauce was watery and the food did n ' t have much flavor .\", \"labels\": \"[{'aspect': 'sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n->Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n[{'aspect': 'wine', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: in any event , this is a place i ' ll be sure to stop by again when i ' m in this part of town .\n->in any event , this is a place i ' ll be sure to stop by again when i ' m in this part of town .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i do n ' t think i would go again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do n ' t think i would go again .\n->", + "output": "{\"text\": \"i do n ' t think i would go again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: volare virgins or weekly regulars , everyone gets treated the same and you ca n ' t ask for more than that when the service is this friendly .\n->volare virgins or weekly regulars , everyone gets treated the same and you ca n ' t ask for more than that when the service is this friendly .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i found the food , service and value exceptional everytime i have been there .\n->i found the food , service and value exceptional everytime i have been there .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: this place is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is great .\n->", + "output": "{\"text\": \"this place is great .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only possible drawback to this last point is that as of the date of this posting , the additional menu items are only written in Chinese .\n->The only possible drawback to this last point is that as of the date of this posting , the additional menu items are only written in Chinese .\n[{'aspect': 'menu items', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n->it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n[{'aspect': 'restaurant', 'opinion': 'repulsive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the waitress was very patient with us and the food is phenomenal !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waitress was very patient with us and the food is phenomenal !\n->", + "output": "{\"text\": \"the waitress was very patient with us and the food is phenomenal !\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'patient', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n->i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n[{'aspect': 'keyboard', 'opinion': 'worried', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n->they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n[{'aspect': 'discount', 'opinion': \"was n't enough\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: service was prompt , friendly and great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was prompt , friendly and great .\n->", + "output": "{\"text\": \"service was prompt , friendly and great .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wonderful machine fast , clean , solid i have to said that this guy i fell from my hands the first day i use , goes to the floor and nothing happens screen is perfect and nothing is damage !\n->wonderful machine fast , clean , solid i have to said that this guy i fell from my hands the first day i use , goes to the floor and nothing happens screen is perfect and nothing is damage !\n[{'aspect': 'machine', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'machine', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n->what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n[{'aspect': 'NULL', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fail', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: slightly on the pricey side but worth it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nslightly on the pricey side but worth it !\n->", + "output": "{\"text\": \"slightly on the pricey side but worth it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was pretty good , but a little flavorless and the portions very small , including dessert .\n->The food was pretty good , but a little flavorless and the portions very small , including dessert .\n[{'aspect': 'dessert', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this chromebook was one of the best gift to my grandauther .\n->this chromebook was one of the best gift to my grandauther .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great pizza and fantastic service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat pizza and fantastic service .\n->", + "output": "{\"text\": \"great pizza and fantastic service .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The music is the best among all the Indian restaurants I have visited .\n->The music is the best among all the Indian restaurants I have visited .\n[{'aspect': 'music', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Unique apppetizers .\n->Unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'Unique', 'polarity': 'positive', 'category': 'NULL'}]\ntext: there was a small wait , but shorter than i expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere was a small wait , but shorter than i expected .\n->", + "output": "{\"text\": \"there was a small wait , but shorter than i expected .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we will be back .\n->we will be back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The Yellowtail was particularly good as well .\n->The Yellowtail was particularly good as well .\n[{'aspect': 'Yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: located at the end of a magnificent block .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlocated at the end of a magnificent block .\n->", + "output": "{\"text\": \"located at the end of a magnificent block .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'magnificent', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: they bring service up a notch by offerng complementary amuse bouche to all tables and gave us a small dessert for our celebration .\n->they bring service up a notch by offerng complementary amuse bouche to all tables and gave us a small dessert for our celebration .\n[{'aspect': 'amuse bouche', 'opinion': 'complementary', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very cozy and warm inside . . . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery cozy and warm inside . . . . .\n->", + "output": "{\"text\": \"very cozy and warm inside . . . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i added an sd card which has expanded on the 16gb of storage .\n->i added an sd card which has expanded on the 16gb of storage .\n[{'aspect': 'sd card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: very disappointed with the wireless radio in this chromebook .\n->very disappointed with the wireless radio in this chromebook .\n[{'aspect': 'wireless radio in this chromebook', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: i will be back !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will be back !\n->", + "output": "{\"text\": \"i will be back !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It was so bad I actually refused to pay for my food .\n->It was so bad I actually refused to pay for my food .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this is the best sushi in new york city - hands down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the best sushi in new york city - hands down .\n->", + "output": "{\"text\": \"this is the best sushi in new york city - hands down .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sushi is amazing ! ! !\n->the sushi is amazing ! ! !\n[{'aspect': 'sushi', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n->they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the place is small and cramped but the food is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place is small and cramped but the food is fantastic .\n->", + "output": "{\"text\": \"the place is small and cramped but the food is fantastic .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sad that i can ' t return this one .\n->sad that i can ' t return this one .\n[{'aspect': 'NULL', 'opinion': 'sad', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\nExample:\ntext: Their coffee is quite good too !\n->Their coffee is quite good too !\n[{'aspect': 'coffee', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: planet thailand has always been a hit with me , i go there usually for the sushi , which is great , the thai food is excellent too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplanet thailand has always been a hit with me , i go there usually for the sushi , which is great , the thai food is excellent too .\n->", + "output": "{\"text\": \"planet thailand has always been a hit with me , i go there usually for the sushi , which is great , the thai food is excellent too .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'planet thailand', 'opinion': 'hit', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is good , i ca n ' t lie .\n->the food is good , i ca n ' t lie .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n->the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: with the great variety on the menu , i eat here often and never get bored .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith the great variety on the menu , i eat here often and never get bored .\n->", + "output": "{\"text\": \"with the great variety on the menu , i eat here often and never get bored .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'great variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n->i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n[{'aspect': 'NULL', 'opinion': 'nasty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the atmosphere is n ' t the greatest , but i suppose that ' s how they keep the prices down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe atmosphere is n ' t the greatest , but i suppose that ' s how they keep the prices down .\n->", + "output": "{\"text\": \"the atmosphere is n ' t the greatest , but i suppose that ' s how they keep the prices down .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': \"is n ' t the greatest\", 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'down', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\n->i like the somosas , chai , and the chole , but the dhosas and dhal were kinda disappointing .\n[{'aspect': 'somosas', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chai', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chole', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dhosas', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dhal', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i just fear for the long term ruggedness of the exterior .\n->i just fear for the long term ruggedness of the exterior .\n[{'aspect': 'exterior', 'opinion': 'fear', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: it ' s all about the food ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s all about the food ! !\n->", + "output": "{\"text\": \"it ' s all about the food ! !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n->it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n[{'aspect': 'anti - reflective coating', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n->it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n[{'aspect': 'company', 'opinion': 'poor', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: first it took us a long time to find the place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst it took us a long time to find the place .\n->", + "output": "{\"text\": \"first it took us a long time to find the place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he likes it\n->he likes it\n[{'aspect': 'NULL', 'opinion': 'likes', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Haru serves very fresh fish , has a trendy , modern ambiance , prime location on Park Avenue South and friendly service .\n->Haru serves very fresh fish , has a trendy , modern ambiance , prime location on Park Avenue South and friendly service .\n[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'trendy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'location', 'opinion': 'prime', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but when we looked at the menu , there were n ' t a lot of choices , most of them were dumplings in the appetizer section .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut when we looked at the menu , there were n ' t a lot of choices , most of them were dumplings in the appetizer section .\n->", + "output": "{\"text\": \"but when we looked at the menu , there were n ' t a lot of choices , most of them were dumplings in the appetizer section .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n->my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n[{'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i ' m seriously considering returning it !\n->i ' m seriously considering returning it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: those rolls were big , but not good and sashimi was n ' t fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthose rolls were big , but not good and sashimi was n ' t fresh .\n->", + "output": "{\"text\": \"those rolls were big , but not good and sashimi was n ' t fresh .\", \"labels\": \"[{'aspect': 'rolls', 'opinion': 'big', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sashimi', 'opinion': \"was n ' t fresh\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very disappointed in this machine .\n->very disappointed in this machine .\n[{'aspect': 'machine', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i absolutely love this chromebook .\n->i absolutely love this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: they were dry and disgusting , i did n ' t even finish my first piece .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey were dry and disgusting , i did n ' t even finish my first piece .\n->", + "output": "{\"text\": \"they were dry and disgusting , i did n ' t even finish my first piece .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the new retina is amazing and the speed is awesome .\n->the new retina is amazing and the speed is awesome .\n[{'aspect': 'retina', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'speed', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: Dessert is a joke ... dont bother\n->Dessert is a joke ... dont bother\n[{'aspect': 'Dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'NULL'}]\ntext: hurley ' s is like cheers where everyone knows your name and they are actually glad you came .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhurley ' s is like cheers where everyone knows your name and they are actually glad you came .\n->", + "output": "{\"text\": \"hurley ' s is like cheers where everyone knows your name and they are actually glad you came .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he ' s very happy with it .\n->he ' s very happy with it .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: excellent food , although the interior could use some help .\n->excellent food , although the interior could use some help .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'interior', 'opinion': 'help', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: try the crunchy tuna , it is to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry the crunchy tuna , it is to die for .\n->", + "output": "{\"text\": \"try the crunchy tuna , it is to die for .\", \"labels\": \"[{'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n->i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\nExample:\ntext: - the speakers are to the sides and not underneath so the sound isn ' t muffled when it ' s resting on something other than a flat surface\n->- the speakers are to the sides and not underneath so the sound isn ' t muffled when it ' s resting on something other than a flat surface\n[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\ntext: try everything for that matter , it is all good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry everything for that matter , it is all good .\n->", + "output": "{\"text\": \"try everything for that matter , it is all good .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the decor is vibrant and eye - pleasing with several semi - private boths on the right side of the dining hall , which are great for a date .\n->the decor is vibrant and eye - pleasing with several semi - private boths on the right side of the dining hall , which are great for a date .\n[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'eye - pleasing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'semi - private boths', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: love the laptop ; great quality ; sent as expected , on time .\n->love the laptop ; great quality ; sent as expected , on time .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: i have been going there since it opened and i ca n ' t get enough .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been going there since it opened and i ca n ' t get enough .\n->", + "output": "{\"text\": \"i have been going there since it opened and i ca n ' t get enough .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sauce tasted more like chinese fast food than decent korean .\n->the sauce tasted more like chinese fast food than decent korean .\n[{'aspect': 'sauce', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: technical support was easy to reach , but not able to stop the problem i was having .\n->technical support was easy to reach , but not able to stop the problem i was having .\n[{'aspect': 'technical support', 'opinion': 'easy', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: first went here to enjoy their garden terrace .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst went here to enjoy their garden terrace .\n->", + "output": "{\"text\": \"first went here to enjoy their garden terrace .\", \"labels\": \"[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: condition even better then i expect .\n->condition even better then i expect .\n[{'aspect': 'condition', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food was amazing , and the service was prompt and helpful , but not over - bearing or rushed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was amazing , and the service was prompt and helpful , but not over - bearing or rushed .\n->", + "output": "{\"text\": \"the food was amazing , and the service was prompt and helpful , but not over - bearing or rushed .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'not over -', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'or', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is nice for the price and has good speed .\n->this is nice for the price and has good speed .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i love the fact that it can extend and be flat .\n->i love the fact that it can extend and be flat .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the steak tartare is a great bet , they fix it for you at the table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe steak tartare is a great bet , they fix it for you at the table .\n->", + "output": "{\"text\": \"the steak tartare is a great bet , they fix it for you at the table .\", \"labels\": \"[{'aspect': 'steak tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n->The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n[{'aspect': 'wait staff', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'knowledgable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'likeable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n->Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'colorful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nadmittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n->", + "output": "{\"text\": \"admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\", \"labels\": \"[{'aspect': 'open kitchen', 'opinion': 'charm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was well prepared and the service impecable .\n->The food was well prepared and the service impecable .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the yellowtail was particularly good as well .\n->the yellowtail was particularly good as well .\n[{'aspect': 'yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: great wine selection , gigondas is worth the price , and the house champagne is a great value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat wine selection , gigondas is worth the price , and the house champagne is a great value .\n->", + "output": "{\"text\": \"great wine selection , gigondas is worth the price , and the house champagne is a great value .\", \"labels\": \"[{'aspect': 'wine selection', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'gigondas', 'opinion': 'worth', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: As much as I like the food there , I ca n't bring myself to go back .\n->As much as I like the food there , I ca n't bring myself to go back .\n[{'aspect': 'food', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n->that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\ntext: it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n->", + "output": "{\"text\": \"it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'french food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n->in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'chrome os', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: not because you are ` ` the four seasons ` ` . . . \u2013 you are allowed to charge an arm and a leg for a romatic dinner .\n->not because you are ` ` the four seasons ` ` . . . \u2013 you are allowed to charge an arm and a leg for a romatic dinner .\n[{'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: have recommended the place to friends , always gets good response .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave recommended the place to friends , always gets good response .\n->", + "output": "{\"text\": \"have recommended the place to friends , always gets good response .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n->you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: but , now i realize the design is flawed .\n->but , now i realize the design is flawed .\n[{'aspect': 'design', 'opinion': 'flawed', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: pizza - the only pizza in nyc that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npizza - the only pizza in nyc that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->", + "output": "{\"text\": \"pizza - the only pizza in nyc that should not have additional toppings - the crust tastes like the best , freshly baked bread !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'freshly baked', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: their designs are made for phones and on this huge screen , they are palpitated .\n->their designs are made for phones and on this huge screen , they are palpitated .\n[{'aspect': 'designs', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: so close , but not good enough .\n->so close , but not good enough .\n[{'aspect': 'NULL', 'opinion': 'not good enough', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\ntext: i take all my nyc guests to vt ' s .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni take all my nyc guests to vt ' s .\n->", + "output": "{\"text\": \"i take all my nyc guests to vt ' s .\", \"labels\": \"[{'aspect': \"vt ' s\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is a little smaller , but it ' s touch and even higher resolution .\n->the screen is a little smaller , but it ' s touch and even higher resolution .\n[{'aspect': 'screen', 'opinion': 'smaller', 'polarity': 'neutral', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'touch', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'higher', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: i use it for streaming with the elgato device and it doesn ' t miss a beat .\n->i use it for streaming with the elgato device and it doesn ' t miss a beat .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: not sure where the previous reviewer , lonk , dined , but saul is in a great neighborhood and has great food !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot sure where the previous reviewer , lonk , dined , but saul is in a great neighborhood and has great food !\n->", + "output": "{\"text\": \"not sure where the previous reviewer , lonk , dined , but saul is in a great neighborhood and has great food !\", \"labels\": \"[{'aspect': 'neighborhood', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen on this looks great , the bezels aren ' t noticeable .\n->the screen on this looks great , the bezels aren ' t noticeable .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': \"' t noticeable\", 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: in essence , if you want a gaming pc , this one will do the job .\n->in essence , if you want a gaming pc , this one will do the job .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' ve been there three times and have always had wonderful experiences .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been there three times and have always had wonderful experiences .\n->", + "output": "{\"text\": \"i ' ve been there three times and have always had wonderful experiences .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n->If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n[{'aspect': 'ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I can not imagine better Indian food in all of the city .\n->I can not imagine better Indian food in all of the city .\n[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' d highly recommend it for a special occasion - - it provides and intimate setting and nice service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' d highly recommend it for a special occasion - - it provides and intimate setting and nice service .\n->", + "output": "{\"text\": \"i ' d highly recommend it for a special occasion - - it provides and intimate setting and nice service .\", \"labels\": \"[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we will be back .\n->we will be back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: While the staff at this little bistro is very friendly , I have never experienced more incompetency .\n->While the staff at this little bistro is very friendly , I have never experienced more incompetency .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n->", + "output": "{\"text\": \"i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\", \"labels\": \"[{'aspect': 'mizu', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent product and experience with the purchase .\n->excellent product and experience with the purchase .\n[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n->From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caviar', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: even after they overcharged me the last time i was there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven after they overcharged me the last time i was there .\n->", + "output": "{\"text\": \"even after they overcharged me the last time i was there .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'overcharged', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pro is by far the best .\n->the pro is by far the best .\n[{'aspect': 'pro', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great laptop great price .\n->great laptop great price .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: it ' s delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s delicious !\n->", + "output": "{\"text\": \"it ' s delicious !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n->and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n[{'aspect': 'backlit keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n->While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: moules were excellent , lobster ravioli was very salty !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmoules were excellent , lobster ravioli was very salty !\n->", + "output": "{\"text\": \"moules were excellent , lobster ravioli was very salty !\", \"labels\": \"[{'aspect': 'moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is always packed .\n->this place is always packed .\n[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Service was very good - prompt , attentive and non-intrusive .\n->Service was very good - prompt , attentive and non-intrusive .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: took my mom for mother ' s day , and the maitre d ' was pretty rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntook my mom for mother ' s day , and the maitre d ' was pretty rude .\n->", + "output": "{\"text\": \"took my mom for mother ' s day , and the maitre d ' was pretty rude .\", \"labels\": \"[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n->the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the speakers on this model are really nice as well .\n->the speakers on this model are really nice as well .\n[{'aspect': 'speakers', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: told us to sit anywhere , and when we sat he said the table was reserved .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntold us to sit anywhere , and when we sat he said the table was reserved .\n->", + "output": "{\"text\": \"told us to sit anywhere , and when we sat he said the table was reserved .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ive researched this and it is very common and apple says it ' s normal .\n->ive researched this and it is very common and apple says it ' s normal .\n[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: stepped on my foot on the second time he reached over me to adjust lighting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstepped on my foot on the second time he reached over me to adjust lighting .\n->", + "output": "{\"text\": \"stepped on my foot on the second time he reached over me to adjust lighting .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n->now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'bummed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n->Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n[{'aspect': 'Thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: tiny dessert was $ 8 . 00 . . . just plain overpriced for what it is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntiny dessert was $ 8 . 00 . . . just plain overpriced for what it is .\n->", + "output": "{\"text\": \"tiny dessert was $ 8 . 00 . . . just plain overpriced for what it is .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessert', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was attentive .\n->The service was attentive .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great food , great decor , great service .\n->great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the drinks are always well made and wine selection is fairly priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe drinks are always well made and wine selection is fairly priced .\n->", + "output": "{\"text\": \"the drinks are always well made and wine selection is fairly priced .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'well made', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine selection', 'opinion': 'fairly priced', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it would not start up after 2 months of purchase and then re - set button didn ' t work .\n->it would not start up after 2 months of purchase and then re - set button didn ' t work .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 're - set button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: The food was average or above including some surprising tasty dishes .\n->The food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: try their chef ' s specials - - they are to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry their chef ' s specials - - they are to die for .\n->", + "output": "{\"text\": \"try their chef ' s specials - - they are to die for .\", \"labels\": \"[{'aspect': \"chef ' s specials\", 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n->The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n[{'aspect': 'seating', 'opinion': 'drafty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'tight', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the only thing you can do here is walk in and eat . . but planning an event , especially a small , intimate one , forget about it .\n->the only thing you can do here is walk in and eat . . but planning an event , especially a small , intimate one , forget about it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: service is not exactly five star , but thats not really a big deal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is not exactly five star , but thats not really a big deal .\n->", + "output": "{\"text\": \"service is not exactly five star , but thats not really a big deal .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'not exactly five star', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * track pad * - the trackpad is well done .\n->* track pad * - the trackpad is well done .\n[{'aspect': 'track pad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: good for students that carry it from class to class .\n->good for students that carry it from class to class .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: downstairs lounge is always a good attraction\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndownstairs lounge is always a good attraction\n->", + "output": "{\"text\": \"downstairs lounge is always a good attraction\", \"labels\": \"[{'aspect': 'downstairs lounge', 'opinion': 'good attraction', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve never had bad service and the fish is fresh and delicious .\n->i ' ve never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: \u2022 super thin and light weight\n->\u2022 super thin and light weight\n[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the exotic food is beautifully presented and is a delight in delicious combinations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe exotic food is beautifully presented and is a delight in delicious combinations .\n->", + "output": "{\"text\": \"the exotic food is beautifully presented and is a delight in delicious combinations .\", \"labels\": \"[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We waited at the bar and had martinis that were just right .\n->We waited at the bar and had martinis that were just right .\n[{'aspect': 'martinis', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: as everyone else says ; the keyboard is not backlit .\n->as everyone else says ; the keyboard is not backlit .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: it is also extremely well priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is also extremely well priced .\n->", + "output": "{\"text\": \"it is also extremely well priced .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'extremely well', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n->for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the asus chromebook flip 302 fit the bill .\n->the asus chromebook flip 302 fit the bill .\n[{'aspect': 'asus chromebook flip 302', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the staff is incredibly helpful and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff is incredibly helpful and attentive .\n->", + "output": "{\"text\": \"the staff is incredibly helpful and attentive .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portions are now very small , the sauces are overly ambitious usually inedible while the service is still good , the restaurant , due to its popularity , seems frantic .\n->The portions are now very small , the sauces are overly ambitious usually inedible while the service is still good , the restaurant , due to its popularity , seems frantic .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'ambitious', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n->The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'barely', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spice', 'opinion': 'took away', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the bar is very well stocked with interesting beers and well priced wines .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bar is very well stocked with interesting beers and well priced wines .\n->", + "output": "{\"text\": \"the bar is very well stocked with interesting beers and well priced wines .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'well stocked', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'beers', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wines', 'opinion': 'well priced', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i go twice a month !\n->i go twice a month !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the fried dumplings are great !\n->the fried dumplings are great !\n[{'aspect': 'fried dumplings', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this is one of my favorite restaurants and it is not to be missed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is one of my favorite restaurants and it is not to be missed .\n->", + "output": "{\"text\": \"this is one of my favorite restaurants and it is not to be missed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: win for the ports , win for the price , and win for a brand new unopened macbook .\n->win for the ports , win for the price , and win for a brand new unopened macbook .\n[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the computer is great , but the charger is garbage .\n->the computer is great , but the charger is garbage .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'charger', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: your friends will thank you for introducing them to this gem !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyour friends will thank you for introducing them to this gem !\n->", + "output": "{\"text\": \"your friends will thank you for introducing them to this gem !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was good not great not worth the wait or another visit\n->Food was good not great not worth the wait or another visit\n[{'aspect': 'wait', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the hostess is rude to the point of being offensive .\n->the hostess is rude to the point of being offensive .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'hostess', 'opinion': 'offensive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: when we stumbled on leon , we thought that we had found quite the gem but , we were certainly wrong .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen we stumbled on leon , we thought that we had found quite the gem but , we were certainly wrong .\n->", + "output": "{\"text\": \"when we stumbled on leon , we thought that we had found quite the gem but , we were certainly wrong .\", \"labels\": \"[{'aspect': 'leon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n->I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The sangria was pretty tasty and good on a hot muggy day .\n->The sangria was pretty tasty and good on a hot muggy day .\n[{'aspect': 'sangria', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sangria', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no . . .\n->", + "output": "{\"text\": \"the waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no . . .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n->the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n[{'aspect': 'servers', 'opinion': 'perfected', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: but overall i give it a 10\n->but overall i give it a 10\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: then , to top things off , she dropped used silverware on my boyfriend ' s jacket and did not stop to apologize or clean the mess that was left on clothes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen , to top things off , she dropped used silverware on my boyfriend ' s jacket and did not stop to apologize or clean the mess that was left on clothes .\n->", + "output": "{\"text\": \"then , to top things off , she dropped used silverware on my boyfriend ' s jacket and did not stop to apologize or clean the mess that was left on clothes .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even better , they know how to cook French classics like Steak au Poivre and Onglet without burning it to death or overcooking it .\n->Even better , they know how to cook French classics like Steak au Poivre and Onglet without burning it to death or overcooking it .\n[{'aspect': 'Steak au Poivre', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Onglet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is good , especially their more basic dishes , and the drinks are delicious .\n->The food is good , especially their more basic dishes , and the drinks are delicious .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: such a disappointment . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuch a disappointment . . .\n->", + "output": "{\"text\": \"such a disappointment . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mousepad is functional but really doesnt get in the way .\n->the mousepad is functional but really doesnt get in the way .\n[{'aspect': 'mousepad', 'opinion': 'functional', 'polarity': 'neutral', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: they smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n->they smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: rude service , medicore food . . . there are tons of restaurants in ny . . . stay away from this one\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nrude service , medicore food . . . there are tons of restaurants in ny . . . stay away from this one\n->", + "output": "{\"text\": \"rude service , medicore food . . . there are tons of restaurants in ny . . . stay away from this one\", \"labels\": \"[{'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'medicore', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the potato balls were not dry at all . . . in fact it was buttery .\n->the potato balls were not dry at all . . . in fact it was buttery .\n[{'aspect': 'potato balls', 'opinion': 'not dry', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'potato balls', 'opinion': 'buttery', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: biggest gripe , no backlights on the keyboard .\n->biggest gripe , no backlights on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i loved everythig about it - especially the shows and actors .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved everythig about it - especially the shows and actors .\n->", + "output": "{\"text\": \"i loved everythig about it - especially the shows and actors .\", \"labels\": \"[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at taj , vegetarians can rejoice-all the dishes are manna from heaven .\n->at taj , vegetarians can rejoice-all the dishes are manna from heaven .\n[{'aspect': 'dishes', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is the most well priced laptop for its spec\n->this is the most well priced laptop for its spec\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'spec', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: our server was very helpful and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour server was very helpful and friendly .\n->", + "output": "{\"text\": \"our server was very helpful and friendly .\", \"labels\": \"[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n->not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n->it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the food was good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was good too .\n->", + "output": "{\"text\": \"the food was good too .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n->microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food is all shared so we get to order together and eat together .\n->the food is all shared so we get to order together and eat together .\n[{'aspect': 'food', 'opinion': 'shared', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the tuna and wasabe potatoes are excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe tuna and wasabe potatoes are excellent .\n->", + "output": "{\"text\": \"the tuna and wasabe potatoes are excellent .\", \"labels\": \"[{'aspect': 'tuna', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wasabe potatoes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i even tried reinstalling the drivers and doing a system restore , nothing would fix it .\n->i even tried reinstalling the drivers and doing a system restore , nothing would fix it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Great Indian food and the service is incredible .\n->Great Indian food and the service is incredible .\n[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n->", + "output": "{\"text\": \"the outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\", \"labels\": \"[{'aspect': 'outdoor atmosphere', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you do n ' t need a full blown laptop this is a good choice .\n->if you do n ' t need a full blown laptop this is a good choice .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: after charging the unit for 2 hours i discovered that the unit will only operate while the charger is connected .\n->after charging the unit for 2 hours i discovered that the unit will only operate while the charger is connected .\n[{'aspect': 'unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: inside is a little cramped , but to be expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ninside is a little cramped , but to be expected .\n->", + "output": "{\"text\": \"inside is a little cramped , but to be expected .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cramped', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n->In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is a very good laptop .\n->it is a very good laptop .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: service was prompt and courteous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was prompt and courteous .\n->", + "output": "{\"text\": \"service was prompt and courteous .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was bland oily .\n->The food was bland oily .\n[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n->the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'halibut special', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'steak', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'top - notch', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: this was a repeat visit and we ' ll definitely be back again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was a repeat visit and we ' ll definitely be back again .\n->", + "output": "{\"text\": \"this was a repeat visit and we ' ll definitely be back again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she loves it .\n->she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n->the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: great service , great food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat service , great food .\n->", + "output": "{\"text\": \"great service , great food .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n->the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: then , to top things off , she dropped used silverware on my boyfriend ' s jacket and did not stop to apologize or clean the mess that was left on clothes .\n->then , to top things off , she dropped used silverware on my boyfriend ' s jacket and did not stop to apologize or clean the mess that was left on clothes .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: prices are in line .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprices are in line .\n->", + "output": "{\"text\": \"prices are in line .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'in line', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I ordered the smoked salmon and roe appetizer and it was off flavor .\n->I ordered the smoked salmon and roe appetizer and it was off flavor .\n[{'aspect': 'smoked salmon and roe appetizer', 'opinion': 'off flavor', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the only thing i didn ' t like is the big dent on the front of the lid .\n->the only thing i didn ' t like is the big dent on the front of the lid .\n[{'aspect': 'dent', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: but too far east !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut too far east !\n->", + "output": "{\"text\": \"but too far east !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'far', 'polarity': 'negative', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good spreads , great beverage selections and bagels really tasty .\n->Good spreads , great beverage selections and bagels really tasty .\n[{'aspect': 'spreads', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beverage selections', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it makes the entire device unusable .\n->it makes the entire device unusable .\n[{'aspect': 'device', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: the pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria ' s .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria ' s .\n->", + "output": "{\"text\": \"the pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria ' s .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mozzarella', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: monitor looks crisp .\n->monitor looks crisp .\n[{'aspect': 'monitor', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: that said just buy it with confidence , it ' s a top quality product all around , and , it looks and feels that way .\n->that said just buy it with confidence , it ' s a top quality product all around , and , it looks and feels that way .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntwo complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->", + "output": "{\"text\": \"two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\", \"labels\": \"[{'aspect': 'appetizer selection', 'opinion': 'stinks', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we had a great time at the jekyll and hyde pub last night .\n->we had a great time at the jekyll and hyde pub last night .\n[{'aspect': 'jekyll and hyde pub', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: apple should be embarrassed .\n->apple should be embarrassed .\n[{'aspect': 'apple', 'opinion': 'embarrassed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: also , because it is so thin , it gets cold very quickly and its not that filling .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , because it is so thin , it gets cold very quickly and its not that filling .\n->", + "output": "{\"text\": \"also , because it is so thin , it gets cold very quickly and its not that filling .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Late nite omelletes are not good here , there is no variety !\n->Late nite omelletes are not good here , there is no variety !\n[{'aspect': 'omelletes', 'opinion': 'not good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n->this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n[{'aspect': 'chromebook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: wait staff is blantently unappreciative of your business but its the best pie on the uws !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwait staff is blantently unappreciative of your business but its the best pie on the uws !\n->", + "output": "{\"text\": \"wait staff is blantently unappreciative of your business but its the best pie on the uws !\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'unappreciative', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'pie', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is so cheap and the waiters are nice .\n->The food is so cheap and the waiters are nice .\n[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n->my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: by far the best salad i have had in a fast food restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nby far the best salad i have had in a fast food restaurant .\n->", + "output": "{\"text\": \"by far the best salad i have had in a fast food restaurant .\", \"labels\": \"[{'aspect': 'salad', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard also feels nice and the backlighting is great .\n->the keyboard also feels nice and the backlighting is great .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlighting', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n->we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: over 100 different choices to create your own .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nover 100 different choices to create your own .\n->", + "output": "{\"text\": \"over 100 different choices to create your own .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great build materials and quality .\n->great build materials and quality .\n[{'aspect': 'build materials', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the drag and drop works poorly which is very annoying .\n->the drag and drop works poorly which is very annoying .\n[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: a must try !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na must try !\n->", + "output": "{\"text\": \"a must try !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my roommate and i love this place .\n->my roommate and i love this place .\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Well , this place is so Ghetto its not even funny .\n->Well , this place is so Ghetto its not even funny .\n[{'aspect': 'place', 'opinion': 'Ghetto', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'not even funny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: on a recent sunday afternoon , a friend and i accidently found this great restaurant on our way to see the pulitzer prize winning play doubt .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non a recent sunday afternoon , a friend and i accidently found this great restaurant on our way to see the pulitzer prize winning play doubt .\n->", + "output": "{\"text\": \"on a recent sunday afternoon , a friend and i accidently found this great restaurant on our way to see the pulitzer prize winning play doubt .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: relatively small screen with high resolution makes reading the screen difficult .\n->relatively small screen with high resolution makes reading the screen difficult .\n[{'aspect': 'screen', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: my husbands was perfect , my was well done and dry .\n->my husbands was perfect , my was well done and dry .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'well done', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n->", + "output": "{\"text\": \"this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is also very gnu + linux friendly if you want to replace the os entirely .\n->it is also very gnu + linux friendly if you want to replace the os entirely .\n[{'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Great vibe , lots of people .\n->Great vibe , lots of people .\n[{'aspect': 'vibe', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->", + "output": "{\"text\": \"the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\", \"labels\": \"[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great selection of wine , and seafood .\n->Great selection of wine , and seafood .\n[{'aspect': 'selection of wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the monitor is good , and graphic chip is enough for my office work , internet browsing and video streaming , don ' t think about what game it can play , i won ' t expect intel graphic chip can do a lot , if you want a gaming laptop , find some model with independent graphic chip , if you want a cheap laptop but can play computer game , you should wake up from the dream .\n->the monitor is good , and graphic chip is enough for my office work , internet browsing and video streaming , don ' t think about what game it can play , i won ' t expect intel graphic chip can do a lot , if you want a gaming laptop , find some model with independent graphic chip , if you want a cheap laptop but can play computer game , you should wake up from the dream .\n[{'aspect': 'monitor', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'graphic chip', 'opinion': 'enough', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: with so many poor experiences to be had in the theater district , is truly an excellent find !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith so many poor experiences to be had in the theater district , is truly an excellent find !\n->", + "output": "{\"text\": \"with so many poor experiences to be had in the theater district , is truly an excellent find !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is also crisp and the speakers are punchy for a laptop .\n->the screen is also crisp and the speakers are punchy for a laptop .\n[{'aspect': 'speakers', 'opinion': 'punchy', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: the touchscreen functions very well , both in laptop and tablet mode , and the trackpad and keyboard are enjoyable to use .\n->the touchscreen functions very well , both in laptop and tablet mode , and the trackpad and keyboard are enjoyable to use .\n[{'aspect': 'touchscreen', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: the staff is no nonsense .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff is no nonsense .\n->", + "output": "{\"text\": \"the staff is no nonsense .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'no nonsense', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is nice .\n->the screen is nice .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: when in use , the lower screen is flickering .\n->when in use , the lower screen is flickering .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: when i lived upstate for a while i would buy freeze the bagels and they would still be better than any else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i lived upstate for a while i would buy freeze the bagels and they would still be better than any else .\n->", + "output": "{\"text\": \"when i lived upstate for a while i would buy freeze the bagels and they would still be better than any else .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m trying to do online college college courses with it and it gets hung up in some intimate nevernever land of unresponsiveness .\n->i ' m trying to do online college college courses with it and it gets hung up in some intimate nevernever land of unresponsiveness .\n[{'aspect': 'NULL', 'opinion': 'unresponsiveness', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: even the keyboard is fantastic .\n->even the keyboard is fantastic .\n[{'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: worth visiting the 1st ave spot because it is the original store .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworth visiting the 1st ave spot because it is the original store .\n->", + "output": "{\"text\": \"worth visiting the 1st ave spot because it is the original store .\", \"labels\": \"[{'aspect': '1st ave spot', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the food was excellent - considering the quality of food in most moderately priced restaurants is mediocre this was slightly more pricey and well worth it .\n->all the food was excellent - considering the quality of food in most moderately priced restaurants is mediocre this was slightly more pricey and well worth it .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality of food', 'opinion': 'mediocre', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'moderately', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The wine is always good , the tapas are always yummy , especially with the warm pita bread .\n->The wine is always good , the tapas are always yummy , especially with the warm pita bread .\n[{'aspect': 'wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tapas', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pita bread', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}]\ntext: he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhe served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n->", + "output": "{\"text\": \"he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\", \"labels\": \"[{'aspect': 'uni hand roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The entertainment was great they have shows that go on through out the dinner .\n->The entertainment was great they have shows that go on through out the dinner .\n[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 2 ssd as it will not fit the slot available .\n->2 ssd as it will not fit the slot available .\n[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: the sake menu should not be overlooked !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sake menu should not be overlooked !\n->", + "output": "{\"text\": \"the sake menu should not be overlooked !\", \"labels\": \"[{'aspect': 'sake menu', 'opinion': 'overlooked', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n->My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n[{'aspect': 'spinach', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shanghai low mein', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: nice keyboard\n->nice keyboard\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: all in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n->", + "output": "{\"text\": \"all in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am sure amazon will exchange it again but it is not worth the time and hassle .\n->i am sure amazon will exchange it again but it is not worth the time and hassle .\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: good laptop , but not great .\n->good laptop , but not great .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: try the lobster teriyaki and the rose special roll .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry the lobster teriyaki and the rose special roll .\n->", + "output": "{\"text\": \"try the lobster teriyaki and the rose special roll .\", \"labels\": \"[{'aspect': 'lobster teriyaki', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rose special roll', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great .\n->The food is great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n->we had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n[{'aspect': 'scallops', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: service was very good - prompt , attentive and non - intrusive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was very good - prompt , attentive and non - intrusive .\n->", + "output": "{\"text\": \"service was very good - prompt , attentive and non - intrusive .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'non - intrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n->i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: It 's a small cute restaurant .\n->It 's a small cute restaurant .\n[{'aspect': 'restaurant', 'opinion': 'small cute', 'polarity': 'positive', 'category': 'NULL'}]\ntext: food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n->", + "output": "{\"text\": \"food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork belly', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doubles as an android tablet and so far the experience with running android apps has been good .\n->it doubles as an android tablet and so far the experience with running android apps has been good .\n[{'aspect': 'android apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: - with the combination of web - based productivity tools and development apps / services , this chromebook can provide a breadth of very viable usage scenarios without bogging the system down with locally install applications .\n->- with the combination of web - based productivity tools and development apps / services , this chromebook can provide a breadth of very viable usage scenarios without bogging the system down with locally install applications .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: wine list selection is good and wine - by - the - glass was generously filled to the top .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwine list selection is good and wine - by - the - glass was generously filled to the top .\n->", + "output": "{\"text\": \"wine list selection is good and wine - by - the - glass was generously filled to the top .\", \"labels\": \"[{'aspect': 'wine list selection', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine - by - the - glass', 'opinion': 'generously filled', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We both opted for a pasta dish and they were served timely and fresh .\n->We both opted for a pasta dish and they were served timely and fresh .\n[{'aspect': 'pasta dish', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop has handled everything i have thrown at it .\n->this laptop has handled everything i have thrown at it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: traditional french decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntraditional french decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n->", + "output": "{\"text\": \"traditional french decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\", \"labels\": \"[{'aspect': 'traditional french decour', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'hall', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great purchase , quick shipping .\n->great purchase , quick shipping .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'shipping', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n->but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'indian food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i ' ve been to at cafe spice probably 5 - 8 times , it is probably still the best indian restaurant around union square .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been to at cafe spice probably 5 - 8 times , it is probably still the best indian restaurant around union square .\n->", + "output": "{\"text\": \"i ' ve been to at cafe spice probably 5 - 8 times , it is probably still the best indian restaurant around union square .\", \"labels\": \"[{'aspect': 'cafe spice', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: upon accepting a graphics driver update , the whole laptop froze .\n->upon accepting a graphics driver update , the whole laptop froze .\n[{'aspect': 'laptop', 'opinion': 'froze', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n->the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n[{'aspect': 'mouse', 'opinion': 'good', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\ntext: to sum it up : service varies from good to mediorce , depending on which waiter you get ; generally it is just average ok .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto sum it up : service varies from good to mediorce , depending on which waiter you get ; generally it is just average ok .\n->", + "output": "{\"text\": \"to sum it up : service varies from good to mediorce , depending on which waiter you get ; generally it is just average ok .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'mediorce', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'average ok', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we have always liked lenovo laptops .\n->we have always liked lenovo laptops .\n[{'aspect': 'lenovo laptops', 'opinion': 'liked', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: the build quality is fantastic .\n->the build quality is fantastic .\n[{'aspect': 'build quality', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: seating is always prompt , though the restaurant does fill up in the evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nseating is always prompt , though the restaurant does fill up in the evening .\n->", + "output": "{\"text\": \"seating is always prompt , though the restaurant does fill up in the evening .\", \"labels\": \"[{'aspect': 'seating', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when my dessert came , there was a candle in it - not because anyone asked for one - but because the waiter must have seen me opening my birthday card and gift , and said he knew it was a special occassion of some sort .\n->when my dessert came , there was a candle in it - not because anyone asked for one - but because the waiter must have seen me opening my birthday card and gift , and said he knew it was a special occassion of some sort .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Even though its good seafood , the prices are too high .\n->Even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: food is usually very good , though ocasionally i wondered about freshmess of raw vegatables in side orders .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood is usually very good , though ocasionally i wondered about freshmess of raw vegatables in side orders .\n->", + "output": "{\"text\": \"food is usually very good , though ocasionally i wondered about freshmess of raw vegatables in side orders .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'raw vegatables in side orders', 'opinion': 'wondered about freshmess', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - jstorrent works nicely for any torrenting needs .\n->- jstorrent works nicely for any torrenting needs .\n[{'aspect': 'jstorrent', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: this chromebook is amazing .\n->this chromebook is amazing .\n[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: as many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .\n->", + "output": "{\"text\": \"as many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my dad says it works extremely well !\n->my dad says it works extremely well !\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n->we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the decor is vibrant and eye - pleasing with several semi - private boths on the right side of the dining hall , which are great for a date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe decor is vibrant and eye - pleasing with several semi - private boths on the right side of the dining hall , which are great for a date .\n->", + "output": "{\"text\": \"the decor is vibrant and eye - pleasing with several semi - private boths on the right side of the dining hall , which are great for a date .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'eye - pleasing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'semi - private boths', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: plug - in or usb microphones seem to work fine , so it ' s not a terribly big issue and i don ' t really even use a microphone that often , but it ' s annoying to buy a product and not have it working .\n->plug - in or usb microphones seem to work fine , so it ' s not a terribly big issue and i don ' t really even use a microphone that often , but it ' s annoying to buy a product and not have it working .\n[{'aspect': 'product', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the screen is pleasantly satisfying with the touch screen and foldability .\n->the screen is pleasantly satisfying with the touch screen and foldability .\n[{'aspect': 'screen', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: i have never been disappointed in the red eye .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have never been disappointed in the red eye .\n->", + "output": "{\"text\": \"i have never been disappointed in the red eye .\", \"labels\": \"[{'aspect': 'red eye', 'opinion': 'never been disappointed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n->i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n[{'aspect': 'computer', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: pro : light , reasonable price , fast .\n->pro : light , reasonable price , fast .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the first time i went , and was completely taken by the live jazz band and atmosphere , i ordered the lobster cobb salad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe first time i went , and was completely taken by the live jazz band and atmosphere , i ordered the lobster cobb salad .\n->", + "output": "{\"text\": \"the first time i went , and was completely taken by the live jazz band and atmosphere , i ordered the lobster cobb salad .\", \"labels\": \"[{'aspect': 'live jazz band', 'opinion': 'taken', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'taken', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first one i received , the space bar got stuck and returned it for a replacement .\n->first one i received , the space bar got stuck and returned it for a replacement .\n[{'aspect': 'space bar', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i returned this one as well .\n->i returned this one as well .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it ' s simply the best meal in nyc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s simply the best meal in nyc .\n->", + "output": "{\"text\": \"it ' s simply the best meal in nyc .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I started out with a Bombay beer which was big enough for two .\n->I started out with a Bombay beer which was big enough for two .\n[{'aspect': 'Bombay beer', 'opinion': 'big', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: But they 've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual ) .\n->But they 've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual ) .\n[{'aspect': 'Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual )', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you can not go wrong at the red eye grill .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can not go wrong at the red eye grill .\n->", + "output": "{\"text\": \"you can not go wrong at the red eye grill .\", \"labels\": \"[{'aspect': 'red eye grill', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food here does a great service to the name ( cantonese that is . . . ) .\n->the food here does a great service to the name ( cantonese that is . . . ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: high speed laptop\n->high speed laptop\n[{'aspect': 'laptop', 'opinion': 'high', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: if we were to move from the upper east side , we would genuinely miss this restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif we were to move from the upper east side , we would genuinely miss this restaurant .\n->", + "output": "{\"text\": \"if we were to move from the upper east side , we would genuinely miss this restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery is not as long\n->battery is not as long\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: go here for the drinks !\n->go here for the drinks !\n[{'aspect': 'drinks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: the restaurant is cute but not upscale .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe restaurant is cute but not upscale .\n->", + "output": "{\"text\": \"the restaurant is cute but not upscale .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'cute', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'not upscale', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n->- i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n[{'aspect': 'chromebooks', 'opinion': 'worried', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: perfect laptop for everyday use .\n->perfect laptop for everyday use .\n[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->", + "output": "{\"text\": \"the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'and', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'with', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in laptop mode the trackpad works very well for this .\n->in laptop mode the trackpad works very well for this .\n[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n->all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n[{'aspect': 'web browsing', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i would highly recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would highly recommend it .\n->", + "output": "{\"text\": \"i would highly recommend it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i gave it 3 out of 5 stars , because there is no sd card slot !\n->i gave it 3 out of 5 stars , because there is no sd card slot !\n[{'aspect': 'sd card slot', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: horrible light bleed from the top edge of the screen\n->horrible light bleed from the top edge of the screen\n[{'aspect': 'screen', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: it ' s one of our favorite places to eat in ny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s one of our favorite places to eat in ny .\n->", + "output": "{\"text\": \"it ' s one of our favorite places to eat in ny .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Waitstaff are very friendly .\n->Waitstaff are very friendly .\n[{'aspect': 'Waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but seemed very poorly made for the money .\n->but seemed very poorly made for the money .\n[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: we had a great time at the jekyll and hyde pub last night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe had a great time at the jekyll and hyde pub last night .\n->", + "output": "{\"text\": \"we had a great time at the jekyll and hyde pub last night .\", \"labels\": \"[{'aspect': 'jekyll and hyde pub', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All conveniently delivered right to the door .\n->All conveniently delivered right to the door .\n[{'aspect': 'delivered', 'opinion': 'conveniently', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s easy find and delete pics and files you ' ve downloaded .\n->it ' s easy find and delete pics and files you ' ve downloaded .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: after really enjoying ourselves at the bar we sat down at a table and had dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter really enjoying ourselves at the bar we sat down at a table and had dinner .\n->", + "output": "{\"text\": \"after really enjoying ourselves at the bar we sat down at a table and had dinner .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .\n->The food was delicious ( I had a halibut special , my husband had steak ) , and the service was top-notch .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'halibut special', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my shiny speedy asus chromebook froze after only one month of use and i am returning it today for a full refund .\n->my shiny speedy asus chromebook froze after only one month of use and i am returning it today for a full refund .\n[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the server was really cool and served us our food and drinks with a smile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe server was really cool and served us our food and drinks with a smile .\n->", + "output": "{\"text\": \"the server was really cool and served us our food and drinks with a smile .\", \"labels\": \"[{'aspect': 'server', 'opinion': 'cool', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only con would be that display is not that bright , although i would say at the brightest setting is probably where it should be .\n->only con would be that display is not that bright , although i would say at the brightest setting is probably where it should be .\n[{'aspect': 'display', 'opinion': 'con', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n->she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the place ' s decor and hidden bathrooms made for a good laugh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place ' s decor and hidden bathrooms made for a good laugh .\n->", + "output": "{\"text\": \"the place ' s decor and hidden bathrooms made for a good laugh .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'hidden bathrooms', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n->Ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ordered the vitello alla marsala and i was pretty impressed .\n->i ordered the vitello alla marsala and i was pretty impressed .\n[{'aspect': 'vitello alla marsala', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i highly recommend visiting this restaurant and having dinner and drinks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend visiting this restaurant and having dinner and drinks !\n->", + "output": "{\"text\": \"i highly recommend visiting this restaurant and having dinner and drinks !\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n->I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n[{'aspect': 'spinach ravioli in a light oil and garlic sauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Scalina Fedeli reminded me why service is so integral to fine dining .\n->Scalina Fedeli reminded me why service is so integral to fine dining .\n[{'aspect': 'service', 'opinion': 'integral', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n->", + "output": "{\"text\": \"if you are the type of person who likes being scared and entertained , this is a great place to go and eat .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is good , I ca n't lie .\n->The food is good , I ca n't lie .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Do n't miss Bloom 's on your next trip to Manhatten .\n->Do n't miss Bloom 's on your next trip to Manhatten .\n[{'aspect': \"Bloom 's\", 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n->", + "output": "{\"text\": \"the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\", \"labels\": \"[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\n->Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\n[{'aspect': 'dishes', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon caserole', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: But the main hit was the whole grilled fish .\n->But the main hit was the whole grilled fish .\n[{'aspect': 'whole grilled fish', 'opinion': 'hit', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the entertainment was great they have shows that go on through out the dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe entertainment was great they have shows that go on through out the dinner .\n->", + "output": "{\"text\": \"the entertainment was great they have shows that go on through out the dinner .\", \"labels\": \"[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: went here last night - nice decor , good service , but the food was surprisingly excellent .\n->went here last night - nice decor , good service , but the food was surprisingly excellent .\n[{'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this place has ruined me for neighborhood sushi .\n->this place has ruined me for neighborhood sushi .\n[{'aspect': 'sushi', 'opinion': 'ruined', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: you will not be disapointed at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou will not be disapointed at all .\n->", + "output": "{\"text\": \"you will not be disapointed at all .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not be disapointed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only detail that i have to reproach is that the connection cables are a little dirty , but in acceptable condition .\n->the only detail that i have to reproach is that the connection cables are a little dirty , but in acceptable condition .\n[{'aspect': 'connection cables', 'opinion': 'dirty', 'polarity': 'neutral', 'category': 'PORTS#QUALITY'}, {'aspect': 'connection cables', 'opinion': 'acceptable', 'polarity': 'neutral', 'category': 'PORTS#QUALITY'}]\nExample:\ntext: first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n->first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n[{'aspect': 'place', 'opinion': '* not * romantic', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: we were not dissappointed in the least bit by this little gem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were not dissappointed in the least bit by this little gem .\n->", + "output": "{\"text\": \"we were not dissappointed in the least bit by this little gem .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: except , you know , when it decided to become unusable .\n->except , you know , when it decided to become unusable .\n[{'aspect': 'it', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: otherwise , build quality is very good , starts quickly , very light .\n->otherwise , build quality is very good , starts quickly , very light .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the bagel was huge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bagel was huge .\n->", + "output": "{\"text\": \"the bagel was huge .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t even think i will be able to work on this it is so distracting .\n->i don ' t even think i will be able to work on this it is so distracting .\n[{'aspect': 'NULL', 'opinion': 'distracting', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: ( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n->( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n[{'aspect': 'it', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: the workers there also absolutely load the bagel with cream cheese ( gets a little messy ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe workers there also absolutely load the bagel with cream cheese ( gets a little messy ) .\n->", + "output": "{\"text\": \"the workers there also absolutely load the bagel with cream cheese ( gets a little messy ) .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'messy', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n->Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n[{'aspect': 'Quality of food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: up until this point , asus chromebooks have been my favorite .\n->up until this point , asus chromebooks have been my favorite .\n[{'aspect': 'asus chromebooks', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i loved it and would highly recommend .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved it and would highly recommend .\n->", + "output": "{\"text\": \"i loved it and would highly recommend .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Diner food at bistro prices is a bummer ... .\n->Diner food at bistro prices is a bummer ... .\n[{'aspect': 'food', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it ' s so much faster and the mac os is so much more secure .\n->it ' s so much faster and the mac os is so much more secure .\n[{'aspect': 'NULL', 'opinion': 'faster', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac os', 'opinion': 'secure', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\ntext: this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n->", + "output": "{\"text\": \"this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i should have known better : msi has boot issues , no way around it .\n->i should have known better : msi has boot issues , no way around it .\n[{'aspect': 'msi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MOTHERBOARD#QUALITY'}]\nExample:\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n->The Steak Tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .\n->", + "output": "{\"text\": \"i have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .\", \"labels\": \"[{'aspect': 'lobster roll', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n->Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n[{'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is great , the computer is fast , and looks great with the aluminum case .\n->the screen is great , the computer is fast , and looks great with the aluminum case .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'aluminum case', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->", + "output": "{\"text\": \"other guests enjoyed pizza , santa fe chopped salad and fish and chips .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i enjoy that it has 10 key .\n->i enjoy that it has 10 key .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: battery is okay .\n->battery is okay .\n[{'aspect': 'battery', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'BATTERY#GENERAL'}]\ntext: i highly recommend cafe st . bart ' s for their food , the ambience and wonderful service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend cafe st . bart ' s for their food , the ambience and wonderful service .\n->", + "output": "{\"text\": \"i highly recommend cafe st . bart ' s for their food , the ambience and wonderful service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n->* * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n[{'aspect': 'laptop', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: nice for one time special occasion .\n->nice for one time special occasion .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: all the staff is absolutely professional ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall the staff is absolutely professional ! !\n->", + "output": "{\"text\": \"all the staff is absolutely professional ! !\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even upon delivery , their juicy pork buns are quite good . .\n->Even upon delivery , their juicy pork buns are quite good . .\n[{'aspect': 'pork buns', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve had to reset the computer multiple times .\n->i ' ve had to reset the computer multiple times .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: ( that is a must , but not every restaurant can do . . . )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( that is a must , but not every restaurant can do . . . )\n->", + "output": "{\"text\": \"( that is a must , but not every restaurant can do . . . )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'must', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n->it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n[{'aspect': 'vibe', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'french food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\ntext: there ' s nice and quiet , small but enough for 6 ( or more ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere ' s nice and quiet , small but enough for 6 ( or more ) .\n->", + "output": "{\"text\": \"there ' s nice and quiet , small but enough for 6 ( or more ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: much of the time it seems like they do not care about you .\n->much of the time it seems like they do not care about you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: battery lasts for quite sometime .\n->battery lasts for quite sometime .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: this restaurant was way overhyped .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis restaurant was way overhyped .\n->", + "output": "{\"text\": \"this restaurant was way overhyped .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'overhyped', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n->- i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n[{'aspect': 'chromebooks', 'opinion': 'worried', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: pro ' s : this chromebook is very light .\n->pro ' s : this chromebook is very light .\n[{'aspect': 'chromebook', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: my chow fun and chow see was really bland and oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy chow fun and chow see was really bland and oily .\n->", + "output": "{\"text\": \"my chow fun and chow see was really bland and oily .\", \"labels\": \"[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have recommended the place to friends , always gets good response .\n->have recommended the place to friends , always gets good response .\n[{'aspect': 'place', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: They smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n->They smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n[{'aspect': 'spinach mushroom calzone', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'canned vegetables', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}]\ntext: okay - i do n ' t mind the oily part ( cause most are cooked that way ) but it was way too bland .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nokay - i do n ' t mind the oily part ( cause most are cooked that way ) but it was way too bland .\n->", + "output": "{\"text\": \"okay - i do n ' t mind the oily part ( cause most are cooked that way ) but it was way too bland .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After dealing with subpar pizza all over the Kensington neighborhood - I 've found little toninos .\n->After dealing with subpar pizza all over the Kensington neighborhood - I 've found little toninos .\n[{'aspect': 'pizza', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The wine list is interesting and has many good values .\n->The wine list is interesting and has many good values .\n[{'aspect': 'wine list', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'good values', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the scallion pancakes and fried dumplings were nothing out of the ordinary .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe scallion pancakes and fried dumplings were nothing out of the ordinary .\n->", + "output": "{\"text\": \"the scallion pancakes and fried dumplings were nothing out of the ordinary .\", \"labels\": \"[{'aspect': 'scallion pancakes', 'opinion': 'ordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'fried dumplings', 'opinion': 'nothing out of the ordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: his response was smug , arrogant , and condescending , totally consistent with his deportment on display all evening .\n->his response was smug , arrogant , and condescending , totally consistent with his deportment on display all evening .\n[{'aspect': 'NULL', 'opinion': 'smug', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'condescending', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: 5 pound laptop with its nine hour battery life .\n->5 pound laptop with its nine hour battery life .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the service was the only thing good about this restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was the only thing good about this restaurant .\n->", + "output": "{\"text\": \"the service was the only thing good about this restaurant .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop itself seemed fine at first .\n->the laptop itself seemed fine at first .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: so good\n->so good\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it ' s boring on the inside , and our sushi was pretty below average . . . the tuna was soggy and the other rolls had no flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s boring on the inside , and our sushi was pretty below average . . . the tuna was soggy and the other rolls had no flavor .\n->", + "output": "{\"text\": \"it ' s boring on the inside , and our sushi was pretty below average . . . the tuna was soggy and the other rolls had no flavor .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'below average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'tuna', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'boring', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well designed , nice fit and finish , and the build quality seems exceptional .\n->well designed , nice fit and finish , and the build quality seems exceptional .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'build quality', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n->I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i definitely would n ' t go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni definitely would n ' t go back .\n->", + "output": "{\"text\": \"i definitely would n ' t go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fabulous decor - makes you feel like you 're in a trendy Manhattan restaurant , very very good food , cheaply-priced , generally friendly staff , and if you 're a Manhattanite , or spend most of your time in Manhattan , Rice Avenue will make you feel at home ... ..very Soho/Village/Upper West Side minus the expensive prices and pretentious clientele ... ..all on Roosevelt Avenue !\n->Fabulous decor - makes you feel like you 're in a trendy Manhattan restaurant , very very good food , cheaply-priced , generally friendly staff , and if you 're a Manhattanite , or spend most of your time in Manhattan , Rice Avenue will make you feel at home ... ..very Soho/Village/Upper West Side minus the expensive prices and pretentious clientele ... ..all on Roosevelt Avenue !\n[{'aspect': 'decor', 'opinion': 'Fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'cheaply-priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I love the atmorphere @ peep !\n->I love the atmorphere @ peep !\n[{'aspect': 'atmorphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: their pad penang is delicious and everything else is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntheir pad penang is delicious and everything else is fantastic .\n->", + "output": "{\"text\": \"their pad penang is delicious and everything else is fantastic .\", \"labels\": \"[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n->i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n[{'aspect': 'asus customer service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: The specials are usually quite good too .\n->The specials are usually quite good too .\n[{'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the price is reasonable although the service is poor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe price is reasonable although the service is poor .\n->", + "output": "{\"text\": \"the price is reasonable although the service is poor .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even though its good seafood , the prices are too high .\n->Even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the support website is incompetent .\n->the support website is incompetent .\n[{'aspect': 'support website', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: great for a romantic evening , or a fun evening with friends . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat for a romantic evening , or a fun evening with friends . . .\n->", + "output": "{\"text\": \"great for a romantic evening , or a fun evening with friends . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the ultimate tablet .\n->this is the ultimate tablet .\n[{'aspect': 'tablet', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Great bagels made the old-fashioned way .\n->Great bagels made the old-fashioned way .\n[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i will be going back very soon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will be going back very soon .\n->", + "output": "{\"text\": \"i will be going back very soon .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a decent laptop no thanks to asus support .\n->this is a decent laptop no thanks to asus support .\n[{'aspect': 'laptop', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n->as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n[{'aspect': 'cable', 'opinion': 'active', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'power led', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: keep up the good work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeep up the good work .\n->", + "output": "{\"text\": \"keep up the good work .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n->the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n[{'aspect': 'touchpad', 'opinion': 'sensitive', 'polarity': 'neutral', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n->one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n[{'aspect': 'chromeos', 'opinion': 'frustrate', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\ntext: it was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was wonderful .\n->", + "output": "{\"text\": \"it was wonderful .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is fantastic giving me over 8 hours easily with moderate usage .\n->battery life is fantastic giving me over 8 hours easily with moderate usage .\n[{'aspect': 'battery life', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: love pizza 33 . . .\n->love pizza 33 . . .\n[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the salads are delicious , both refreshing and very spicy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe salads are delicious , both refreshing and very spicy .\n->", + "output": "{\"text\": \"the salads are delicious , both refreshing and very spicy .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is a lot of variety even for people who eat vegetarian like me .\n->there is a lot of variety even for people who eat vegetarian like me .\n[{'aspect': 'NULL', 'opinion': 'a lot of variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: since i already do all of my work in google drive , this is perfect for me .\n->since i already do all of my work in google drive , this is perfect for me .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: we had pam ' s special fried fish and it was amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe had pam ' s special fried fish and it was amazing .\n->", + "output": "{\"text\": \"we had pam ' s special fried fish and it was amazing .\", \"labels\": \"[{'aspect': \"pam ' s special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n->it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n[{'aspect': 'spinach', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i also really like the finish on the case .\n->i also really like the finish on the case .\n[{'aspect': 'case', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: too large for just two people but nothing was left .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntoo large for just two people but nothing was left .\n->", + "output": "{\"text\": \"too large for just two people but nothing was left .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'too large', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , after having the computer for about 4 months it suddenly died one day and would not turn on .\n->however , after having the computer for about 4 months it suddenly died one day and would not turn on .\n[{'aspect': 'computer', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: thin , light , cool are what i feel when holding it and carry around .\n->thin , light , cool are what i feel when holding it and carry around .\n[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: great vibe , lots of people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat vibe , lots of people .\n->", + "output": "{\"text\": \"great vibe , lots of people .\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now the start up is failing .\n->now the start up is failing .\n[{'aspect': 'start up', 'opinion': 'failing', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n->i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: my husbands was perfect , my was well done and dry .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy husbands was perfect , my was well done and dry .\n->", + "output": "{\"text\": \"my husbands was perfect , my was well done and dry .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'well done', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n->Our favorite meal is a pesto pizza , the house salad , and a good bottle of wine .\n[{'aspect': 'pesto pizza', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house salad', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bottle of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The only fallback on this restaurant is the prices .\n->The only fallback on this restaurant is the prices .\n[{'aspect': 'restaurant', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i did n ' t complain , i liked the atmosphere so much .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did n ' t complain , i liked the atmosphere so much .\n->", + "output": "{\"text\": \"i did n ' t complain , i liked the atmosphere so much .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the premium feel combined with the charming simplicity of chrome os really gives it a stunning experience .\n->the premium feel combined with the charming simplicity of chrome os really gives it a stunning experience .\n[{'aspect': 'NULL', 'opinion': 'premium', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'charming', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n->the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n[{'aspect': 'computer', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'well - made', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: on a hot day it was fabulous to stop in and enjoy lunch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non a hot day it was fabulous to stop in and enjoy lunch .\n->", + "output": "{\"text\": \"on a hot day it was fabulous to stop in and enjoy lunch .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - retina display is actually sharper .\n->- retina display is actually sharper .\n[{'aspect': 'retina display', 'opinion': 'sharper', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: will not be back .\n->will not be back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nambience is so cute and quaint , good for business although we were there on vacation .\n->", + "output": "{\"text\": \"ambience is so cute and quaint , good for business although we were there on vacation .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great sushi experience .\n->great sushi experience .\n[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: customer service told me that i ' d have to buy a new one , item is still under warranty .\n->customer service told me that i ' d have to buy a new one , item is still under warranty .\n[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: salads were fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsalads were fantastic .\n->", + "output": "{\"text\": \"salads were fantastic .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cute place , nice wait staff but would never go there again .\n->Cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Drinks way over priced .\n->Drinks way over priced .\n[{'aspect': 'Drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'NULL'}]\ntext: although we were looking for regular lettuce and some walnuts the salads we got were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough we were looking for regular lettuce and some walnuts the salads we got were great .\n->", + "output": "{\"text\": \"although we were looking for regular lettuce and some walnuts the salads we got were great .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was wonderful .\n->it was wonderful .\n[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: a large is $ 20 , and toppings are about $ 3 each .\n->a large is $ 20 , and toppings are about $ 3 each .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'toppings', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: ingredients are organic which is a real plus for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ningredients are organic which is a real plus for me .\n->", + "output": "{\"text\": \"ingredients are organic which is a real plus for me .\", \"labels\": \"[{'aspect': 'ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: called acer support , they were completely useless .\n->called acer support , they were completely useless .\n[{'aspect': 'acer support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: i don ' t ask much of a laptop so i am happy with it .\n->i don ' t ask much of a laptop so i am happy with it .\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: we will be back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe will be back .\n->", + "output": "{\"text\": \"we will be back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place had all the trimmings and i mean all .\n->this place had all the trimmings and i mean all .\n[{'aspect': 'trimmings', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n->ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: the spicy tuna roll is huge and probably the best that i ' ve had at this price range .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe spicy tuna roll is huge and probably the best that i ' ve had at this price range .\n->", + "output": "{\"text\": \"the spicy tuna roll is huge and probably the best that i ' ve had at this price range .\", \"labels\": \"[{'aspect': 'spicy tuna roll', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'spicy tuna roll', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy tuna roll', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I expected quite a bit more from such an expensive menu .\n->I expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n->the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n[{'aspect': 'laptop', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'spec', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the yellowtail was particularly good as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe yellowtail was particularly good as well .\n->", + "output": "{\"text\": \"the yellowtail was particularly good as well .\", \"labels\": \"[{'aspect': 'yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was good .\n->the food was good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n->i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: i have reservations about the all you can eat deal , however - - the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have reservations about the all you can eat deal , however - - the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n->", + "output": "{\"text\": \"i have reservations about the all you can eat deal , however - - the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\", \"labels\": \"[{'aspect': 'all you can eat deal', 'opinion': 'limited', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'all you can eat deal', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the lava cake dessert was incredible and i recommend it .\n->the lava cake dessert was incredible and i recommend it .\n[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: everything looked great .\n->everything looked great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: in any event , this is a place i ' ll be sure to stop by again when i ' m in this part of town .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin any event , this is a place i ' ll be sure to stop by again when i ' m in this part of town .\n->", + "output": "{\"text\": \"in any event , this is a place i ' ll be sure to stop by again when i ' m in this part of town .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the unit is sleek , nice and the keyboard feels tactily right .\n->the unit is sleek , nice and the keyboard feels tactily right .\n[{'aspect': 'unit', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'unit', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'right', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: biggest gripe , no backlights on the keyboard .\n->biggest gripe , no backlights on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: big wong gets big ups for a fine establishment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbig wong gets big ups for a fine establishment .\n->", + "output": "{\"text\": \"big wong gets big ups for a fine establishment .\", \"labels\": \"[{'aspect': 'big wong', 'opinion': 'big ups', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'big wong', 'opinion': 'fine', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recieved prompt service with a smile .\n->i recieved prompt service with a smile .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: consequently , their burgers fell apart in their hands and made such a mess that they did ' nt feel like finishing them .\n->consequently , their burgers fell apart in their hands and made such a mess that they did ' nt feel like finishing them .\n[{'aspect': 'burgers', 'opinion': 'fell apart', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: they have it all - - great price , food , and service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey have it all - - great price , food , and service .\n->", + "output": "{\"text\": \"they have it all - - great price , food , and service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - performance can be stuttering when under heavy load .\n->- performance can be stuttering when under heavy load .\n[{'aspect': 'performance', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: nothing on it feels cheap at all .\n->nothing on it feels cheap at all .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->", + "output": "{\"text\": \"the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n->and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n[{'aspect': 'google environment', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: the food is prepared quickly and efficiently .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is prepared quickly and efficiently .\n->", + "output": "{\"text\": \"the food is prepared quickly and efficiently .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: disappointed in reliability : used in a small business operated by my wife .\n->disappointed in reliability : used in a small business operated by my wife .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: The service is decent even when this small place is packed .\n->The service is decent even when this small place is packed .\n[{'aspect': 'service', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'packed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n->", + "output": "{\"text\": \"and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'congee', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'noodles', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'rice dishes', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's simply the best meal in NYC .\n->It 's simply the best meal in NYC .\n[{'aspect': 'meal', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n->Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the takeout is great too since they give high quality tupperware as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe takeout is great too since they give high quality tupperware as well .\n->", + "output": "{\"text\": \"the takeout is great too since they give high quality tupperware as well .\", \"labels\": \"[{'aspect': 'takeout', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambiance -- relaxed and stylish .\n->Ambiance -- relaxed and stylish .\n[{'aspect': 'Ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Highly recommended is the Spicy Fried Clam Rolls and Spider Rolls .\n->Highly recommended is the Spicy Fried Clam Rolls and Spider Rolls .\n[{'aspect': 'Spicy Fried Clam Rolls', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Spider Rolls', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'NULL'}]\ntext: enjoyed a very nice caesar salad while my wife had arugula and goat cheese . . . . both very tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nenjoyed a very nice caesar salad while my wife had arugula and goat cheese . . . . both very tasty .\n->", + "output": "{\"text\": \"enjoyed a very nice caesar salad while my wife had arugula and goat cheese . . . . both very tasty .\", \"labels\": \"[{'aspect': 'caesar salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'caesar salad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'arugula and goat cheese', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am beyond impressed with this little machine , i would absolutly buy this again !\n->i am beyond impressed with this little machine , i would absolutly buy this again !\n[{'aspect': 'machine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I 'm happy to have Nosh in the neighborhood and the food is very comforting .\n->I 'm happy to have Nosh in the neighborhood and the food is very comforting .\n[{'aspect': 'food', 'opinion': 'comforting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we both opted for a pasta dish and they were served timely and fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe both opted for a pasta dish and they were served timely and fresh .\n->", + "output": "{\"text\": \"we both opted for a pasta dish and they were served timely and fresh .\", \"labels\": \"[{'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: from the camera and audio quality through to the build styling , every aspect of this pc has been meticulously considered and it represents the best chromebook that has been delivered to date\n->from the camera and audio quality through to the build styling , every aspect of this pc has been meticulously considered and it represents the best chromebook that has been delivered to date\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n->a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n[{'aspect': 'server', 'opinion': 'enhanced', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: we concluded with tiramisu chocolate cake , both were delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe concluded with tiramisu chocolate cake , both were delicious .\n->", + "output": "{\"text\": \"we concluded with tiramisu chocolate cake , both were delicious .\", \"labels\": \"[{'aspect': 'tiramisu chocolate cake', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the side dishes were passable , and i did get a refill upon request .\n->the side dishes were passable , and i did get a refill upon request .\n[{'aspect': 'side dishes', 'opinion': 'passable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the sangria was pretty tasty and good on a hot muggy day .\n->the sangria was pretty tasty and good on a hot muggy day .\n[{'aspect': 'sangria', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'sangria', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: we ' d go back again\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ' d go back again\n->", + "output": "{\"text\": \"we ' d go back again\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop charger has sparked repeatedly .\n->the laptop charger has sparked repeatedly .\n[{'aspect': 'laptop charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: there is really no excuse why it can ' t have one .\n->there is really no excuse why it can ' t have one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i recently went to this restaurant with some co - workers for lunch and had an amazing time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recently went to this restaurant with some co - workers for lunch and had an amazing time .\n->", + "output": "{\"text\": \"i recently went to this restaurant with some co - workers for lunch and had an amazing time .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'amazing time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a lot of websites don ' t work properly with the chromebook , even though you ' re using the same browser .\n->a lot of websites don ' t work properly with the chromebook , even though you ' re using the same browser .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Toons has recently been redone , so it 's now a very attractive space .\n->Toons has recently been redone , so it 's now a very attractive space .\n[{'aspect': 'space', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the staff was accomodating , the food was absolutely delicious and the place is lovely .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff was accomodating , the food was absolutely delicious and the place is lovely .\n->", + "output": "{\"text\": \"the staff was accomodating , the food was absolutely delicious and the place is lovely .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the core m3 allows this system to get fast and to stay quiet .\n->the core m3 allows this system to get fast and to stay quiet .\n[{'aspect': 'core m3', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'core m3', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: hit the power button and plug it in , it will be ready before you are .\n->hit the power button and plug it in , it will be ready before you are .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: we even had a visit from the manager who wanted to make sure we were enjoying ourselves .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe even had a visit from the manager who wanted to make sure we were enjoying ourselves .\n->", + "output": "{\"text\": \"we even had a visit from the manager who wanted to make sure we were enjoying ourselves .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: build quality seems excellent .\n->build quality seems excellent .\n[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n->The Steak Tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: yes , the prices are high , but i felt it was worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyes , the prices are high , but i felt it was worth it .\n->", + "output": "{\"text\": \"yes , the prices are high , but i felt it was worth it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'high', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the good news is that the android features ( google play store apps ) work nearly across the board .\n->the good news is that the android features ( google play store apps ) work nearly across the board .\n[{'aspect': 'android features', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: we all felt it was worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe all felt it was worth it .\n->", + "output": "{\"text\": \"we all felt it was worth it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n->Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n[{'aspect': 'Barbecued codfish', 'opinion': 'moist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seasoning', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spice rub', 'opinion': 'overwhelmed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'herb mix', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: as a computer , for typing and using internet in general , this computer is good .\n->as a computer , for typing and using internet in general , this computer is good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: sometimes i get good food and ok service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes i get good food and ok service .\n->", + "output": "{\"text\": \"sometimes i get good food and ok service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service ok but unfriendly , filthy bathroom .\n->service ok but unfriendly , filthy bathroom .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'bathroom', 'opinion': 'filthy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n->it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes i get bad food and bad service , sometimes i get good good and bad service .\n->", + "output": "{\"text\": \"sometimes i get bad food and bad service , sometimes i get good good and bad service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is a lot of fun with live entertainment and all kinds of Disney type special effects .\n->It is a lot of fun with live entertainment and all kinds of Disney type special effects .\n[{'aspect': 'live entertainment', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'special effects', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n->the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n[{'aspect': 'boths', 'opinion': 'not as small', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'boths', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: it is not consistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is not consistent .\n->", + "output": "{\"text\": \"it is not consistent .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not consistent', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is thick and slightly soggy .\n->it is thick and slightly soggy .\n[{'aspect': 'NULL', 'opinion': 'thick', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i only wish this laptop had a removable battery other than that it ' s great .\n->i only wish this laptop had a removable battery other than that it ' s great .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->", + "output": "{\"text\": \"the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n->some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n[{'aspect': 'NULL', 'opinion': 'respectable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'builds', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard dock', 'opinion': 'superior', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i just find the battery draining to quickly in my opinion .\n->i just find the battery draining to quickly in my opinion .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: and where does patis go wrong ; no where .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand where does patis go wrong ; no where .\n->", + "output": "{\"text\": \"and where does patis go wrong ; no where .\", \"labels\": \"[{'aspect': 'patis', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: save yourself the time and trouble and skip this one !\n->save yourself the time and trouble and skip this one !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Lucky Strike is a great casual place to just grab a bite to eat .\n->Lucky Strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'place', 'opinion': 'great casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n->", + "output": "{\"text\": \"you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n->Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n[{'aspect': 'sake list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Also a little more expensive than your average bagel place .\n->Also a little more expensive than your average bagel place .\n[{'aspect': 'bagel', 'opinion': 'expensive', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: right off the l in brooklyn this is a nice cozy place with good pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nright off the l in brooklyn this is a nice cozy place with good pizza .\n->", + "output": "{\"text\": \"right off the l in brooklyn this is a nice cozy place with good pizza .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Average to good Thai food , but terrible delivery .\n->Average to good Thai food , but terrible delivery .\n[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i only use the chormebook for surving the internet , checking my email , and watch hulu or netflix .\n->i only use the chormebook for surving the internet , checking my email , and watch hulu or netflix .\n[{'aspect': 'chormebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n->", + "output": "{\"text\": \"mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\", \"labels\": \"[{'aspect': 'raddichio', 'opinion': 'bitter', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the cortana !\n->love the cortana !\n[{'aspect': 'cortana', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: bottles of wine are cheap and good .\n->bottles of wine are cheap and good .\n[{'aspect': 'bottles of wine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}, {'aspect': 'bottles of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: my friend got the mushroom pizza which tasted better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy friend got the mushroom pizza which tasted better .\n->", + "output": "{\"text\": \"my friend got the mushroom pizza which tasted better .\", \"labels\": \"[{'aspect': 'mushroom pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great product build - the quality of this asus chromebook is impressive at any price , but at sub - $ 500 it ' s astounding .\n->great product build - the quality of this asus chromebook is impressive at any price , but at sub - $ 500 it ' s astounding .\n[{'aspect': 'product build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus chromebook', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'asus chromebook', 'opinion': 'astounding', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: very unsatisfied with warranty service .\n->very unsatisfied with warranty service .\n[{'aspect': 'warranty service', 'opinion': 'unsatisfied', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\ntext: the sangria was pretty tasty and good on a hot muggy day .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sangria was pretty tasty and good on a hot muggy day .\n->", + "output": "{\"text\": \"the sangria was pretty tasty and good on a hot muggy day .\", \"labels\": \"[{'aspect': 'sangria', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'sangria', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard layout is not the best , do not like that i have to press the function key to raise or lower the volume / brightness on the arrow keys .\n->keyboard layout is not the best , do not like that i have to press the function key to raise or lower the volume / brightness on the arrow keys .\n[{'aspect': 'keyboard layout is', 'opinion': 'not the best', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: excellent for those uses .\n->excellent for those uses .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n->", + "output": "{\"text\": \"kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'small', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Every course was better than the next .\n->Every course was better than the next .\n[{'aspect': 'course', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: keyboard has a nice feel to it .\n->keyboard has a nice feel to it .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: overall i would recommend it and go back again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall i would recommend it and go back again .\n->", + "output": "{\"text\": \"overall i would recommend it and go back again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything is amazing , love the look and everything about it until now .\n->everything is amazing , love the look and everything about it until now .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: my main complain involves terrible battery life .\n->my main complain involves terrible battery life .\n[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: i started out with a bombay beer which was big enough for two .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni started out with a bombay beer which was big enough for two .\n->", + "output": "{\"text\": \"i started out with a bombay beer which was big enough for two .\", \"labels\": \"[{'aspect': 'bombay beer', 'opinion': 'big', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were charged full price .\n->we were charged full price .\n[{'aspect': 'NULL', 'opinion': 'full', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: if only they delivered , they ' d make a mint !\n->if only they delivered , they ' d make a mint !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: mmmmm . . . it was delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmmmmm . . . it was delicious .\n->", + "output": "{\"text\": \"mmmmm . . . it was delicious .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - trackpad is too finicky and not my favorite\n->- trackpad is too finicky and not my favorite\n[{'aspect': 'trackpad', 'opinion': 'finicky', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: the mousepad is functional but really doesnt get in the way .\n->the mousepad is functional but really doesnt get in the way .\n[{'aspect': 'mousepad', 'opinion': 'functional', 'polarity': 'neutral', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: service was slow , but the people were friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was slow , but the people were friendly .\n->", + "output": "{\"text\": \"service was slow , but the people were friendly .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n->Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: then the asus flip c302 came into my life :\n->then the asus flip c302 came into my life :\n[{'aspect': 'asus flip c302', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: it ' s a nice place to relax and have conversation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a nice place to relax and have conversation .\n->", + "output": "{\"text\": \"it ' s a nice place to relax and have conversation .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my first chromebook , and so far ( about one month of use ) i like it .\n->my first chromebook , and so far ( about one month of use ) i like it .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: our phones are usb c so one cable does everything for me .\n->our phones are usb c so one cable does everything for me .\n[{'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'PORTS#GENERAL'}]\ntext: i ca n ' t wait to go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ca n ' t wait to go back .\n->", + "output": "{\"text\": \"i ca n ' t wait to go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n->i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: as a refurbished item it was indistinguishable from a new item .\n->as a refurbished item it was indistinguishable from a new item .\n[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the food is authentic italian - delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is authentic italian - delicious !\n->", + "output": "{\"text\": \"the food is authentic italian - delicious !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - memory is not easily upgradable anymore .\n->- memory is not easily upgradable anymore .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n->we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n[{'aspect': 'r11', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: pizza is terrific , as is homemade pasta .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npizza is terrific , as is homemade pasta .\n->", + "output": "{\"text\": \"pizza is terrific , as is homemade pasta .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'homemade', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no caps lock on the keyboard .\n->no caps lock on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n->I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n[{'aspect': 'upstairs', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ambience is delightful , service impeccable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nambience is delightful , service impeccable .\n->", + "output": "{\"text\": \"ambience is delightful , service impeccable .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The spicy Tuna roll is huge and probably the best that I 've had at this price range .\n->The spicy Tuna roll is huge and probably the best that I 've had at this price range .\n[{'aspect': 'spicy Tuna roll', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spicy Tuna roll', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n->I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n[{'aspect': 'meal', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'nice', 'polarity': 'negative', 'category': 'NULL'}]\ntext: service was also horrible and the ambience is not that great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was also horrible and the ambience is not that great .\n->", + "output": "{\"text\": \"service was also horrible and the ambience is not that great .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Big Wong gets big Ups for a fine establishment .\n->Big Wong gets big Ups for a fine establishment .\n[{'aspect': 'Big Wong', 'opinion': 'big Ups', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Big Wong', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my six year old loved it .\n->my six year old loved it .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n->", + "output": "{\"text\": \"do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love how quick this thing is .\n->i love how quick this thing is .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n->the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n[{'aspect': 'tracking pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: i go and eat out at many different restaurants and this is one place you have go and try .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni go and eat out at many different restaurants and this is one place you have go and try .\n->", + "output": "{\"text\": \"i go and eat out at many different restaurants and this is one place you have go and try .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this one is pretty , but obviously not sturdy .\n->this one is pretty , but obviously not sturdy .\n[{'aspect': 'NULL', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not sturdy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i can not imagine better indian food in all of the city .\n->i can not imagine better indian food in all of the city .\n[{'aspect': 'indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this is my first time writing a review for a restaurant because the food and service was excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my first time writing a review for a restaurant because the food and service was excellent .\n->", + "output": "{\"text\": \"this is my first time writing a review for a restaurant because the food and service was excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fish was overdone .\n->Fish was overdone .\n[{'aspect': 'Fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: amazing !\n->amazing !\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the filet mignon dish was superb !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe filet mignon dish was superb !\n->", + "output": "{\"text\": \"the filet mignon dish was superb !\", \"labels\": \"[{'aspect': 'filet mignon dish', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s the best solution i ' ve used for collaborating on a budget .\n->it ' s the best solution i ' ve used for collaborating on a budget .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: open late ( well as late as i ever got there and i ' m a night person )\n->open late ( well as late as i ever got there and i ' m a night person )\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i would defiantly come back here again as one of my top choices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would defiantly come back here again as one of my top choices .\n->", + "output": "{\"text\": \"i would defiantly come back here again as one of my top choices .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'top choices', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Waitstaff were very nice and suggested swordfish for my husband he enjoyed his meal .\n->The Waitstaff were very nice and suggested swordfish for my husband he enjoyed his meal .\n[{'aspect': 'Waitstaff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'swordfish', 'opinion': 'suggested', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - memory is not easily upgradable anymore .\n->- memory is not easily upgradable anymore .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\ntext: it ' s a small cute restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a small cute restaurant .\n->", + "output": "{\"text\": \"it ' s a small cute restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'small', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'cute', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n->the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i have been using this notebook for a month and i absolutely love it !\n->i have been using this notebook for a month and i absolutely love it !\n[{'aspect': 'notebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i absolutely love this place ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely love this place ! ! !\n->", + "output": "{\"text\": \"i absolutely love this place ! ! !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: going to bark is always worth the train ride , and will make your tongue and belly very happy !\n->going to bark is always worth the train ride , and will make your tongue and belly very happy !\n[{'aspect': 'bark', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it is an ok laptop .\n->it is an ok laptop .\n[{'aspect': 'laptop', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: i like the ambience , it ' s very dark and original .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like the ambience , it ' s very dark and original .\n->", + "output": "{\"text\": \"i like the ambience , it ' s very dark and original .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but after last night , spice grill is the only place i ' m eating indian cuisine .\n->but after last night , spice grill is the only place i ' m eating indian cuisine .\n[{'aspect': 'indian cuisine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the sushi is amazing ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sushi is amazing ! ! !\n->", + "output": "{\"text\": \"the sushi is amazing ! ! !\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n->the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n[{'aspect': 'lobster knuckles', 'opinion': 'ok', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'lobster knuckles', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n->I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n[{'aspect': 'quality of food', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: and amazingly cheap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand amazingly cheap .\n->", + "output": "{\"text\": \"and amazingly cheap .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazingly', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicious bagels , especially when right out of the oven .\n->delicious bagels , especially when right out of the oven .\n[{'aspect': 'bagels', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s boring on the inside , and our sushi was pretty below average . . . the tuna was soggy and the other rolls had no flavor .\n->it ' s boring on the inside , and our sushi was pretty below average . . . the tuna was soggy and the other rolls had no flavor .\n[{'aspect': 'sushi', 'opinion': 'below average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'tuna', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'boring', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: very affordable and excellent ambient !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery affordable and excellent ambient !\n->", + "output": "{\"text\": \"very affordable and excellent ambient !\", \"labels\": \"[{'aspect': 'ambient', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is delicious and beautifully prepared along with the friendly and personable service .\n->The food is delicious and beautifully prepared along with the friendly and personable service .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'beautifully prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'personable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Great atmoshere and worth every bit .\n->Great atmoshere and worth every bit .\n[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we ordered some beef and noodle soup dishes from the thai section of the menu but nothing we got was thai .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ordered some beef and noodle soup dishes from the thai section of the menu but nothing we got was thai .\n->", + "output": "{\"text\": \"we ordered some beef and noodle soup dishes from the thai section of the menu but nothing we got was thai .\", \"labels\": \"[{'aspect': 'beef and noodle soup dishes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * decent selection of ports for its size\n->* decent selection of ports for its size\n[{'aspect': 'ports', 'opinion': 'decent', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\nExample:\ntext: the food was average to above - average ; the french onion soup filling yet not overly impressive , and the desserts not brilliant in any way .\n->the food was average to above - average ; the french onion soup filling yet not overly impressive , and the desserts not brilliant in any way .\n[{'aspect': 'food', 'opinion': 'average to above - average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'french onion soup', 'opinion': 'not overly impressive', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'desserts', 'opinion': 'not brilliant', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: we were very disappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were very disappointed .\n->", + "output": "{\"text\": \"we were very disappointed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n->i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n[{'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n->Add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we wo n ' t go to this place again for a good meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe wo n ' t go to this place again for a good meal .\n->", + "output": "{\"text\": \"we wo n ' t go to this place again for a good meal .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it came in a regular brown box and the power cord was a bit scratched .\n->it came in a regular brown box and the power cord was a bit scratched .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: great hot dogs !\n->great hot dogs !\n[{'aspect': 'hot dogs', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: however , i think this place is a good hang out spot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , i think this place is a good hang out spot .\n->", + "output": "{\"text\": \"however , i think this place is a good hang out spot .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not a huge gamer , but it can run crysis with full mods on ultra settings and doesn ' t make so much as a light hum .\n->i ' m not a huge gamer , but it can run crysis with full mods on ultra settings and doesn ' t make so much as a light hum .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i have been going back again and again .\n->i have been going back again and again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy girlfriend and i stumbled onto this hopping place the other night and had a great time !\n->", + "output": "{\"text\": \"my girlfriend and i stumbled onto this hopping place the other night and had a great time !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it didn ' t come with the box but it came with the charger and so far , i ' ve only been using for a few days , but i have no issues with the item at all .\n->it didn ' t come with the box but it came with the charger and so far , i ' ve only been using for a few days , but i have no issues with the item at all .\n[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: * very weak wifi reception from the built - in antenna .\n->* very weak wifi reception from the built - in antenna .\n[{'aspect': 'wifi', 'opinion': 'weak', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: they tell me they are going to cover the garden in glass for the winter , so i ' m looking forward to going there on a snowy night to enjoy it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey tell me they are going to cover the garden in glass for the winter , so i ' m looking forward to going there on a snowy night to enjoy it .\n->", + "output": "{\"text\": \"they tell me they are going to cover the garden in glass for the winter , so i ' m looking forward to going there on a snowy night to enjoy it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen resolution super ( retina ) .\n->screen resolution super ( retina ) .\n[{'aspect': 'screen resolution', 'opinion': 'super', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: it does have an ips screen , battery life is going strong , and no touch pad issues .\n->it does have an ips screen , battery life is going strong , and no touch pad issues .\n[{'aspect': 'ips screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'strong', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: check this place out !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncheck this place out !\n->", + "output": "{\"text\": \"check this place out !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place . . . god where do i begin .\n->this place . . . god where do i begin .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: - windows 10 ( do i really need to list the drawbacks of 10 ?\n->- windows 10 ( do i really need to list the drawbacks of 10 ?\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: highly recommended ! ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly recommended ! ! ! ! !\n->", + "output": "{\"text\": \"highly recommended ! ! ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was very attentive , the ambience lovely , and the food superb .\n->The staff was very attentive , the ambience lovely , and the food superb .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n->Our friendly server made great food suggestions and also sent both the sommelier and the fromager to the table to help suggest different pairings for wine and cheese .\n[{'aspect': 'food suggestions', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n->", + "output": "{\"text\": \"first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\", \"labels\": \"[{'aspect': 'place', 'opinion': '* not * romantic', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great wine selection , gigondas is worth the price , and the house champagne is a great value .\n->great wine selection , gigondas is worth the price , and the house champagne is a great value .\n[{'aspect': 'wine selection', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'gigondas', 'opinion': 'worth', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n->the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n[{'aspect': 'keyboard', 'opinion': 'large', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: either that , or the editor ' s idea of romance must be sharing a conversation with the next table , while trying to speak louder than the traffic on 57th .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neither that , or the editor ' s idea of romance must be sharing a conversation with the next table , while trying to speak louder than the traffic on 57th .\n->", + "output": "{\"text\": \"either that , or the editor ' s idea of romance must be sharing a conversation with the next table , while trying to speak louder than the traffic on 57th .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it boots up instantaneously .\n->it boots up instantaneously .\n[{'aspect': 'boots up', 'opinion': 'instantaneously', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: you ' re not gon na find a deal like this too often .\n->you ' re not gon na find a deal like this too often .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the tables are crammed way too close , the menu is typical of any italian restaurant , and the wine list is simply overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe tables are crammed way too close , the menu is typical of any italian restaurant , and the wine list is simply overpriced .\n->", + "output": "{\"text\": \"the tables are crammed way too close , the menu is typical of any italian restaurant , and the wine list is simply overpriced .\", \"labels\": \"[{'aspect': 'tables', 'opinion': 'crammed', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'tables', 'opinion': 'close', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'menu', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'wine list', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hardware is still pretty sound .\n->hardware is still pretty sound .\n[{'aspect': 'hardware', 'opinion': 'sound', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: not a great place for family or general dining .\n->not a great place for family or general dining .\n[{'aspect': 'place', 'opinion': 'not a great', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nslightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n->", + "output": "{\"text\": \"slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\", \"labels\": \"[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A cool bar with great food , and tons of excellent beer .\n->A cool bar with great food , and tons of excellent beer .\n[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m giving this five stars considering the price .\n->i ' m giving this five stars considering the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: service is not what one would expect from a joint in this price category .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is not what one would expect from a joint in this price category .\n->", + "output": "{\"text\": \"service is not what one would expect from a joint in this price category .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: laptop failed after only six months of use .\n->laptop failed after only six months of use .\n[{'aspect': 'laptop', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: hands down the best pizza on the planet .\n->hands down the best pizza on the planet .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: for instance , plates were just dumped on the table , i was handed the wine list upside down , etc . . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor instance , plates were just dumped on the table , i was handed the wine list upside down , etc . . . .\n->", + "output": "{\"text\": \"for instance , plates were just dumped on the table , i was handed the wine list upside down , etc . . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was too busy ordering sushi for dinner and then laying it out to eat on the bar to even bring me my check .\n->The staff was too busy ordering sushi for dinner and then laying it out to eat on the bar to even bring me my check .\n[{'aspect': 'staff', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n->your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n[{'aspect': 'retina screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: somehow working the italian charm with constant mille grazie does not constitute proper service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsomehow working the italian charm with constant mille grazie does not constitute proper service .\n->", + "output": "{\"text\": \"somehow working the italian charm with constant mille grazie does not constitute proper service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The steak is good , the fish is good and the sushi was surprisingly great .\n->The steak is good , the fish is good and the sushi was surprisingly great .\n[{'aspect': 'steak', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the only thing i think that could be better is the volume of the speakers .\n->the only thing i think that could be better is the volume of the speakers .\n[{'aspect': 'speakers', 'opinion': 'could be better', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n->", + "output": "{\"text\": \"to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'teodora', 'opinion': 'deficiencies', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n->Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'colorful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my wife had barely touched that mess of a dish .\n->my wife had barely touched that mess of a dish .\n[{'aspect': 'dish', 'opinion': 'mess', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n->", + "output": "{\"text\": \"not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\", \"labels\": \"[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is a fanless processor so there are no vents or openings of any kind which makes this exceptionally quiet as well .\n->it is a fanless processor so there are no vents or openings of any kind which makes this exceptionally quiet as well .\n[{'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#DESIGN_FEATURES'}]\nExample:\ntext: has a long battery life .\n->has a long battery life .\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: fish was overdone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfish was overdone .\n->", + "output": "{\"text\": \"fish was overdone .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Overall , not worth the money .\n->Overall , not worth the money .\n[{'aspect': 'money', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: in total , it took 4 updates to access the google play store - - completely unacceptable !\n->in total , it took 4 updates to access the google play store - - completely unacceptable !\n[{'aspect': 'google play store', 'opinion': 'unacceptable', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: cute place , nice wait staff but would never go there again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncute place , nice wait staff but would never go there again .\n->", + "output": "{\"text\": \"cute place , nice wait staff but would never go there again .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i play a game , there is noise on the screen .\n->when i play a game , there is noise on the screen .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food was delicious but do not come here on a empty stomach .\n->the food was delicious but do not come here on a empty stomach .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: someone else recommended the dessert - we also left that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsomeone else recommended the dessert - we also left that .\n->", + "output": "{\"text\": \"someone else recommended the dessert - we also left that .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'recommended', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * track pad * - the trackpad is well done .\n->* track pad * - the trackpad is well done .\n[{'aspect': 'track pad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the acer is similar but bigger and heavier .\n->the acer is similar but bigger and heavier .\n[{'aspect': 'acer', 'opinion': 'bigger', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'acer', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: one of us actually liked the expresso - that ' s it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of us actually liked the expresso - that ' s it .\n->", + "output": "{\"text\": \"one of us actually liked the expresso - that ' s it .\", \"labels\": \"[{'aspect': 'expresso', 'opinion': 'liked', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n->they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: indoor was very cozy and cute .\n->indoor was very cozy and cute .\n[{'aspect': 'indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: skip this restaurant , it ' s a big disappointment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nskip this restaurant , it ' s a big disappointment .\n->", + "output": "{\"text\": \"skip this restaurant , it ' s a big disappointment .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'skip', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: go there to relax and feel like your somewhere else .\n->go there to relax and feel like your somewhere else .\n[{'aspect': 'NULL', 'opinion': 'relax', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The entertainment was great they have shows that go on through out the dinner .\n->The entertainment was great they have shows that go on through out the dinner .\n[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmyagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n->", + "output": "{\"text\": \"myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\", \"labels\": \"[{'aspect': 'myagi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n->I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n[{'aspect': 'Edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: excellent product , feels like quality all the way around .\n->excellent product , feels like quality all the way around .\n[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: i ' ve never had bad service and the fish is fresh and delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve never had bad service and the fish is fresh and delicious .\n->", + "output": "{\"text\": \"i ' ve never had bad service and the fish is fresh and delicious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: was able to get past setting up the log in info , but then once you log in the screen continuously goes black and comes back on and goes black and comes back on ; continuous cycle .\n->was able to get past setting up the log in info , but then once you log in the screen continuously goes black and comes back on and goes black and comes back on ; continuous cycle .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n->The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'moist not dry', 'polarity': 'positive', 'category': 'NULL'}]\ntext: their tuna tartar appetizer is to die for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntheir tuna tartar appetizer is to die for .\n->", + "output": "{\"text\": \"their tuna tartar appetizer is to die for .\", \"labels\": \"[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apps start very fast , graphics are much more responsive and capable when not being shared with chromeos and there are a number of ways you can tweak the ui / ux to your own liking .\n->apps start very fast , graphics are much more responsive and capable when not being shared with chromeos and there are a number of ways you can tweak the ui / ux to your own liking .\n[{'aspect': 'graphics', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'GRAPHICS#USABILITY'}, {'aspect': 'graphics', 'opinion': 'capable', 'polarity': 'positive', 'category': 'GRAPHICS#USABILITY'}]\nExample:\ntext: nice laptop !\n->nice laptop !\n[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n->", + "output": "{\"text\": \"i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'ashamed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do not recommend this to anyone in the gaming labtop market .\n->i do not recommend this to anyone in the gaming labtop market .\n[{'aspect': 'gaming labtop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - power button next to delete button ?\n->- power button next to delete button ?\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: the place is so cool and the service is prompt and curtious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place is so cool and the service is prompt and curtious .\n->", + "output": "{\"text\": \"the place is so cool and the service is prompt and curtious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but this one was a piece of trash .\n->but this one was a piece of trash .\n[{'aspect': 'trash', 'opinion': 'trash', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - though the case is plastic , the keyboard area itself has a cold metallic feel .\n->- though the case is plastic , the keyboard area itself has a cold metallic feel .\n[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard area', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i highly recommend to anyone to give this place a try .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend to anyone to give this place a try .\n->", + "output": "{\"text\": \"i highly recommend to anyone to give this place a try .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very pleased\n->very pleased\n[{'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i also hate the square rather than rounded edges .\n->i also hate the square rather than rounded edges .\n[{'aspect': 'edges', 'opinion': 'hate', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: you will find yourself returning quite often .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou will find yourself returning quite often .\n->", + "output": "{\"text\": \"you will find yourself returning quite often .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend this product to any one whose needs are simple and mostly web based .\n->i highly recommend this product to any one whose needs are simple and mostly web based .\n[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: my only other complaint is trackpad sensitivity .\n->my only other complaint is trackpad sensitivity .\n[{'aspect': 'trackpad sensitivity', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: a restaurant that does n ' t try to do anything except serve great food with great service in a pleasant atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na restaurant that does n ' t try to do anything except serve great food with great service in a pleasant atmosphere .\n->", + "output": "{\"text\": \"a restaurant that does n ' t try to do anything except serve great food with great service in a pleasant atmosphere .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good experience\n->good experience\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The Thali was small , thoroughly unremarkable , and $ 14.95 .\n->The Thali was small , thoroughly unremarkable , and $ 14.95 .\n[{'aspect': 'Thali', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Thali', 'opinion': 'unremarkable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: no gimmicks here - - the food speaks for itself in its freshness and preparation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno gimmicks here - - the food speaks for itself in its freshness and preparation .\n->", + "output": "{\"text\": \"no gimmicks here - - the food speaks for itself in its freshness and preparation .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'freshness', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'preparation', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n->I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n[{'aspect': 'restaurant', 'opinion': 'amazing time', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n->this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n[{'aspect': 'silver bullet', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'silver bullet', 'opinion': 'functional', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the dining room is quietly elegant with no music to shout over - - how refreshing !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dining room is quietly elegant with no music to shout over - - how refreshing !\n->", + "output": "{\"text\": \"the dining room is quietly elegant with no music to shout over - - how refreshing !\", \"labels\": \"[{'aspect': 'dining room', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'dining room', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its alright\n->its alright\n[{'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The highly spiced chai tea was great too .\n->The highly spiced chai tea was great too .\n[{'aspect': 'chai tea', 'opinion': 'highly spiced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chai tea', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n->", + "output": "{\"text\": \"the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food arrived 20 minutes after i called , cold and soggy .\n->the food arrived 20 minutes after i called , cold and soggy .\n[{'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - i wish the sound quality was better .\n->- i wish the sound quality was better .\n[{'aspect': 'sound quality', 'opinion': 'better', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: looking around , i saw a room full of new yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlooking around , i saw a room full of new yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n->", + "output": "{\"text\": \"looking around , i saw a room full of new yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'real', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'restaurant', 'opinion': 'real', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was good , the place was clean and affordable .\n->the food was good , the place was clean and affordable .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: The service is good and ambience is good for a date or group outing .\n->The service is good and ambience is good for a date or group outing .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the view is breathtaking the service is top notch . . . the ambiance is wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe view is breathtaking the service is top notch . . . the ambiance is wonderful .\n->", + "output": "{\"text\": \"the view is breathtaking the service is top notch . . . the ambiance is wonderful .\", \"labels\": \"[{'aspect': 'view', 'opinion': 'breathtaking', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ambiance', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this asus chromebook fits the bill .\n->this asus chromebook fits the bill .\n[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: product worked great until it randomly stopped charging .\n->product worked great until it randomly stopped charging .\n[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: the staff offers impeccable service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff offers impeccable service .\n->", + "output": "{\"text\": \"the staff offers impeccable service .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer can not repair whatever disc issues it has .\n->the computer can not repair whatever disc issues it has .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it does n ' t look appetizing as it ' s covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\n->it does n ' t look appetizing as it ' s covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i had lobster bisque it has 2 oz . of maine lobster in it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had lobster bisque it has 2 oz . of maine lobster in it .\n->", + "output": "{\"text\": \"i had lobster bisque it has 2 oz . of maine lobster in it .\", \"labels\": \"[{'aspect': 'lobster bisque', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the tech and the quantities ( ram , etc . )\n->the tech and the quantities ( ram , etc . )\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: lenovo should put a better battery in it , and should make a retrofit available .\n->lenovo should put a better battery in it , and should make a retrofit available .\n[{'aspect': 'better', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#DESIGN_FEATURES'}]\ntext: my boyfriend had the new england chowder it was good but i think the award should go to the lobster bisque .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy boyfriend had the new england chowder it was good but i think the award should go to the lobster bisque .\n->", + "output": "{\"text\": \"my boyfriend had the new england chowder it was good but i think the award should go to the lobster bisque .\", \"labels\": \"[{'aspect': 'new england chowder', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster bisque', 'opinion': 'award', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doubles as an android tablet and so far the experience with running android apps has been good .\n->it doubles as an android tablet and so far the experience with running android apps has been good .\n[{'aspect': 'android apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: love my macbook , beautiful and use daily !\n->love my macbook , beautiful and use daily !\n[{'aspect': 'macbook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'macbook', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it was divine melts in your mouth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was divine melts in your mouth .\n->", + "output": "{\"text\": \"it was divine melts in your mouth .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'divine', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n->We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n[{'aspect': 'desserts', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cannoli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Nice atmosphere , the service was very pleasant and the desert was good .\n->Nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'Nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my boyfriend had prime rib it was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy boyfriend had prime rib it was good .\n->", + "output": "{\"text\": \"my boyfriend had prime rib it was good .\", \"labels\": \"[{'aspect': 'prime rib', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recieved prompt service with a smile .\n->I recieved prompt service with a smile .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: on a hot day it was fabulous to stop in and enjoy lunch .\n->on a hot day it was fabulous to stop in and enjoy lunch .\n[{'aspect': 'NULL', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: we did n ' t want a bottle of bubbly on a weekday so we each got little bottles of korbett it was just enough .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe did n ' t want a bottle of bubbly on a weekday so we each got little bottles of korbett it was just enough .\n->", + "output": "{\"text\": \"we did n ' t want a bottle of bubbly on a weekday so we each got little bottles of korbett it was just enough .\", \"labels\": \"[{'aspect': 'bottles of korbett', 'opinion': 'enough', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n->certain apps ( especially flash based apps ) will get the machine very hot .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: note : i haven ' t had any issues with the touchscreen at all .\n->note : i haven ' t had any issues with the touchscreen at all .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: it ' s cuz it ' s so good you need to taste it ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s cuz it ' s so good you need to taste it ! ! !\n->", + "output": "{\"text\": \"it ' s cuz it ' s so good you need to taste it ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the internet download speed with the acer was akin to an old dial - up modem speed .\n->the internet download speed with the acer was akin to an old dial - up modem speed .\n[{'aspect': 'internet download speed with the acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .\n->Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .\n[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'NULL'}]\ntext: all in all we ' re already coming up with excuses to go ahead really soon in the next few wks ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all we ' re already coming up with excuses to go ahead really soon in the next few wks ! ! ! !\n->", + "output": "{\"text\": \"all in all we ' re already coming up with excuses to go ahead really soon in the next few wks ! ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he wanted something that could browse the internet fast - and this chromebook does just that !\n->he wanted something that could browse the internet fast - and this chromebook does just that !\n[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n->i loved this chromebook but i had to return it bevause it had sound issues .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n->", + "output": "{\"text\": \"my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price you can ' t beat a chromebook .\n->for the price you can ' t beat a chromebook .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n->I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n[{'aspect': 'Edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you like spicy food get the chicken vindaloo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you like spicy food get the chicken vindaloo .\n->", + "output": "{\"text\": \"if you like spicy food get the chicken vindaloo .\", \"labels\": \"[{'aspect': 'chicken vindaloo', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery life on the laptop is disappointing and the webcam doesn ' t work it .\n->the battery life on the laptop is disappointing and the webcam doesn ' t work it .\n[{'aspect': 'battery life on the laptop', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'webcam', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great atmoshere and worth every bit .\n->Great atmoshere and worth every bit .\n[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s very spicy but not offensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s very spicy but not offensive .\n->", + "output": "{\"text\": \"it ' s very spicy but not offensive .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not offensive', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the decor however seems to be the distraction so you wo n ' t notice that you just payed 300 bucks for some cold eggplant that took 2 frickin hours to come ! ! ! !\n->the decor however seems to be the distraction so you wo n ' t notice that you just payed 300 bucks for some cold eggplant that took 2 frickin hours to come ! ! ! !\n[{'aspect': 'decor', 'opinion': 'distraction', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'eggplant', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'eggplant', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: our food was great too !\n->our food was great too !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: we will definitely go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe will definitely go back .\n->", + "output": "{\"text\": \"we will definitely go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has everything he wanted and needs .\n->it has everything he wanted and needs .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: in the summer months , the back garden area is really nice .\n->in the summer months , the back garden area is really nice .\n[{'aspect': 'back garden area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: go to volare for 1st class service and terrific food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngo to volare for 1st class service and terrific food .\n->", + "output": "{\"text\": \"go to volare for 1st class service and terrific food .\", \"labels\": \"[{'aspect': 'service', 'opinion': '1st class', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is good .\n->the keyboard is good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: The pizza was great .\n->The pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the portions are large and the servers always surprise us with a different starter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe portions are large and the servers always surprise us with a different starter .\n->", + "output": "{\"text\": \"the portions are large and the servers always surprise us with a different starter .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n->the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n[{'aspect': 'battery life', 'opinion': 'solid', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'worth', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'quality', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'worth', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\nExample:\ntext: it has and does everything it should .\n->it has and does everything it should .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the wine list is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wine list is excellent .\n->", + "output": "{\"text\": \"the wine list is excellent .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is great and they have a good selection of wines at reasonable prices .\n->the food is great and they have a good selection of wines at reasonable prices .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wines', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: They have authentic Indian at amazin prices .\n->They have authentic Indian at amazin prices .\n[{'aspect': 'Indian', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'amazin', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->", + "output": "{\"text\": \"the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': \"chef ' s tasting menu\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n->and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n[{'aspect': 'google environment', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: For appetizers , I recommend the shrimp fritters and dumplings .\n->For appetizers , I recommend the shrimp fritters and dumplings .\n[{'aspect': 'shrimp fritters', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n->", + "output": "{\"text\": \"the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n->The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n[{'aspect': 'Bagels', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'chewy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'gummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great service , great food .\n->great service , great food .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n ' t like .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n ' t like .\n->", + "output": "{\"text\": \"the food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n ' t like .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'prixe fixe tasting menu', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'prixe fixe tasting menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great and they have a good selection of wines at reasonable prices .\n->The food is great and they have a good selection of wines at reasonable prices .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good laptop , but not great .\n->good laptop , but not great .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: please be aware that it ' s cash or amex only !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplease be aware that it ' s cash or amex only !\n->", + "output": "{\"text\": \"please be aware that it ' s cash or amex only !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is very disappointing an causing me big issues while i write .\n->this is very disappointing an causing me big issues while i write .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: Their tuna tartar appetizer is to die for .\n->Their tuna tartar appetizer is to die for .\n[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\ntext: with the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->", + "output": "{\"text\": \"with the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lemon salad', 'opinion': 'exception', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i thought i had died and gone to heaven .\n->i thought i had died and gone to heaven .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n[{'aspect': 'appetizer selection', 'opinion': 'stinks', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->", + "output": "{\"text\": \"we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\", \"labels\": \"[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is good and the resturant is clean .\n->the service is good and the resturant is clean .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'resturant', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n->In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n->", + "output": "{\"text\": \"the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'diner - ish', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n->the pizza was delivered cold and the cheese was n ' t even fully melted !\n[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n->The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: problem is nothing at prune is particularly memorable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nproblem is nothing at prune is particularly memorable .\n->", + "output": "{\"text\": \"problem is nothing at prune is particularly memorable .\", \"labels\": \"[{'aspect': 'prune', 'opinion': 'memorable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the included charger charges it very quickly though .\n->the included charger charges it very quickly though .\n[{'aspect': 'included charger', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The table next to us asked if he crushed the grapes himself when their long overdue bottle of wine finally arrived .\n->The table next to us asked if he crushed the grapes himself when their long overdue bottle of wine finally arrived .\n[{'aspect': 'bottle of wine', 'opinion': 'long overdue', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: no plans to return anytime soon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno plans to return anytime soon .\n->", + "output": "{\"text\": \"no plans to return anytime soon .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: What came to our table was burned beyond recognition and stringy .\n->What came to our table was burned beyond recognition and stringy .\n[{'aspect': 'table', 'opinion': 'burned', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Food was very good , but not what I would consider out of this world .\n->Food was very good , but not what I would consider out of this world .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: this place is so much fun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is so much fun .\n->", + "output": "{\"text\": \"this place is so much fun .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i eventually returned it .\n->i eventually returned it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The best pad thai i 've ever had .\n->The best pad thai i 've ever had .\n[{'aspect': 'pad thai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: our family never expected such incredible entertainment in a restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour family never expected such incredible entertainment in a restaurant .\n->", + "output": "{\"text\": \"our family never expected such incredible entertainment in a restaurant .\", \"labels\": \"[{'aspect': 'entertainment', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: plus the screen is matte , so bright lights are n ' t glaring .\n->plus the screen is matte , so bright lights are n ' t glaring .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n->During the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: our food was great too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour food was great too !\n->", + "output": "{\"text\": \"our food was great too !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ask for Usha , the nicest bartender in manhattan .\n->Ask for Usha , the nicest bartender in manhattan .\n[{'aspect': 'Usha', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Diner food at bistro prices is a bummer ... .\n->Diner food at bistro prices is a bummer ... .\n[{'aspect': 'food', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}]\ntext: and really large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand really large portions .\n->", + "output": "{\"text\": \"and really large portions .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the flavors are amazing and the value is phenomenal .\n->the flavors are amazing and the value is phenomenal .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: the staff was the friendliest that have seen in new york .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff was the friendliest that have seen in new york .\n->", + "output": "{\"text\": \"the staff was the friendliest that have seen in new york .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .\n->cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .\n[{'aspect': 'atmosphere', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'fire place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n->well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: if you want something really different than try jekyll and hyde .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you want something really different than try jekyll and hyde .\n->", + "output": "{\"text\": \"if you want something really different than try jekyll and hyde .\", \"labels\": \"[{'aspect': 'jekyll and hyde', 'opinion': 'different', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n->this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n[{'aspect': 'computer', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The drinks are always well made and wine selection is fairly priced .\n->The drinks are always well made and wine selection is fairly priced .\n[{'aspect': 'drinks', 'opinion': 'well made', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine selection', 'opinion': 'fairly priced', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is a lot of fun with live entertainment and all kinds of disney type special effects .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a lot of fun with live entertainment and all kinds of disney type special effects .\n->", + "output": "{\"text\": \"it is a lot of fun with live entertainment and all kinds of disney type special effects .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will return it .\n->i will return it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it is the best chromebook that i have ever used .\n->it is the best chromebook that i have ever used .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the food was pretty tradional but it was hot and good with large portions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was pretty tradional but it was hot and good with large portions .\n->", + "output": "{\"text\": \"the food was pretty tradional but it was hot and good with large portions .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tradional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'hot', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it started to get slow a week ago .\n->it started to get slow a week ago .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i highly recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend it .\n->", + "output": "{\"text\": \"i highly recommend it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n->You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'amiable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: It 's also attached to Angel 's Share , which is a cool , more romantic bar ...\n->It 's also attached to Angel 's Share , which is a cool , more romantic bar ...\n[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the place is a lot of fun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place is a lot of fun .\n->", + "output": "{\"text\": \"the place is a lot of fun .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portions are small but being that the food was so good makes up for that .\n->The portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n->one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: my six year old loved it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy six year old loved it .\n->", + "output": "{\"text\": \"my six year old loved it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i boot it up and notice quickly the display seems to be 720p ( or something close ) but for what i need this thing to do .\n->i boot it up and notice quickly the display seems to be 720p ( or something close ) but for what i need this thing to do .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n->All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n[{'aspect': 'pastas', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade lasagna', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food however , is what one might expect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food however , is what one might expect .\n->", + "output": "{\"text\": \"the food however , is what one might expect .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'expect', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good display\n->good display\n[{'aspect': 'display', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: the battery capacity is 3 hours on a full charge\n->the battery capacity is 3 hours on a full charge\n[{'aspect': 'battery capacity', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: it is very overpriced and not very tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is very overpriced and not very tasty .\n->", + "output": "{\"text\": \"it is very overpriced and not very tasty .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is small and cramped but the food is fantastic .\n->the place is small and cramped but the food is fantastic .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Pizza was a little soggy .\n->Pizza was a little soggy .\n[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: went there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwent there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .\n->", + "output": "{\"text\": \"went there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is such a lovely , peaceful place to eat outside .\n->this is such a lovely , peaceful place to eat outside .\n[{'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'peaceful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n->i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: service was slow had to wait to order and get food although not crowded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was slow had to wait to order and get food although not crowded .\n->", + "output": "{\"text\": \"service was slow had to wait to order and get food although not crowded .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sound isn ' t great on both devices but they ' ll suffice ; at least the asus speakers are side - firing and not coming from the bottom like the pro .\n->the sound isn ' t great on both devices but they ' ll suffice ; at least the asus speakers are side - firing and not coming from the bottom like the pro .\n[{'aspect': 'asus speakers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: my favorite place lol\n->my favorite place lol\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: drinks way over priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndrinks way over priced .\n->", + "output": "{\"text\": \"drinks way over priced .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food has been consistant for years and it never lets you down .\n->The food has been consistant for years and it never lets you down .\n[{'aspect': 'food', 'opinion': 'consistant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We 've been to Grocery three times and not once has an item on the menu disappointed .\n->We 've been to Grocery three times and not once has an item on the menu disappointed .\n[{'aspect': 'menu', 'opinion': 'disappointed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: food was good not great not worth the wait or another visit\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was good not great not worth the wait or another visit\n->", + "output": "{\"text\": \"food was good not great not worth the wait or another visit\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good not great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend the restaurant based on our experience last night .\n->i highly recommend the restaurant based on our experience last night .\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n->everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n[{'aspect': 'atmosphere', 'opinion': 'raved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rooms', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'views', 'opinion': 'incomparable', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: great pizza for lunch place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat pizza for lunch place .\n->", + "output": "{\"text\": \"great pizza for lunch place .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had the laptop for a full day now and i can say it is quite impressive .\n->i ' ve had the laptop for a full day now and i can say it is quite impressive .\n[{'aspect': 'laptop', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: however with that being said i bought this laptop about 3 days ago and it ' s already not working .\n->however with that being said i bought this laptop about 3 days ago and it ' s already not working .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: service was quick .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was quick .\n->", + "output": "{\"text\": \"service was quick .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: condition even better then i expect .\n->condition even better then i expect .\n[{'aspect': 'condition', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I can not imagine better Indian food in all of the city .\n->I can not imagine better Indian food in all of the city .\n[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the pizza was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pizza was great .\n->", + "output": "{\"text\": \"the pizza was great .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n->The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n[{'aspect': 'sauce', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck noodles', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i can no longer return it and have wasted $ 850 , the support refuses to get back in touch or provide any form of civility .\n->i can no longer return it and have wasted $ 850 , the support refuses to get back in touch or provide any form of civility .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: and it was quick which is very important .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand it was quick which is very important .\n->", + "output": "{\"text\": \"and it was quick which is very important .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'important', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n->we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'delight', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n->I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n[{'aspect': 'upstairs', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: have the iced tea .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave the iced tea .\n->", + "output": "{\"text\": \"have the iced tea .\", \"labels\": \"[{'aspect': 'iced tea', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: greatest thing i ' ve bought myself in a long time .\n->greatest thing i ' ve bought myself in a long time .\n[{'aspect': 'NULL', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - it freezes up depending on the program you use and you have to restart it .\n->- it freezes up depending on the program you use and you have to restart it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: whenever you need a sushi fix , mizu will be there with quality fish and great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhenever you need a sushi fix , mizu will be there with quality fish and great service .\n->", + "output": "{\"text\": \"whenever you need a sushi fix , mizu will be there with quality fish and great service .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was as they advertised .\n->it was as they advertised .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: skip dessert .\n->skip dessert .\n[{'aspect': 'dessert', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: delivery is fast too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelivery is fast too .\n->", + "output": "{\"text\": \"delivery is fast too .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was below average , the service was pathetic , there was no ambience at all .\n->The food was below average , the service was pathetic , there was no ambience at all .\n[{'aspect': 'food', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The food is great and authentic .\n->The food is great and authentic .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great friendly service , fast seating , fast delivery , excellent sushi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat friendly service , fast seating , fast delivery , excellent sushi .\n->", + "output": "{\"text\": \"great friendly service , fast seating , fast delivery , excellent sushi .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'seating', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The buffet had a nice selection .\n->The buffet had a nice selection .\n[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n->i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n[{'aspect': 'lenovo ideapad 320', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: ess - a - bagel ( either by sty - town or midtown ) is by far the best bagel in ny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ness - a - bagel ( either by sty - town or midtown ) is by far the best bagel in ny .\n->", + "output": "{\"text\": \"ess - a - bagel ( either by sty - town or midtown ) is by far the best bagel in ny .\", \"labels\": \"[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was great and the service was even better .\n->The food was great and the service was even better .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after running through the setup wizard , the laptop failed to boot .\n->after running through the setup wizard , the laptop failed to boot .\n[{'aspect': 'laptop', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->", + "output": "{\"text\": \"the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: decent wine at reasonable prices .\n->decent wine at reasonable prices .\n[{'aspect': 'wine', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: it is an okay laptop and nothing more .\n->it is an okay laptop and nothing more .\n[{'aspect': 'laptop', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: they have a huge selection of different cream cheeses and all of their salads are great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey have a huge selection of different cream cheeses and all of their salads are great .\n->", + "output": "{\"text\": \"they have a huge selection of different cream cheeses and all of their salads are great .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a real dissapointment .\n->a real dissapointment .\n[{'aspect': 'NULL', 'opinion': 'dissapointment', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n->the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n[{'aspect': 'computer', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'well - made', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: the lox is always fresh too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe lox is always fresh too .\n->", + "output": "{\"text\": \"the lox is always fresh too .\", \"labels\": \"[{'aspect': 'lox', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We have been to this place many times , and always have great food , wine , and service .\n->We have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: when i got there the place was packed but they made sure to seat me quickly .\n->when i got there the place was packed but they made sure to seat me quickly .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: highly recommended to all !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly recommended to all !\n->", + "output": "{\"text\": \"highly recommended to all !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen resolution super ( retina ) .\n->screen resolution super ( retina ) .\n[{'aspect': 'screen resolution', 'opinion': 'super', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n->For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: not impressed with the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot impressed with the food .\n->", + "output": "{\"text\": \"not impressed with the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'not impressed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the temperatures were good , and the overall responsiveness of the system was fine .\n->the temperatures were good , and the overall responsiveness of the system was fine .\n[{'aspect': 'temperatures', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'responsiveness of the system', 'opinion': 'fine', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: * excellent form factor , extremely portable while remaining a serious pro computer\n->* excellent form factor , extremely portable while remaining a serious pro computer\n[{'aspect': 'pro computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro computer', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: prices too high for this cramped and unappealing resturant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprices too high for this cramped and unappealing resturant .\n->", + "output": "{\"text\": \"prices too high for this cramped and unappealing resturant .\", \"labels\": \"[{'aspect': 'resturant', 'opinion': 'high', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'resturant', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'resturant', 'opinion': 'unappealing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The ambience was nice , but service was n't so great .\n->The ambience was nice , but service was n't so great .\n[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': \"was n't so great\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: zero ambiance to boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nzero ambiance to boot .\n->", + "output": "{\"text\": \"zero ambiance to boot .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'zero', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recently went to this restaurant with some co - workers for lunch and had an amazing time .\n->i recently went to this restaurant with some co - workers for lunch and had an amazing time .\n[{'aspect': 'restaurant', 'opinion': 'amazing time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n->the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i thought this place was totally overrated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni thought this place was totally overrated .\n->", + "output": "{\"text\": \"i thought this place was totally overrated .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve been there three times and have always had wonderful experiences .\n->i ' ve been there three times and have always had wonderful experiences .\n[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n->My wife and I will usually only order one primi and one secondi and split them , as they tend to offer large portions .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: everything we had was good or ok . . . . but definitely nothing great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything we had was good or ok . . . . but definitely nothing great .\n->", + "output": "{\"text\": \"everything we had was good or ok . . . . but definitely nothing great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we concluded with tiramisu chocolate cake , both were delicious .\n->we concluded with tiramisu chocolate cake , both were delicious .\n[{'aspect': 'tiramisu chocolate cake', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The location and ambience is Ok but the food is what makes up for it .\n->The location and ambience is Ok but the food is what makes up for it .\n[{'aspect': 'location', 'opinion': 'Ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'Ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'makes up', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the ambience was nice , but service was n ' t so great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe ambience was nice , but service was n ' t so great .\n->", + "output": "{\"text\": \"the ambience was nice , but service was n ' t so great .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': \"was n ' t so great\", 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place survives on reputation alone .\n->this place survives on reputation alone .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the only thing i don ' t like is that the power button sits beside the delete key .\n->the only thing i don ' t like is that the power button sits beside the delete key .\n[{'aspect': 'power button', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: when you add it all together , it just does n ' t seem worth it to me . . . especially considering the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen you add it all together , it just does n ' t seem worth it to me . . . especially considering the prices .\n->", + "output": "{\"text\": \"when you add it all together , it just does n ' t seem worth it to me . . . especially considering the prices .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': \"does n ' t seem worth\", 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': \"does n ' t seem worth\", 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n->While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n[{'aspect': 'room', 'opinion': 'not particularly comfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i am reluctant to write because i would not want my jem of a pizza place to become overcrowded .\n->i am reluctant to write because i would not want my jem of a pizza place to become overcrowded .\n[{'aspect': 'pizza place', 'opinion': 'overcrowded', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: this is the best shabu - shabu restaurant in the try - state area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the best shabu - shabu restaurant in the try - state area .\n->", + "output": "{\"text\": \"this is the best shabu - shabu restaurant in the try - state area .\", \"labels\": \"[{'aspect': 'shabu - shabu restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n->sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n[{'aspect': 'server', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: no dvd drive , but who uses those anymore anyway ?\n->no dvd drive , but who uses those anymore anyway ?\n[{'aspect': 'dvd drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\ntext: thius is a must for anyone who loves shabu - shabu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthius is a must for anyone who loves shabu - shabu .\n->", + "output": "{\"text\": \"thius is a must for anyone who loves shabu - shabu .\", \"labels\": \"[{'aspect': 'shabu - shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n->on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n[{'aspect': 'trackpad', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: battery is all day amazing\n->battery is all day amazing\n[{'aspect': 'battery', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n->", + "output": "{\"text\": \"the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Admittedly , this is not the place for gigantic pieces of fish overflowing the plate ( and thank goodness , in my opinion ) but for simple , elegant sushi there is no better place in New York or anywhere in the US .\n->Admittedly , this is not the place for gigantic pieces of fish overflowing the plate ( and thank goodness , in my opinion ) but for simple , elegant sushi there is no better place in New York or anywhere in the US .\n[{'aspect': 'sushi', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The production is a symphony , alot of fun to experience.The food sublime for the most part .\n->The production is a symphony , alot of fun to experience.The food sublime for the most part .\n[{'aspect': 'food', 'opinion': 'sublime', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the owner and staff are all japanese as well and that adds to the entire ambiance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe owner and staff are all japanese as well and that adds to the entire ambiance .\n->", + "output": "{\"text\": \"the owner and staff are all japanese as well and that adds to the entire ambiance .\", \"labels\": \"[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'ambiance', 'opinion': 'adds', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chicken lollipop is my favorite , most of the dishes ( i have to agree with a previous reviewer ) are quite oily and very spicy , espeically the chilli chicken .\n->the chicken lollipop is my favorite , most of the dishes ( i have to agree with a previous reviewer ) are quite oily and very spicy , espeically the chilli chicken .\n[{'aspect': 'chicken lollipop', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chilli chicken', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chilli chicken', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: then the light bleed becomes annoying and distracting .\n->then the light bleed becomes annoying and distracting .\n[{'aspect': 'light bleed', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'light bleed', 'opinion': 'distracting', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndespite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n->", + "output": "{\"text\": \"despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'done to perfection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n->the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n[{'aspect': 'responses', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: i have been going there since it opened and i ca n ' t get enough .\n->i have been going there since it opened and i ca n ' t get enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i must give it yon out of yon stars !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni must give it yon out of yon stars !\n->", + "output": "{\"text\": \"i must give it yon out of yon stars !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: beware that stains in the coating of the display have been detected in all of the macbook retina editions .\n->beware that stains in the coating of the display have been detected in all of the macbook retina editions .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: You can certainly find restaurants that offer a superior fine dining experience , but for superb food at reasonable prices , La Villa ca n't be beat .\n->You can certainly find restaurants that offer a superior fine dining experience , but for superb food at reasonable prices , La Villa ca n't be beat .\n[{'aspect': 'food', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: taxan delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntaxan delicious !\n->", + "output": "{\"text\": \"taxan delicious !\", \"labels\": \"[{'aspect': 'taxan', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n->If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n[{'aspect': 'ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n->the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n[{'aspect': 'restaurant', 'opinion': 'family feel', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'portions', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'veal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: this restaurant was recommended by a local . . . as a matter of fact , he made the reservation for us and said we would not be disappointed !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis restaurant was recommended by a local . . . as a matter of fact , he made the reservation for us and said we would not be disappointed !\n->", + "output": "{\"text\": \"this restaurant was recommended by a local . . . as a matter of fact , he made the reservation for us and said we would not be disappointed !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is ok , some of the people did n ' t get what they asked for .\n->the service is ok , some of the people did n ' t get what they asked for .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n->Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n[{'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: we were n ' t !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were n ' t !\n->", + "output": "{\"text\": \"we were n ' t !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice laptop !\n->nice laptop !\n[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n->My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n[{'aspect': 'spinach', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shanghai low mein', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the prices were cheap compared to the quality of service and food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe prices were cheap compared to the quality of service and food .\n->", + "output": "{\"text\": \"the prices were cheap compared to the quality of service and food .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after that , it ' s actually been running well .\n->after that , it ' s actually been running well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n->on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power', 'opinion': 'failed', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: we liked it so much , that we will always make it a point to dine here when we visit new york .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe liked it so much , that we will always make it a point to dine here when we visit new york .\n->", + "output": "{\"text\": \"we liked it so much , that we will always make it a point to dine here when we visit new york .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'liked', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the price is excellent for what you get .\n->and the price is excellent for what you get .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i got this laptop 2 days ago and it says plugged in , not charged .\n->i got this laptop 2 days ago and it says plugged in , not charged .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the location and ambience is ok but the food is what makes up for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe location and ambience is ok but the food is what makes up for it .\n->", + "output": "{\"text\": \"the location and ambience is ok but the food is what makes up for it .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LOCATION#GENERAL'}, {'aspect': 'ambience', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n->needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: other than that it has been great so far !\n->other than that it has been great so far !\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: there is a lot of variety even for people who eat vegetarian like me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is a lot of variety even for people who eat vegetarian like me .\n->", + "output": "{\"text\": \"there is a lot of variety even for people who eat vegetarian like me .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'a lot of variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n->5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n[{'aspect': 'emmc storage', 'opinion': 'slower', 'polarity': 'negative', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: - the hard drive is really slow and really loud .\n->- the hard drive is really slow and really loud .\n[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'hard drive', 'opinion': 'loud', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: try green curry with vegetables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry green curry with vegetables .\n->", + "output": "{\"text\": \"try green curry with vegetables .\", \"labels\": \"[{'aspect': 'green curry with vegetables', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 1 month update : chromebook is still working great .\n->1 month update : chromebook is still working great .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n->to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'teodora', 'opinion': 'deficiencies', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the quantity is also very good , you will come out satisfied .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe quantity is also very good , you will come out satisfied .\n->", + "output": "{\"text\": \"the quantity is also very good , you will come out satisfied .\", \"labels\": \"[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fans run more often on the latest version .\n->- fans run more often on the latest version .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}]\nExample:\ntext: so far the performance has been spectacular !\n->so far the performance has been spectacular !\n[{'aspect': 'performance', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the service is ok , some of the people did n ' t get what they asked for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service is ok , some of the people did n ' t get what they asked for .\n->", + "output": "{\"text\": \"the service is ok , some of the people did n ' t get what they asked for .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Dessert - ca n't be missed , so save room ! ! !\n->Dessert - ca n't be missed , so save room ! ! !\n[{'aspect': 'Dessert', 'opinion': \"ca n't be missed\", 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i have the intel core i7 , soooo fast !\n->i have the intel core i7 , soooo fast !\n[{'aspect': 'intel core i7', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: i was there on sat . for my birthday and we had an excellent time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was there on sat . for my birthday and we had an excellent time .\n->", + "output": "{\"text\": \"i was there on sat . for my birthday and we had an excellent time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i stumbled upon this great pizzeria as i explored my new neighborhood .\n->i stumbled upon this great pizzeria as i explored my new neighborhood .\n[{'aspect': 'pizzeria', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the mousepad was not very responsive .\n->the mousepad was not very responsive .\n[{'aspect': 'mousepad', 'opinion': 'not very responsive', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: i had the best ravioli ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had the best ravioli ever .\n->", + "output": "{\"text\": \"i had the best ravioli ever .\", \"labels\": \"[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: maggot in the food !\n->maggot in the food !\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i would highly recommend it .\n->i would highly recommend it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the wine the service was very good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wine the service was very good too .\n->", + "output": "{\"text\": \"the wine the service was very good too .\", \"labels\": \"[{'aspect': 'wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: What is even better , is that the prices are very affordable as well , and the food is really good .\n->What is even better , is that the prices are very affordable as well , and the food is really good .\n[{'aspect': 'prices', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n->while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n[{'aspect': 'NULL', 'opinion': 'uncourteous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: moderate prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmoderate prices .\n->", + "output": "{\"text\": \"moderate prices .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fans run more often on the latest version .\n->- fans run more often on the latest version .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}]\nExample:\ntext: Friendly staff that actually lets you enjoy your meal and the company you 're with .\n->Friendly staff that actually lets you enjoy your meal and the company you 're with .\n[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a little noise but i think that was because of our party !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na little noise but i think that was because of our party !\n->", + "output": "{\"text\": \"a little noise but i think that was because of our party !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i contacted asus and they could do nothing .\n->i contacted asus and they could do nothing .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: to get the features like this good luck .\n->to get the features like this good luck .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis quaint and romantic trattoria is at the top of my manhattan restaurant list .\n->", + "output": "{\"text\": \"this quaint and romantic trattoria is at the top of my manhattan restaurant list .\", \"labels\": \"[{'aspect': 'trattoria', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'trattoria', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for my daughter for school and she loves it .\n->i bought this for my daughter for school and she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: always busy but fast moving .\n->always busy but fast moving .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the food is delicious - from the specials to the regular menu - fare , the dishes are never a disappointment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is delicious - from the specials to the regular menu - fare , the dishes are never a disappointment .\n->", + "output": "{\"text\": \"the food is delicious - from the specials to the regular menu - fare , the dishes are never a disappointment .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'specials', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'regular menu - fare', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'never a disappointment', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 8th generation processor and ssd make for a very snappy computer , and the easy upgradablility is helpful now and will be useful in the future .\n->the 8th generation processor and ssd make for a very snappy computer , and the easy upgradablility is helpful now and will be useful in the future .\n[{'aspect': 'computer', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': '8th generation processor', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: it melted in my little mouth and the perfect consistency - not too fishy , creamy , and slightly buttery .\n->it melted in my little mouth and the perfect consistency - not too fishy , creamy , and slightly buttery .\n[{'aspect': 'NULL', 'opinion': 'perfect consistency', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: whether it ' s the parmesean porcini souffle or the lamb glazed with balsamic vinegar , you will surely be transported to northern italy with one bite .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhether it ' s the parmesean porcini souffle or the lamb glazed with balsamic vinegar , you will surely be transported to northern italy with one bite .\n->", + "output": "{\"text\": \"whether it ' s the parmesean porcini souffle or the lamb glazed with balsamic vinegar , you will surely be transported to northern italy with one bite .\", \"labels\": \"[{'aspect': 'parmesean porcini souffle', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb glazed with balsamic vinegar', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Tuk Tuk is one of those comfortable neighborhood joints where you know you will always have a good meal at a fair price .\n->Tuk Tuk is one of those comfortable neighborhood joints where you know you will always have a good meal at a fair price .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'fair', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n->the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n[{'aspect': 'ambience', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: although the tables may be closely situated , the candle - light , food - quality and service overcompensate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough the tables may be closely situated , the candle - light , food - quality and service overcompensate .\n->", + "output": "{\"text\": \"although the tables may be closely situated , the candle - light , food - quality and service overcompensate .\", \"labels\": \"[{'aspect': 'tables', 'opinion': 'closely situated', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'candle - light', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: purchased it the first time , thought it was faulty hardware , returned for a replacement of the exact same , and it had the exact problems .\n->purchased it the first time , thought it was faulty hardware , returned for a replacement of the exact same , and it had the exact problems .\n[{'aspect': 'hardware', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: We always have a delicious meal and always leave feeling satisfied .\n->We always have a delicious meal and always leave feeling satisfied .\n[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a guaranteeed delight !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na guaranteeed delight !\n->", + "output": "{\"text\": \"a guaranteeed delight !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'delight', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is some really good , inexpensive sushi .\n->This is some really good , inexpensive sushi .\n[{'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We wo n't go to this place again for a good meal .\n->We wo n't go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i have known about this secret for the last 13 years , emilio ( the godfather ) has continued to serve food and wine for the gods at mortal prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have known about this secret for the last 13 years , emilio ( the godfather ) has continued to serve food and wine for the gods at mortal prices .\n->", + "output": "{\"text\": \"i have known about this secret for the last 13 years , emilio ( the godfather ) has continued to serve food and wine for the gods at mortal prices .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'wine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nice ambiance , nice little bar , good bartender , Francois , and good service .\n->Nice ambiance , nice little bar , good bartender , Francois , and good service .\n[{'aspect': 'bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bartender', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good food .\n->good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: if you go , try the marinara / arrabiatta sauce , the mozzarella en carozza is mmmmmmmm . . . . . everything is just delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you go , try the marinara / arrabiatta sauce , the mozzarella en carozza is mmmmmmmm . . . . . everything is just delicious .\n->", + "output": "{\"text\": \"if you go , try the marinara / arrabiatta sauce , the mozzarella en carozza is mmmmmmmm . . . . . everything is just delicious .\", \"labels\": \"[{'aspect': 'marinara / arrabiatta sauce', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'marinara / arrabiatta sauce', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: average to good thai food , but terrible delivery .\n->average to good thai food , but terrible delivery .\n[{'aspect': 'thai food', 'opinion': 'average to good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: tapping it on either end is hit or miss .\n->tapping it on either end is hit or miss .\n[{'aspect': 'tapping', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i pray it stays open forever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni pray it stays open forever .\n->", + "output": "{\"text\": \"i pray it stays open forever .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is a lot of fun with live entertainment and all kinds of disney type special effects .\n->it is a lot of fun with live entertainment and all kinds of disney type special effects .\n[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n->The hanger steak was like rubber and the tuna was flavorless not to mention it tasted like it had just been thawed .\n[{'aspect': 'hanger steak', 'opinion': 'rubber', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tuna', 'opinion': 'flavorless', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i found the food , service and value exceptional everytime i have been there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni found the food , service and value exceptional everytime i have been there .\n->", + "output": "{\"text\": \"i found the food , service and value exceptional everytime i have been there .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other than that it ' s everything i imagined and more .\n->other than that it ' s everything i imagined and more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - the touchscreen works great and is very responsive .\n->- the touchscreen works great and is very responsive .\n[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: i felt as though i were eating in paris .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni felt as though i were eating in paris .\n->", + "output": "{\"text\": \"i felt as though i were eating in paris .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There was a small wait , but shorter than I expected .\n->There was a small wait , but shorter than I expected .\n[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i have to say , this is a very nice product .\n->i have to say , this is a very nice product .\n[{'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the service was excellent - friendly and attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was excellent - friendly and attentive .\n->", + "output": "{\"text\": \"the service was excellent - friendly and attentive .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i took one look at the chicken and i was appalled .\n->i took one look at the chicken and i was appalled .\n[{'aspect': 'chicken', 'opinion': 'appalled', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: this staff should be fired .\n->this staff should be fired .\n[{'aspect': 'staff', 'opinion': 'fired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the prices are wonderfully low .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe prices are wonderfully low .\n->", + "output": "{\"text\": \"the prices are wonderfully low .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'wonderfully low', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was good too .\n->The food was good too .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: so far no problems except i tried to print something from my email and am having trouble linking to my printer .\n->so far no problems except i tried to print something from my email and am having trouble linking to my printer .\n[{'aspect': 'printer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: very good wine choices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good wine choices .\n->", + "output": "{\"text\": \"very good wine choices .\", \"labels\": \"[{'aspect': 'wine choices', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Yellowtail was particularly good as well .\n->The Yellowtail was particularly good as well .\n[{'aspect': 'Yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n->my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n[{'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: who has room for cheesesticks with the best pizza in nyc !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwho has room for cheesesticks with the best pizza in nyc !\n->", + "output": "{\"text\": \"who has room for cheesesticks with the best pizza in nyc !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the food was fantastic .\n->And the food was fantastic .\n[{'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great chromebook .\n->great chromebook .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: get the pepperoni - yum - and a family style salad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nget the pepperoni - yum - and a family style salad .\n->", + "output": "{\"text\": \"get the pepperoni - yum - and a family style salad .\", \"labels\": \"[{'aspect': 'pepperoni', 'opinion': 'yum', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'family style salad', 'opinion': 'yum', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great deal on a great computer !\n->great deal on a great computer !\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' m very happy i chose this unit .\n->i ' m very happy i chose this unit .\n[{'aspect': 'unit', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat staff .\n->", + "output": "{\"text\": \"great staff .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: beef noodle soup is good as well .\n->beef noodle soup is good as well .\n[{'aspect': 'beef noodle soup', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: If you 're craving for Haru 's great food , especially the House Roll , but ca n't stand the wait building outisde , head across the street to their Sake Bar !\n->If you 're craving for Haru 's great food , especially the House Roll , but ca n't stand the wait building outisde , head across the street to their Sake Bar !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: always great service !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalways great service !\n->", + "output": "{\"text\": \"always great service !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have to say , this is a very nice product .\n->i have to say , this is a very nice product .\n[{'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s a pretty good laptop .\n->it ' s a pretty good laptop .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i go twice a month !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni go twice a month !\n->", + "output": "{\"text\": \"i go twice a month !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i think i will forever be a mac user from now on , it is an awesome product !\n->i think i will forever be a mac user from now on , it is an awesome product !\n[{'aspect': 'product', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The owner and staff are all Japanese as well and that adds to the entire ambiance .\n->The owner and staff are all Japanese as well and that adds to the entire ambiance .\n[{'aspect': 'ambiance', 'opinion': 'adds', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food is good , i ca n ' t lie .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is good , i ca n ' t lie .\n->", + "output": "{\"text\": \"the food is good , i ca n ' t lie .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n->not the food , not the ambiance , not the service , I agree with the previous reviews you wait and wait , the wait staff are very rude and when you get in they are looking to get you right out .\n[{'aspect': 'wait staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the menu has so many fish items and oysters .\n->the menu has so many fish items and oysters .\n[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the hostess and the waitress were incredibly rude and did everything they could to rush us out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hostess and the waitress were incredibly rude and did everything they could to rush us out .\n->", + "output": "{\"text\": \"the hostess and the waitress were incredibly rude and did everything they could to rush us out .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitress', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am much more productive with this machine .\n->i am much more productive with this machine .\n[{'aspect': 'machine', 'opinion': 'productive', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the sashimi is always fresh and the rolls are innovative and delicious .\n->the sashimi is always fresh and the rolls are innovative and delicious .\n[{'aspect': 'sashimi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: we were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .\n->", + "output": "{\"text\": \"we were planning to get dessert but the waitress basically through the bill at us before we had a chance to order .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n->i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the body of the chromebook feels solid due to the aluminium body .\n->the body of the chromebook feels solid due to the aluminium body .\n[{'aspect': 'body of the chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: this place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .\n->", + "output": "{\"text\": \"this place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'the', 'opinion': 'is', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'the', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the style , aluminum shell , 14 inches monitor , and decent resolution .\n->love the style , aluminum shell , 14 inches monitor , and decent resolution .\n[{'aspect': 'style', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'aluminum shell', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '14 inches monitor', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n->it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n[{'aspect': 'NULL', 'opinion': 'not really bad', 'polarity': 'neutral', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: save yourself the time and trouble and skip this one !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsave yourself the time and trouble and skip this one !\n->", + "output": "{\"text\": \"save yourself the time and trouble and skip this one !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Amma is nothing special .\n->Amma is nothing special .\n[{'aspect': 'Amma', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: So , for good food i 'd recommend it , but not for a fun night out .\n->So , for good food i 'd recommend it , but not for a fun night out .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: amma is nothing special .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namma is nothing special .\n->", + "output": "{\"text\": \"amma is nothing special .\", \"labels\": \"[{'aspect': 'amma', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n->if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n[{'aspect': 'corona', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: good quality all around hardware + software , of course that is what apple is known for .\n->good quality all around hardware + software , of course that is what apple is known for .\n[{'aspect': 'hardware', 'opinion': 'good', 'polarity': 'positive', 'category': 'HARDWARE#QUALITY'}, {'aspect': 'software', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#QUALITY'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: i ate here a week ago and found most dishes average at best and too expensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ate here a week ago and found most dishes average at best and too expensive .\n->", + "output": "{\"text\": \"i ate here a week ago and found most dishes average at best and too expensive .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'average at best', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not super confident this asus unit will last half that long .\n->not super confident this asus unit will last half that long .\n[{'aspect': 'asus unit', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: will not be back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill not be back .\n->", + "output": "{\"text\": \"will not be back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n->They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n[{'aspect': 'RICE', 'opinion': 'BURNT', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i heard the lobster roll was excellent .\n->i heard the lobster roll was excellent .\n[{'aspect': 'lobster roll', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: do n ' t dine at tamarind for the vegetarian dishes , they are simply not up to par with the non - veg selections .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo n ' t dine at tamarind for the vegetarian dishes , they are simply not up to par with the non - veg selections .\n->", + "output": "{\"text\": \"do n ' t dine at tamarind for the vegetarian dishes , they are simply not up to par with the non - veg selections .\", \"labels\": \"[{'aspect': 'vegetarian dishes', 'opinion': 'not up to par', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'non - veg selections', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this does exactly what i need , writing on google docs .\n->this does exactly what i need , writing on google docs .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: The restuarant itself is not large , but seems to have several round tables to accomodate large groups hoping to save a buck to eat authentic Taiwanese .\n->The restuarant itself is not large , but seems to have several round tables to accomodate large groups hoping to save a buck to eat authentic Taiwanese .\n[{'aspect': 'round tables', 'opinion': 'several', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Taiwanese', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this place is always packed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is always packed .\n->", + "output": "{\"text\": \"this place is always packed .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n->today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food is outstanding and the service is quick , friendly and very professional .\n->the food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: most importantly , food is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmost importantly , food is excellent .\n->", + "output": "{\"text\": \"most importantly , food is excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far it ' s running smooth with no issues as if it was new .\n->so far it ' s running smooth with no issues as if it was new .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Good food .\n->Good food .\n[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: try the sea bass .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry the sea bass .\n->", + "output": "{\"text\": \"try the sea bass .\", \"labels\": \"[{'aspect': 'sea bass', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very good to use in korea .\n->it is very good to use in korea .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: This place is so much fun .\n->This place is so much fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\ntext: highly recommended .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly recommended .\n->", + "output": "{\"text\": \"highly recommended .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i agree that dining at casa la femme is like no other dining experience !\n->i agree that dining at casa la femme is like no other dining experience !\n[{'aspect': 'casa la femme', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: will not be back .\n->will not be back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: first of all dal bukhara rocks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst of all dal bukhara rocks .\n->", + "output": "{\"text\": \"first of all dal bukhara rocks .\", \"labels\": \"[{'aspect': 'dal bukhara rocks .', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the extra ram , however , is great for everyone , as it makes this device more capable of running many tabs , or handling higher demand tasks like streaming content , without tabs crashing out or caching / reloading .\n->the extra ram , however , is great for everyone , as it makes this device more capable of running many tabs , or handling higher demand tasks like streaming content , without tabs crashing out or caching / reloading .\n[{'aspect': 'ram', 'opinion': 'great', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n->I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i am happy i did the food was awsome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am happy i did the food was awsome .\n->", + "output": "{\"text\": \"i am happy i did the food was awsome .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The exotic food is beautifully presented and is a delight in delicious combinations .\n->The exotic food is beautifully presented and is a delight in delicious combinations .\n[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Acceptable prices .\n->Acceptable prices .\n[{'aspect': 'prices', 'opinion': 'Acceptable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: and yes dal bukhara is so dam good and so are all the kababs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand yes dal bukhara is so dam good and so are all the kababs .\n->", + "output": "{\"text\": \"and yes dal bukhara is so dam good and so are all the kababs .\", \"labels\": \"[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dal bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service not the friendliest to our ` ` large party ' ' !\n->service not the friendliest to our ` ` large party ' ' !\n[{'aspect': 'service', 'opinion': 'not the friendliest', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n->Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n[{'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but overall i give it a 10\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut overall i give it a 10\n->", + "output": "{\"text\": \"but overall i give it a 10\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - screen feels smaller than other of the same size .\n->- screen feels smaller than other of the same size .\n[{'aspect': 'screen', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: this little computer is awesome and it was so inexpensive for what you get !\n->this little computer is awesome and it was so inexpensive for what you get !\n[{'aspect': 'computer', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: 10\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n10\n->", + "output": "{\"text\": \"10\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n->The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n[{'aspect': 'wine list', 'opinion': \"is n't great\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'not as good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The service is a little scatty at times but all is forgiven when the food arrives .\n->The service is a little scatty at times but all is forgiven when the food arrives .\n[{'aspect': 'service', 'opinion': 'scatty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'forgiven', 'polarity': 'positive', 'category': 'NULL'}]\ntext: haru on park s is simply disgusting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nharu on park s is simply disgusting .\n->", + "output": "{\"text\": \"haru on park s is simply disgusting .\", \"labels\": \"[{'aspect': 'haru on park s', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - this chromebook has access to the android beta channel for android apps\n->- this chromebook has access to the android beta channel for android apps\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n->i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the fish was not fresh and the rice tasted old and stale .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fish was not fresh and the rice tasted old and stale .\n->", + "output": "{\"text\": \"the fish was not fresh and the rice tasted old and stale .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rice', 'opinion': 'old', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rice', 'opinion': 'stale', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they charge different prices all the time .\n->they charge different prices all the time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: however , after a couple of months the keyboard case started to crack at the corner .\n->however , after a couple of months the keyboard case started to crack at the corner .\n[{'aspect': 'keyboard case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: quite frankly , this is some of the worst sushi i have ever tried .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nquite frankly , this is some of the worst sushi i have ever tried .\n->", + "output": "{\"text\": \"quite frankly , this is some of the worst sushi i have ever tried .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i * love * it !\n->i * love * it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: service was slow had to wait to order and get food although not crowded .\n->service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: will never be back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill never be back .\n->", + "output": "{\"text\": \"will never be back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the webcam sucks but i don ' t care about that .\n->- the webcam sucks but i don ' t care about that .\n[{'aspect': 'webcam', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: Pizza was a little soggy .\n->Pizza was a little soggy .\n[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: honestly the worst sushi my husband and i had in our entire lives .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhonestly the worst sushi my husband and i had in our entire lives .\n->", + "output": "{\"text\": \"honestly the worst sushi my husband and i had in our entire lives .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stepped on my foot on the second time he reached over me to adjust lighting .\n->stepped on my foot on the second time he reached over me to adjust lighting .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n->even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n[{'aspect': 'touchpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: not sure why this restaurant would be rated that highly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot sure why this restaurant would be rated that highly .\n->", + "output": "{\"text\": \"not sure why this restaurant would be rated that highly .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'highly', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent computer , better than expected .\n->excellent computer , better than expected .\n[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: once you get accustomed to the interface , you realize they do everything you need .\n->once you get accustomed to the interface , you realize they do everything you need .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the all - u - can - eat sushi is definitely in very poor quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe all - u - can - eat sushi is definitely in very poor quality .\n->", + "output": "{\"text\": \"the all - u - can - eat sushi is definitely in very poor quality .\", \"labels\": \"[{'aspect': 'all - u - can - eat sushi', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n->Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n[{'aspect': 'dishes', 'opinion': 'sake-friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n->they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: wont come back again for sure !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwont come back again for sure !\n->", + "output": "{\"text\": \"wont come back again for sure !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with so many good restaurants on the uws , i do n ' t need overpriced food , absurdly arrogant wait - staff who do n ' t recognize they work at a glorified diner , clumsy service , and management that does n ' t care .\n->with so many good restaurants on the uws , i do n ' t need overpriced food , absurdly arrogant wait - staff who do n ' t recognize they work at a glorified diner , clumsy service , and management that does n ' t care .\n[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'wait - staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n->a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar - soaked rice , and the scallion on top of the fish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar - soaked rice , and the scallion on top of the fish .\n->", + "output": "{\"text\": \"the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar - soaked rice , and the scallion on top of the fish .\", \"labels\": \"[{'aspect': 'soy sauce', 'opinion': 'salty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rice', 'opinion': 'vinegar - soaked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service not the friendliest to our ` ` large party ' ' !\n->service not the friendliest to our ` ` large party ' ' !\n[{'aspect': 'service', 'opinion': 'not the friendliest', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: but , now i realize the design is flawed .\n->but , now i realize the design is flawed .\n[{'aspect': 'design', 'opinion': 'flawed', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the waitstaffs are nice though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waitstaffs are nice though .\n->", + "output": "{\"text\": \"the waitstaffs are nice though .\", \"labels\": \"[{'aspect': 'waitstaffs', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish i could like this place more , and i wish someone would retrain the staff .\n->i wish i could like this place more , and i wish someone would retrain the staff .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: our server was very helpful and friendly .\n->our server was very helpful and friendly .\n[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i have been to roth ' s twice and both times were very disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been to roth ' s twice and both times were very disappointing .\n->", + "output": "{\"text\": \"i have been to roth ' s twice and both times were very disappointing .\", \"labels\": \"[{'aspect': \"roth ' s\", 'opinion': 'disappointing .', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n->it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n->the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n[{'aspect': 'plastic', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keys', 'opinion': \"' t stick\", 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mousepad', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: both times i was extremely dissappointed by the service , which was boarderline rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nboth times i was extremely dissappointed by the service , which was boarderline rude .\n->", + "output": "{\"text\": \"both times i was extremely dissappointed by the service , which was boarderline rude .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'dissappointed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it runs pretty fast but the keyboard is not lit , the speakers are on the bottom and the track pad is a pos .\n->it runs pretty fast but the keyboard is not lit , the speakers are on the bottom and the track pad is a pos .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'not lit', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}, {'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: works great !\n->works great !\n[{'aspect': 'works', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: they wouldnt even let me finish my glass of wine before offering another .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey wouldnt even let me finish my glass of wine before offering another .\n->", + "output": "{\"text\": \"they wouldnt even let me finish my glass of wine before offering another .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had the lobster sandwich and it was FANTASTIC .\n->We had the lobster sandwich and it was FANTASTIC .\n[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m partial to the gnocchi .\n->i ' m partial to the gnocchi .\n[{'aspect': 'gnocchi', 'opinion': 'partial', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: much of the time it seems like they do not care about you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmuch of the time it seems like they do not care about you .\n->", + "output": "{\"text\": \"much of the time it seems like they do not care about you .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m very disappointed with my purchase\n->i ' m very disappointed with my purchase\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this little chromebook is very nice and is pretty much what i expected .\n->this little chromebook is very nice and is pretty much what i expected .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the dinner was ok , nothing i would have again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dinner was ok , nothing i would have again .\n->", + "output": "{\"text\": \"the dinner was ok , nothing i would have again .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'ok', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nicely sized , thin and portable , the works .\n->nicely sized , thin and portable , the works .\n[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: the touch screen works quite well and i have found myself watching videos in tablet mode with ease .\n->the touch screen works quite well and i have found myself watching videos in tablet mode with ease .\n[{'aspect': 'touch screen', 'opinion': 'well', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: this was a great surprise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was a great surprise .\n->", + "output": "{\"text\": \"this was a great surprise .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n->lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n[{'aspect': 'touch pad', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: had an awful experience at casa la femme on a saturday dinner .\n->had an awful experience at casa la femme on a saturday dinner .\n[{'aspect': 'casa la femme', 'opinion': 'awful', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: i look forward to eating here again\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni look forward to eating here again\n->", + "output": "{\"text\": \"i look forward to eating here again\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for a fabulous wedding !\n->for a fabulous wedding !\n[{'aspect': 'NULL', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n->At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n[{'aspect': 'broth with noodles', 'opinion': 'mild', 'polarity': 'positive', 'category': 'NULL'}]\ntext: planet thai is great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplanet thai is great !\n->", + "output": "{\"text\": \"planet thai is great !\", \"labels\": \"[{'aspect': 'planet thai', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a perfect place to have a amazing indian food .\n->it ' s a perfect place to have a amazing indian food .\n[{'aspect': 'indian food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the food is great and the environment is even better .\n->the food is great and the environment is even better .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'environment', 'opinion': 'better', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: we love the food , drinks , and atmosphere !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe love the food , drinks , and atmosphere !\n->", + "output": "{\"text\": \"we love the food , drinks , and atmosphere !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fast to start up .\n->fast to start up .\n[{'aspect': 'start up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Definitely a great spot for a nice occasion or date .\n->Definitely a great spot for a nice occasion or date .\n[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->", + "output": "{\"text\": \"the svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\", \"labels\": \"[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'must', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the hard drive is really slow and really loud .\n->- the hard drive is really slow and really loud .\n[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'hard drive', 'opinion': 'loud', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: personally , i would steer clear of this chromebook .\n->personally , i would steer clear of this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: try the pad thai , it ' s fabulous and their prices are so cheap !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry the pad thai , it ' s fabulous and their prices are so cheap !\n->", + "output": "{\"text\": \"try the pad thai , it ' s fabulous and their prices are so cheap !\", \"labels\": \"[{'aspect': 'pad thai', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'so cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve found the large trackpad to be responsive and accurate .\n->i ' ve found the large trackpad to be responsive and accurate .\n[{'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n->the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n[{'aspect': 'meat', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauces', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchi', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'salad', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'meal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: just because it ' s cheap does not mean the portions are small or the food is nasty , it is great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust because it ' s cheap does not mean the portions are small or the food is nasty , it is great !\n->", + "output": "{\"text\": \"just because it ' s cheap does not mean the portions are small or the food is nasty , it is great !\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'nasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try their chef 's specials -- they are to die for .\n->Try their chef 's specials -- they are to die for .\n[{'aspect': \"chef 's specials\", 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"chef 's specials\", 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: also , because it is so thin , it gets cold very quickly and its not that filling .\n->also , because it is so thin , it gets cold very quickly and its not that filling .\n[{'aspect': 'NULL', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n->", + "output": "{\"text\": \"they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the waiter was attentive .\n->the waiter was attentive .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: on the upside , the internet is lightning fast and it interfaces with tv through hdmi which is great , is bluetooth compatible and has two usb ports .\n->on the upside , the internet is lightning fast and it interfaces with tv through hdmi which is great , is bluetooth compatible and has two usb ports .\n[{'aspect': 'internet', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#USABILITY'}, {'aspect': 'hdmi', 'opinion': 'great', 'polarity': 'positive', 'category': 'PORTS#PORTABILITY'}]\ntext: also , the sandwiches ( nearing $ 7 ) did n ' t come with anything like chips or a side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , the sandwiches ( nearing $ 7 ) did n ' t come with anything like chips or a side .\n->", + "output": "{\"text\": \"also , the sandwiches ( nearing $ 7 ) did n ' t come with anything like chips or a side .\", \"labels\": \"[{'aspect': 'sandwiches', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'sandwiches', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n->I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: A glass of Leaping Lizard , a glass of prosecco , and the mussels had everything happy .\n->A glass of Leaping Lizard , a glass of prosecco , and the mussels had everything happy .\n[{'aspect': 'glass of prosecco', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'glass of Leaping Lizard', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overall , not worth the money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , not worth the money .\n->", + "output": "{\"text\": \"overall , not worth the money .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fan blows like crazy and it makes so much noise .\n->the fan blows like crazy and it makes so much noise .\n[{'aspect': 'fan', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\nExample:\ntext: The cream cheeses are out of this world and I love that coffee ! !\n->The cream cheeses are out of this world and I love that coffee ! !\n[{'aspect': 'cream cheeses', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'coffee', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: eating in , the atmosphere saves it , but at your desk , it ' s a very disappointing experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neating in , the atmosphere saves it , but at your desk , it ' s a very disappointing experience .\n->", + "output": "{\"text\": \"eating in , the atmosphere saves it , but at your desk , it ' s a very disappointing experience .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'saves', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But they 've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual ) .\n->But they 've done a really nice job of offering all the typical pizzeria faves plus some terrific specials like the Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual ) .\n[{'aspect': 'Godmother pizza ( a sort of traditional flat pizza with an olive oil-brushed crust and less tomato sauce than usual )', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we decided to eat in tea room which was small and cute .\n->we decided to eat in tea room which was small and cute .\n[{'aspect': 'tea room', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tea room', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\ntext: chennai garden is my favorite indian restaurant in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchennai garden is my favorite indian restaurant in the city .\n->", + "output": "{\"text\": \"chennai garden is my favorite indian restaurant in the city .\", \"labels\": \"[{'aspect': 'chennai garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n->My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n[{'aspect': 'food', 'opinion': 'ranting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'raving', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: decor is charming .\n->decor is charming .\n[{'aspect': 'decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: they have authentic indian at amazing prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey have authentic indian at amazing prices .\n->", + "output": "{\"text\": \"they have authentic indian at amazing prices .\", \"labels\": \"[{'aspect': 'indian', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n->You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n[{'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Wine list selection is good and wine-by-the-glass was generously filled to the top .\n->Wine list selection is good and wine-by-the-glass was generously filled to the top .\n[{'aspect': 'Wine list selection', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine-by-the-glass', 'opinion': 'generously filled', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this restaurant is vegetarian ; there are no meat dishes whatsoever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis restaurant is vegetarian ; there are no meat dishes whatsoever .\n->", + "output": "{\"text\": \"this restaurant is vegetarian ; there are no meat dishes whatsoever .\", \"labels\": \"[{'aspect': 'meat dishes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Fresh , authentic , french cuisine in substantial portions .\n->Fresh , authentic , french cuisine in substantial portions .\n[{'aspect': 'french cuisine', 'opinion': 'Fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french cuisine', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'substantial', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the seats are uncomfortable if you are sitting against the wall on wooden benches .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe seats are uncomfortable if you are sitting against the wall on wooden benches .\n->", + "output": "{\"text\": \"the seats are uncomfortable if you are sitting against the wall on wooden benches .\", \"labels\": \"[{'aspect': 'seats', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - battery life is great .\n->- battery life is great .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: We love the food , drinks , and atmosphere !\n->We love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s a rather cramped and busy restaurant and it closes early .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a rather cramped and busy restaurant and it closes early .\n->", + "output": "{\"text\": \"it ' s a rather cramped and busy restaurant and it closes early .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'busy', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'restaurant', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall impression : this is a killer laptop for a killer deal !\n->overall impression : this is a killer laptop for a killer deal !\n[{'aspect': 'laptop', 'opinion': 'killer', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: it is going on 3 hours now and is only 17 % done .\n->it is going on 3 hours now and is only 17 % done .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the food is tasty and portion sizes are appropriate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is tasty and portion sizes are appropriate .\n->", + "output": "{\"text\": \"the food is tasty and portion sizes are appropriate .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apps are glitchy .\n->apps are glitchy .\n[{'aspect': 'apps', 'opinion': 'glitchy', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n->its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n[{'aspect': 'machine', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: this is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\n->", + "output": "{\"text\": \"this is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Much more reasonably priced too !\n->Much more reasonably priced too !\n[{'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: everyone seemed generally happy with their food , except my brother who had the grilled mahi mahi , seemingly drenched in grapfruit juice !\n->everyone seemed generally happy with their food , except my brother who had the grilled mahi mahi , seemingly drenched in grapfruit juice !\n[{'aspect': 'food', 'opinion': 'happy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled mahi mahi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled mahi mahi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: not a great place for family or general dining .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot a great place for family or general dining .\n->", + "output": "{\"text\": \"not a great place for family or general dining .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'not a great', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n->i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\nExample:\ntext: The pizza is overpriced and soggy .\n->The pizza is overpriced and soggy .\n[{'aspect': 'pizza', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: food is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood is excellent .\n->", + "output": "{\"text\": \"food is excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you are in search of the most authentic NYC deli experience look no further than the famous and historic Katz 's Deli down on the Lower East Side .\n->If you are in search of the most authentic NYC deli experience look no further than the famous and historic Katz 's Deli down on the Lower East Side .\n[{'aspect': 'deli', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was prompt and courteous .\n->service was prompt and courteous .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: fish is so very fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfish is so very fresh .\n->", + "output": "{\"text\": \"fish is so very fresh .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is an amazing place to try some roti rolls .\n->This is an amazing place to try some roti rolls .\n[{'aspect': 'roti rolls', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The bagel was huge .\n->The bagel was huge .\n[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\ntext: waitstaff are very friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwaitstaff are very friendly .\n->", + "output": "{\"text\": \"waitstaff are very friendly .\", \"labels\": \"[{'aspect': 'waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n->service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'setting / atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n->But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n[{'aspect': 'atmosphere', 'opinion': 'delightfully', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love yuka .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove yuka .\n->", + "output": "{\"text\": \"love yuka .\", \"labels\": \"[{'aspect': 'yuka', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For some reason , all the seafood on the menu was unavailable except for the Salmon .\n->For some reason , all the seafood on the menu was unavailable except for the Salmon .\n[{'aspect': 'seafood', 'opinion': 'unavailable', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'unavailable', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Salmon', 'opinion': 'except', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: downloading is very fast over wifi .\n->downloading is very fast over wifi .\n[{'aspect': 'wifi', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: mermaid inn is an overall good restaurant with really good seafood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmermaid inn is an overall good restaurant with really good seafood .\n->", + "output": "{\"text\": \"mermaid inn is an overall good restaurant with really good seafood .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mermaid inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first one that they shipped was obviously defective , super slow and speakers were garbled .\n->first one that they shipped was obviously defective , super slow and speakers were garbled .\n[{'aspect': 'NULL', 'opinion': 'defective', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'speakers', 'opinion': 'garbled', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n->returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the lobster sandwich is good and the spaghetti with scallops and shrimp is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe lobster sandwich is good and the spaghetti with scallops and shrimp is great .\n->", + "output": "{\"text\": \"the lobster sandwich is good and the spaghetti with scallops and shrimp is great .\", \"labels\": \"[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spaghetti with scallops and shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i hate the new keyboard the newer version comes with .\n->i hate the new keyboard the newer version comes with .\n[{'aspect': 'keyboard', 'opinion': 'hate', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: screen not aligned perfectly .\n->screen not aligned perfectly .\n[{'aspect': 'screen', 'opinion': 'not aligned perfectly', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the service is good and ambience is good for a date or group outing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service is good and ambience is good for a date or group outing .\n->", + "output": "{\"text\": \"the service is good and ambience is good for a date or group outing .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with magsafe 2 , even the gentlest pull makes the plug to disconnect , which is very annoying for me .\n->with magsafe 2 , even the gentlest pull makes the plug to disconnect , which is very annoying for me .\n[{'aspect': 'plug', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\nExample:\ntext: she just nodded and walked off .\n->she just nodded and walked off .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the only fallback on this restaurant is the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only fallback on this restaurant is the prices .\n->", + "output": "{\"text\": \"the only fallback on this restaurant is the prices .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'fallback', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There was no ambiance .\n->There was no ambiance .\n[{'aspect': 'ambiance', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n->Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n[{'aspect': 'Quality of food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\ntext: even though its good seafood , the prices are too high .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven though its good seafood , the prices are too high .\n->", + "output": "{\"text\": \"even though its good seafood , the prices are too high .\", \"labels\": \"[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'too high', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was , from start to finish , a mind - bogglingly uncomfortable experience .\n->this was , from start to finish , a mind - bogglingly uncomfortable experience .\n[{'aspect': 'NULL', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: quiet keyboard .\n->quiet keyboard .\n[{'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: the lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .\n->", + "output": "{\"text\": \"the lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .\", \"labels\": \"[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster sandwich', 'opinion': 'not nearly enough', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 5 pound laptop with its nine hour battery life .\n->5 pound laptop with its nine hour battery life .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The filet mignon dish was superb !\n->The filet mignon dish was superb !\n[{'aspect': 'filet mignon dish', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we were very pleasantly surprised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were very pleasantly surprised .\n->", + "output": "{\"text\": \"we were very pleasantly surprised .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pleasantly surprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first msi and if it stays great i will be a returning customer .\n->this is my first msi and if it stays great i will be a returning customer .\n[{'aspect': 'msi', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n->this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n[{'aspect': 'computer', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n->", + "output": "{\"text\": \"the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'halibut special', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'steak', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'top - notch', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have eaten there 3-4 times and the food was always good .\n->I have eaten there 3-4 times and the food was always good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .\n->cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .\n[{'aspect': 'atmosphere', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'fire place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: when my dessert came , there was a candle in it - not because anyone asked for one - but because the waiter must have seen me opening my birthday card and gift , and said he knew it was a special occassion of some sort .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen my dessert came , there was a candle in it - not because anyone asked for one - but because the waiter must have seen me opening my birthday card and gift , and said he knew it was a special occassion of some sort .\n->", + "output": "{\"text\": \"when my dessert came , there was a candle in it - not because anyone asked for one - but because the waiter must have seen me opening my birthday card and gift , and said he knew it was a special occassion of some sort .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Diner food at bistro prices is a bummer ... .\n->Diner food at bistro prices is a bummer ... .\n[{'aspect': 'food', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: other than my hope that it would be light enough to use as a tablet all the time , this is the chromebook i ' ve been wanting for a long time now .\n->other than my hope that it would be light enough to use as a tablet all the time , this is the chromebook i ' ve been wanting for a long time now .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PORTABILITY'}]\ntext: truly the mark of an attentive waiter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntruly the mark of an attentive waiter .\n->", + "output": "{\"text\": \"truly the mark of an attentive waiter .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what ' s not to like , it ' s an amazing machine .\n->what ' s not to like , it ' s an amazing machine .\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n->Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'understated', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i highly recommend the restaurant based on our experience last night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend the restaurant based on our experience last night .\n->", + "output": "{\"text\": \"i highly recommend the restaurant based on our experience last night .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is fast and friendly .\n->service is fast and friendly .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: keyboard problems too .\n->keyboard problems too .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: we ate at this thai place following the reviews but very unhappy with the foods .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ate at this thai place following the reviews but very unhappy with the foods .\n->", + "output": "{\"text\": \"we ate at this thai place following the reviews but very unhappy with the foods .\", \"labels\": \"[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only complaint i have is relatively minor in that the screen is a little small for my taste .\n->the only complaint i have is relatively minor in that the screen is a little small for my taste .\n[{'aspect': 'screen', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'small', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: they are clearly working with more than one person at a time , and not effective multi - taskers .\n->they are clearly working with more than one person at a time , and not effective multi - taskers .\n[{'aspect': 'NULL', 'opinion': 'not effective', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: we thought that this place is using too much of msg cooking in the foods .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe thought that this place is using too much of msg cooking in the foods .\n->", + "output": "{\"text\": \"we thought that this place is using too much of msg cooking in the foods .\", \"labels\": \"[{'aspect': 'foods', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would never have thought that a chromebook would be so fun to use .\n->i would never have thought that a chromebook would be so fun to use .\n[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: about 4 hours of battery\n->about 4 hours of battery\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: they do n ' t concern much of customer ' s health , just want to make money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey do n ' t concern much of customer ' s health , just want to make money .\n->", + "output": "{\"text\": \"they do n ' t concern much of customer ' s health , just want to make money .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: short charging cable .\n->short charging cable .\n[{'aspect': 'charging cable', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\nExample:\ntext: The portions are small but being that the food was so good makes up for that .\n->The portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: please do n ' t fool us .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplease do n ' t fool us .\n->", + "output": "{\"text\": \"please do n ' t fool us .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fool', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my macbook pro 15 \u201d can overheating when some do it !\n->my macbook pro 15 \u201d can overheating when some do it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it has and does everything it should .\n->it has and does everything it should .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n->", + "output": "{\"text\": \"i recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\", \"labels\": \"[{'aspect': 'jelly fish', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'drunken chicken', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'soupy dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'stir fry blue crab', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do n ' t judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\n->do n ' t judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: battery life sucks .\n->battery life sucks .\n[{'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: the food is so cheap and the waiters are nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is so cheap and the waiters are nice .\n->", + "output": "{\"text\": \"the food is so cheap and the waiters are nice .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is decent .\n->the food is decent .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Pizza is terrific , as is homemade pasta .\n->Pizza is terrific , as is homemade pasta .\n[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\ntext: of course , it is crowded but who cares .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nof course , it is crowded but who cares .\n->", + "output": "{\"text\": \"of course , it is crowded but who cares .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n->the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n[{'aspect': 'cold appetizer dishes', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this tiny restaurant is as cozy as it gets , with that certain parisian flair .\n->this tiny restaurant is as cozy as it gets , with that certain parisian flair .\n[{'aspect': 'restaurant', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: authentic shanghai style and i can not recommend a better shanghai place in new york .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nauthentic shanghai style and i can not recommend a better shanghai place in new york .\n->", + "output": "{\"text\": \"authentic shanghai style and i can not recommend a better shanghai place in new york .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is good , the tablet mode is nice , and the keyboard has a good feel .\n->the screen is good , the tablet mode is nice , and the keyboard has a good feel .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: The strong scents coming from the left and right of me negatively affected my taste buds .\n->The strong scents coming from the left and right of me negatively affected my taste buds .\n[{'aspect': 'scents', 'opinion': 'strong', 'polarity': 'negative', 'category': 'NULL'}]\ntext: have frequented ' ino for several years and the food remains excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave frequented ' ino for several years and the food remains excellent .\n->", + "output": "{\"text\": \"have frequented ' ino for several years and the food remains excellent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my boyfriend and i went there to celebrate my birthday the other night and all i can say is that it was magnificent .\n->my boyfriend and i went there to celebrate my birthday the other night and all i can say is that it was magnificent .\n[{'aspect': 'NULL', 'opinion': 'magnificent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Average to good Thai food , but terrible delivery .\n->Average to good Thai food , but terrible delivery .\n[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: cheese plate is a varied delight and great bargain at $ 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncheese plate is a varied delight and great bargain at $ 10 .\n->", + "output": "{\"text\": \"cheese plate is a varied delight and great bargain at $ 10 .\", \"labels\": \"[{'aspect': 'cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cheese plate is a varied delight and great bargain at $ 10 .\n->Cheese plate is a varied delight and great bargain at $ 10 .\n[{'aspect': 'Cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my wife had the fried shrimp which are huge and loved it .\n->my wife had the fried shrimp which are huge and loved it .\n[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .\n->", + "output": "{\"text\": \"the large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .\", \"labels\": \"[{'aspect': 'bruschettas', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'paninis', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'tramezzinis', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - pretty loud speakers .\n->- pretty loud speakers .\n[{'aspect': 'speakers', 'opinion': 'loud', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: the only thing i think that could be better is the volume of the speakers .\n->the only thing i think that could be better is the volume of the speakers .\n[{'aspect': 'speakers', 'opinion': 'could be better', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: ( the asparagus , truffle oil , parmesan bruschetta is a winner ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( the asparagus , truffle oil , parmesan bruschetta is a winner ! )\n->", + "output": "{\"text\": \"( the asparagus , truffle oil , parmesan bruschetta is a winner ! )\", \"labels\": \"[{'aspect': 'asparagus', 'opinion': 'winner', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'truffle oil', 'opinion': 'winner', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'parmesan bruschetta', 'opinion': 'winner', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is reliable and the price is moderate .\n->the food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: purchased it the first time , thought it was faulty hardware , returned for a replacement of the exact same , and it had the exact problems .\n->purchased it the first time , thought it was faulty hardware , returned for a replacement of the exact same , and it had the exact problems .\n[{'aspect': 'hardware', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: be sure to try the seasonal , and always delicious , specials .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbe sure to try the seasonal , and always delicious , specials .\n->", + "output": "{\"text\": \"be sure to try the seasonal , and always delicious , specials .\", \"labels\": \"[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n->i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it stopped working a week after i recieved it .\n->it stopped working a week after i recieved it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: definitely a neighborhood favorite .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely a neighborhood favorite .\n->", + "output": "{\"text\": \"definitely a neighborhood favorite .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: avoid this place !\n->avoid this place !\n[{'aspect': 'place', 'opinion': 'avoid', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: edges of sides can be a little sharp .\n->edges of sides can be a little sharp .\n[{'aspect': 'edges', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i loved this place ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved this place ! !\n->", + "output": "{\"text\": \"i loved this place ! !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n->i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n[{'aspect': 'the new upgrades', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i almost hesititate to write a review because the atmosphere was so great and i would hate for it too become to crowded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni almost hesititate to write a review because the atmosphere was so great and i would hate for it too become to crowded .\n->", + "output": "{\"text\": \"i almost hesititate to write a review because the atmosphere was so great and i would hate for it too become to crowded .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when we stumbled on leon , we thought that we had found quite the gem but , we were certainly wrong .\n->when we stumbled on leon , we thought that we had found quite the gem but , we were certainly wrong .\n[{'aspect': 'leon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: below medium build quality\n->below medium build quality\n[{'aspect': 'build quality', 'opinion': 'below medium', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i like cafe noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like cafe noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->", + "output": "{\"text\": \"i like cafe noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\", \"labels\": \"[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'cafe noir', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: be prepared to wait , because the place is pretty tiny .\n->be prepared to wait , because the place is pretty tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i picked up the stylus and it fell apart , no drops no damage .\n->i picked up the stylus and it fell apart , no drops no damage .\n[{'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: the service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n->", + "output": "{\"text\": \"the service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Decent wine at reasonable prices .\n->Decent wine at reasonable prices .\n[{'aspect': 'wine', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Authentic Pakistani food .\n->Authentic Pakistani food .\n[{'aspect': 'Pakistani food', 'opinion': 'Authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the waitress , seems to be more concerned of looking good than actually waitressing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waitress , seems to be more concerned of looking good than actually waitressing .\n->", + "output": "{\"text\": \"the waitress , seems to be more concerned of looking good than actually waitressing .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n->You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'amiable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is reliable and the price is moderate .\n->The food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: after dinner the manager grabbed my boyfriend , asked him : where are you from . . . maybe you dont know how things work in america . . . and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter dinner the manager grabbed my boyfriend , asked him : where are you from . . . maybe you dont know how things work in america . . . and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .\n->", + "output": "{\"text\": \"after dinner the manager grabbed my boyfriend , asked him : where are you from . . . maybe you dont know how things work in america . . . and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n->They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n[{'aspect': 'RICE', 'opinion': 'BURNT', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The fries are yummy .\n->The fries are yummy .\n[{'aspect': 'fries', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we did tip , i guess the model / waitress just wanted more and complained to the manager .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe did tip , i guess the model / waitress just wanted more and complained to the manager .\n->", + "output": "{\"text\": \"we did tip , i guess the model / waitress just wanted more and complained to the manager .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The well mannered , pleasant staff that Tony has in his employ .\n->The well mannered , pleasant staff that Tony has in his employ .\n[{'aspect': 'staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n->once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n[{'aspect': 'cosette', 'opinion': 'off - the - beaten', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: pizza here is consistently good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npizza here is consistently good .\n->", + "output": "{\"text\": \"pizza here is consistently good .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing is that it does ' t have too much storage room .\n->the only thing is that it does ' t have too much storage room .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: best italian food i ever had ( and being italian , that means alot ) .\n->best italian food i ever had ( and being italian , that means alot ) .\n[{'aspect': 'italian food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: salads are a delicious way to begin the meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsalads are a delicious way to begin the meal .\n->", + "output": "{\"text\": \"salads are a delicious way to begin the meal .\", \"labels\": \"[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this issue happens more frequently when using netflix ( app or through chrome browser ) .\n->this issue happens more frequently when using netflix ( app or through chrome browser ) .\n[{'aspect': 'happens', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: The food is wonderful , tasty and filling , and the service is professional and friendly .\n->The food is wonderful , tasty and filling , and the service is professional and friendly .\n[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'filling', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is thick and slightly soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is thick and slightly soggy .\n->", + "output": "{\"text\": \"it is thick and slightly soggy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'thick', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the power button is no longer on the keyboard , but is instead on the side of the machine which is fine .\n->the power button is no longer on the keyboard , but is instead on the side of the machine which is fine .\n[{'aspect': 'power button', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: this computer lives up to its expectations .\n->this computer lives up to its expectations .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: decor is charming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndecor is charming .\n->", + "output": "{\"text\": \"decor is charming .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice keyboard\n->nice keyboard\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: Their tuna tartar appetizer is to die for .\n->Their tuna tartar appetizer is to die for .\n[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\ntext: service is average .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is average .\n->", + "output": "{\"text\": \"service is average .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n->if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Most importantly , food is excellent .\n->Most importantly , food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: what a great place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat a great place .\n->", + "output": "{\"text\": \"what a great place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n->i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n->bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n[{'aspect': 'specs', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n->", + "output": "{\"text\": \"the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 720p screen that ' s not very bright .\n->720p screen that ' s not very bright .\n[{'aspect': '720p screen', 'opinion': 'not very bright', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n->I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n[{'aspect': 'restaurant', 'opinion': 'amazing time', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i was n ' t thrilled to have to wait on line for thirty minutes , but i guess that ' s the price you pay for a popular place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was n ' t thrilled to have to wait on line for thirty minutes , but i guess that ' s the price you pay for a popular place .\n->", + "output": "{\"text\": \"i was n ' t thrilled to have to wait on line for thirty minutes , but i guess that ' s the price you pay for a popular place .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Hats off to the chef .\n->Hats off to the chef .\n[{'aspect': 'chef', 'opinion': 'Hats off', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n->extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n[{'aspect': 'seller', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: i would definitely recommend sea if you like thai cuisine !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would definitely recommend sea if you like thai cuisine !\n->", + "output": "{\"text\": \"i would definitely recommend sea if you like thai cuisine !\", \"labels\": \"[{'aspect': 'thai cuisine', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , the way i see it , i ' m getting a big screen hd android galaxy tablet with a keyboard for the price of a tablet .\n->overall , the way i see it , i ' m getting a big screen hd android galaxy tablet with a keyboard for the price of a tablet .\n[{'aspect': 'tablet', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: A cool place to hang with your friends for a couple of healthy drinks and desserts .\n->A cool place to hang with your friends for a couple of healthy drinks and desserts .\n[{'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->", + "output": "{\"text\": \"i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n->the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n[{'aspect': 'hot dogs', 'opinion': 'juicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dogs', 'opinion': 'tender', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the speakers on at the front on bottom so sound quality isn ' t the best .\n->the speakers on at the front on bottom so sound quality isn ' t the best .\n[{'aspect': 'speakers', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'sound quality', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n->", + "output": "{\"text\": \"a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\", \"labels\": \"[{'aspect': 'gentleman', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 2015 version has the old keyboard with keys that you can actually type on without the fear of a typo every other word .\n->the 2015 version has the old keyboard with keys that you can actually type on without the fear of a typo every other word .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: product worked great until it randomly stopped charging .\n->product worked great until it randomly stopped charging .\n[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\n->", + "output": "{\"text\": \"while it is understandable that every place is indeed different , there was not a need to be uncourteous to customers and downright rude .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'uncourteous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - performance can be stuttering when under heavy load .\n->- performance can be stuttering when under heavy load .\n[{'aspect': 'performance', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: for the price i think it ' s just fine .\n->for the price i think it ' s just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: never again !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnever again !\n->", + "output": "{\"text\": \"never again !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'never again', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The lava cake dessert was incredible and I recommend it .\n->The lava cake dessert was incredible and I recommend it .\n[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->The chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i absolutely loved this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely loved this place .\n->", + "output": "{\"text\": \"i absolutely loved this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n->the menu looked great , and the waiter was very nice , but when the food came , it was average .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: you can ' t beat the price for what you are getting with this computer .\n->you can ' t beat the price for what you are getting with this computer .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: excellent atmosphere , delicious dishes good and friendly service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent atmosphere , delicious dishes good and friendly service .\n->", + "output": "{\"text\": \"excellent atmosphere , delicious dishes good and friendly service .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: easily the worst stir - fried squid i ' ve ever tasted .\n->easily the worst stir - fried squid i ' ve ever tasted .\n[{'aspect': 'stir - fried squid', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i ' ve used this daily for nearly eight months and have been very happy with .\n->i ' ve used this daily for nearly eight months and have been very happy with .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this is can became on e of the ny italian food fare institutions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is can became on e of the ny italian food fare institutions .\n->", + "output": "{\"text\": \"this is can became on e of the ny italian food fare institutions .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n->its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n[{'aspect': 'machine', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: i was so excited to buy the asus chromebook , and bought some for my grandchildren .\n->i was so excited to buy the asus chromebook , and bought some for my grandchildren .\n[{'aspect': 'asus chromebook', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i think that it is absolutely brilliant and well runned business operation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think that it is absolutely brilliant and well runned business operation .\n->", + "output": "{\"text\": \"i think that it is absolutely brilliant and well runned business operation .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'well runned', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have never before eaten 40 pieces of relatively good nigiri .\n->I have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: just not good at all .\n->just not good at all .\n[{'aspect': 'NULL', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the wine list is also really nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wine list is also really nice .\n->", + "output": "{\"text\": \"the wine list is also really nice .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is good at about 10 hours .\n->battery life is good at about 10 hours .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: not happy with this one .\n->not happy with this one .\n[{'aspect': 'NULL', 'opinion': 'not happy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i have been to casimir over 5 times and i have always had a great time there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been to casimir over 5 times and i have always had a great time there .\n->", + "output": "{\"text\": \"i have been to casimir over 5 times and i have always had a great time there .\", \"labels\": \"[{'aspect': 'casimir', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - great quality build .\n->- great quality build .\n[{'aspect': 'quality build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: * very solidly built and it transitions nicely from laptop to tablet mode .\n->* very solidly built and it transitions nicely from laptop to tablet mode .\n[{'aspect': 'built', 'opinion': 'solidly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'tablet', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the food is great and reasonably priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is great and reasonably priced .\n->", + "output": "{\"text\": \"the food is great and reasonably priced .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n->however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: 2 ) slow start up and performance given\n->2 ) slow start up and performance given\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i have tried to make reservations , but both times , the hostess did n ' t have my name .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have tried to make reservations , but both times , the hostess did n ' t have my name .\n->", + "output": "{\"text\": \"i have tried to make reservations , but both times , the hostess did n ' t have my name .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all in all , the food was great ( except for the dessserts ) .\n->all in all , the food was great ( except for the dessserts ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessserts', 'opinion': 'except', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this issue happens more frequently when using netflix ( app or through chrome browser ) .\n->this issue happens more frequently when using netflix ( app or through chrome browser ) .\n[{'aspect': 'happens', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: if the weather is nice , try to snag an outside table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif the weather is nice , try to snag an outside table .\n->", + "output": "{\"text\": \"if the weather is nice , try to snag an outside table .\", \"labels\": \"[{'aspect': 'outside table', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We love the food , drinks , and atmosphere !\n->We love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n->an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n[{'aspect': 'spot', 'opinion': 'unpretentious', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'effective', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->", + "output": "{\"text\": \"the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'stressed', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'unisex bathroom', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen maximum brightness is still not bright enough\n->screen maximum brightness is still not bright enough\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the trackpad is mediocre in use .\n->the trackpad is mediocre in use .\n[{'aspect': 'trackpad', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: my boyfriend and i went there to celebrate my birthday the other night and all i can say is that it was magnificent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy boyfriend and i went there to celebrate my birthday the other night and all i can say is that it was magnificent .\n->", + "output": "{\"text\": \"my boyfriend and i went there to celebrate my birthday the other night and all i can say is that it was magnificent .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'magnificent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i type 100 wpm and i like the keyboard and touch pad a lot .\n->i type 100 wpm and i like the keyboard and touch pad a lot .\n[{'aspect': 'keyboard', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: the laptop build is cheap looking and basic , but functional to say the least while i save up money .\n->the laptop build is cheap looking and basic , but functional to say the least while i save up money .\n[{'aspect': 'laptop build', 'opinion': 'cheap looking', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop build', 'opinion': 'basic', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop build', 'opinion': 'functional', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: from the spectacular caviar to the hospitable waitstaff , i felt like royalty and enjoyed every second of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfrom the spectacular caviar to the hospitable waitstaff , i felt like royalty and enjoyed every second of it .\n->", + "output": "{\"text\": \"from the spectacular caviar to the hospitable waitstaff , i felt like royalty and enjoyed every second of it .\", \"labels\": \"[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very happy with my chromebook !\n->i am very happy with my chromebook !\n[{'aspect': 'chromebook', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: for me has been worth the $ 500 for the computer .\n->for me has been worth the $ 500 for the computer .\n[{'aspect': 'computer', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nconsidering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n->", + "output": "{\"text\": \"considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\", \"labels\": \"[{'aspect': 'waitstaff', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitstaff', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen looks fantastic and movies look great .\n->the screen looks fantastic and movies look great .\n[{'aspect': 'screen', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n->One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n[{'aspect': 'menu', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i highly recommend caviar russe to anyone who wants delicious top grade caviar and fantastic service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend caviar russe to anyone who wants delicious top grade caviar and fantastic service .\n->", + "output": "{\"text\": \"i highly recommend caviar russe to anyone who wants delicious top grade caviar and fantastic service .\", \"labels\": \"[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we started off with a delightful sashimi amuse bouche .\n->we started off with a delightful sashimi amuse bouche .\n[{'aspect': 'sashimi amuse bouche', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s complementary , not revolutionary , which is much more intuitive and useful .\n->it ' s complementary , not revolutionary , which is much more intuitive and useful .\n[{'aspect': 'NULL', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: nice family owned traditional restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice family owned traditional restaurant .\n->", + "output": "{\"text\": \"nice family owned traditional restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: before i get to that , let me first confirm that the keyboard is indeed appallingly bad .\n->before i get to that , let me first confirm that the keyboard is indeed appallingly bad .\n[{'aspect': 'keyboard', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: I recommend this place to everyone who asks me where to go for a good meal .\n->I recommend this place to everyone who asks me where to go for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i was pleasantly surprised to find this gem in hoboken .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was pleasantly surprised to find this gem in hoboken .\n->", + "output": "{\"text\": \"i was pleasantly surprised to find this gem in hoboken .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: volume was not working .\n->volume was not working .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: traditional french decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n->traditional french decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n[{'aspect': 'traditional french decour', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'hall', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: i found the food to be outstanding , particulary the salmon dish i had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni found the food to be outstanding , particulary the salmon dish i had .\n->", + "output": "{\"text\": \"i found the food to be outstanding , particulary the salmon dish i had .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon dish', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n->i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n->overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n[{'aspect': 'computer', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i also ordered the change mojito , which was out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni also ordered the change mojito , which was out of this world .\n->", + "output": "{\"text\": \"i also ordered the change mojito , which was out of this world .\", \"labels\": \"[{'aspect': 'change mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will admit that i needed to get used to a combination of keystrokes and screen touches , but the touch screen is both sensitive and accurate .\n->i will admit that i needed to get used to a combination of keystrokes and screen touches , but the touch screen is both sensitive and accurate .\n[{'aspect': 'keystrokes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'screen touches', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'touch screen', 'opinion': 'sensitive', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'touch screen', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: never got an explanation as to what was going on .\n->never got an explanation as to what was going on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: my friends settled for rice dishes , but we came back the following day to try the dim sum , which was good . . . not outstanding , but good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy friends settled for rice dishes , but we came back the following day to try the dim sum , which was good . . . not outstanding , but good .\n->", + "output": "{\"text\": \"my friends settled for rice dishes , but we came back the following day to try the dim sum , which was good . . . not outstanding , but good .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'dim sum', 'opinion': 'not outstanding', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was pretty good , but a little flavorless and the portions very small , including dessert .\n->The food was pretty good , but a little flavorless and the portions very small , including dessert .\n[{'aspect': 'dessert', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The food looked very appetizing and delicious since it came on a variety of fancy plates .\n->The food looked very appetizing and delicious since it came on a variety of fancy plates .\n[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'plates', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n->", + "output": "{\"text\": \"we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\", \"labels\": \"[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: whoever the jazz duo was , they were on point .\n->whoever the jazz duo was , they were on point .\n[{'aspect': 'jazz duo', 'opinion': 'on point', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i would recommend reservations on weekends though .\n->i would recommend reservations on weekends though .\n[{'aspect': 'reservations', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: overall , excellent restaurant !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , excellent restaurant !\n->", + "output": "{\"text\": \"overall , excellent restaurant !\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: please note that the track pad is way better than most .\n->please note that the track pad is way better than most .\n[{'aspect': 'track pad', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: for desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .\n->for desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .\n[{'aspect': 'frozen black sesame mousse', 'opinion': 'interesting', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'frozen black sesame mousse', 'opinion': 'extraordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'matcha ( powdered green tea ) and blueberry cheesecake', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the food was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was good .\n->", + "output": "{\"text\": \"the food was good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n->You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n[{'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the fans did not turn on loudly if at all .\n->the fans did not turn on loudly if at all .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: the place was nice and calm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place was nice and calm .\n->", + "output": "{\"text\": \"the place was nice and calm .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'calm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can ' t get it to work at all .\n->i can ' t get it to work at all .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: up date per may 13 / 2018 about two months ago , the charger wont work .\n->up date per may 13 / 2018 about two months ago , the charger wont work .\n[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: but the service was a bit slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the service was a bit slow .\n->", + "output": "{\"text\": \"but the service was a bit slow .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the color of everything is so very much brighter and clearer it makes the extra cost is worth more for just that .\n->the color of everything is so very much brighter and clearer it makes the extra cost is worth more for just that .\n[{'aspect': 'color', 'opinion': 'brighter', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'color', 'opinion': 'clearer', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'color', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: thanks amazon for your great return policy !\n->thanks amazon for your great return policy !\n[{'aspect': 'amazon', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'return policy', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: the buffet had a nice selection .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe buffet had a nice selection .\n->", + "output": "{\"text\": \"the buffet had a nice selection .\", \"labels\": \"[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this made me realize that arm processors are not ready for desktop - class browsing .\n->this made me realize that arm processors are not ready for desktop - class browsing .\n[{'aspect': 'arm processors', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\nExample:\ntext: Try ordering from the regular menu , then you would not regret !\n->Try ordering from the regular menu , then you would not regret !\n[{'aspect': 'menu', 'opinion': 'regret', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food was average or above including some surprising tasty dishes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was average or above including some surprising tasty dishes .\n->", + "output": "{\"text\": \"the food was average or above including some surprising tasty dishes .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The owner truly caters to all your needs .\n->The owner truly caters to all your needs .\n[{'aspect': 'owner', 'opinion': 'caters', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n->i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n[{'aspect': 'acer monitors', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: service was also very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was also very good .\n->", + "output": "{\"text\": \"service was also very good .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Also , specify if you like your food spicy- its rather bland if you do n't .\n->Also , specify if you like your food spicy- its rather bland if you do n't .\n[{'aspect': 'food', 'opinion': 'bland', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n->The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n[{'aspect': 'anti-pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i would go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would go back .\n->", + "output": "{\"text\": \"i would go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n->I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n[{'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my husbands was perfect , my was well done and dry .\n->my husbands was perfect , my was well done and dry .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'well done', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i got an excellent piece of cheesecake and we had several other nice pastries .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got an excellent piece of cheesecake and we had several other nice pastries .\n->", + "output": "{\"text\": \"i got an excellent piece of cheesecake and we had several other nice pastries .\", \"labels\": \"[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen backlight stopped working after just one month of light use .\n->screen backlight stopped working after just one month of light use .\n[{'aspect': 'screen backlight', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i purchased this asus chromebook in may of 2018 and initially loved it .\n->i purchased this asus chromebook in may of 2018 and initially loved it .\n[{'aspect': 'asus chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i would recommend roxy ' s for that , but not for their food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would recommend roxy ' s for that , but not for their food .\n->", + "output": "{\"text\": \"i would recommend roxy ' s for that , but not for their food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard layout is not the best , do not like that i have to press the function key to raise or lower the volume / brightness on the arrow keys .\n->keyboard layout is not the best , do not like that i have to press the function key to raise or lower the volume / brightness on the arrow keys .\n[{'aspect': 'keyboard layout is', 'opinion': 'not the best', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: Leon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .\n->Leon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .\n[{'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'lively', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'French bistro fare', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my son and his girlfriend both wanted cheeseburgers and they were huge !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy son and his girlfriend both wanted cheeseburgers and they were huge !\n->", + "output": "{\"text\": \"my son and his girlfriend both wanted cheeseburgers and they were huge !\", \"labels\": \"[{'aspect': 'cheeseburgers', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n->While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n[{'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: ess - a - bagel ( either by sty - town or midtown ) is by far the best bagel in ny .\n->ess - a - bagel ( either by sty - town or midtown ) is by far the best bagel in ny .\n[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: but , they were too big for the bun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut , they were too big for the bun .\n->", + "output": "{\"text\": \"but , they were too big for the bun .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'too big', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n->quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n[{'aspect': 'NULL', 'opinion': 'tranquility', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i love my laptop !\n->i love my laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: consequently , their burgers fell apart in their hands and made such a mess that they did ' nt feel like finishing them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nconsequently , their burgers fell apart in their hands and made such a mess that they did ' nt feel like finishing them .\n->", + "output": "{\"text\": \"consequently , their burgers fell apart in their hands and made such a mess that they did ' nt feel like finishing them .\", \"labels\": \"[{'aspect': 'burgers', 'opinion': 'fell apart', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n->The Steak Tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: But that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\n->But that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\n[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'NULL'}]\ntext: also they were $ 15 each !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso they were $ 15 each !\n->", + "output": "{\"text\": \"also they were $ 15 each !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build - quality is pretty good .\n->the build - quality is pretty good .\n[{'aspect': 'build - quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: if known ahead of time , we would have not purchased this machine .\n->if known ahead of time , we would have not purchased this machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i had a huge pastrami sandwich on a roll .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had a huge pastrami sandwich on a roll .\n->", + "output": "{\"text\": \"i had a huge pastrami sandwich on a roll .\", \"labels\": \"[{'aspect': 'pastrami sandwich on a roll', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pick a bagel has the best bagels in the city .\n->Pick a bagel has the best bagels in the city .\n[{'aspect': 'bagels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it calls itself a computer , but it ' s really not .\n->it calls itself a computer , but it ' s really not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n->", + "output": "{\"text\": \"it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not really bad', 'polarity': 'neutral', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really needed this product for work and had big plans for using it ' s features / versatility .\n->i really needed this product for work and had big plans for using it ' s features / versatility .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: light weight , very convenient to use .\n->light weight , very convenient to use .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->", + "output": "{\"text\": \"this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'trendi', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n->i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n[{'aspect': 'chromebook', 'opinion': 'enthusiast', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: not enough wines by the glass either .\n->not enough wines by the glass either .\n[{'aspect': 'wines by the glass', 'opinion': 'not enough', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->", + "output": "{\"text\": \"the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had this laptop repaired within the first 6 months of owning it .\n->i had this laptop repaired within the first 6 months of owning it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i loved it and would go again .\n->i loved it and would go again .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: and the tom kha soup was pathetic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand the tom kha soup was pathetic .\n->", + "output": "{\"text\": \"and the tom kha soup was pathetic .\", \"labels\": \"[{'aspect': 'tom kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not even going to bother to describe it ; it speaks for itself .\n->i ' m not even going to bother to describe it ; it speaks for itself .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the device itself is light and handsome - but virtually useless for long documents .\n->the device itself is light and handsome - but virtually useless for long documents .\n[{'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'handsome', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: if you want good authentic thai this place is not the place to go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you want good authentic thai this place is not the place to go .\n->", + "output": "{\"text\": \"if you want good authentic thai this place is not the place to go .\", \"labels\": \"[{'aspect': 'thai', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: loved it\n->loved it\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n->if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n[{'aspect': 'corona', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->", + "output": "{\"text\": \"we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n->thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i must say i am surprised by the bad reviews of the restaurant earlier in the year , though .\n->i must say i am surprised by the bad reviews of the restaurant earlier in the year , though .\n[{'aspect': 'restaurant', 'opinion': 'bad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n->", + "output": "{\"text\": \"the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\", \"labels\": \"[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Sushi was n't anything spectacular for the price .\n->Sushi was n't anything spectacular for the price .\n[{'aspect': 'Sushi', 'opinion': 'spectacular', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The portions are large and the servers always surprise us with a different starter .\n->The portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: conveniently located too , being right on bedford ave .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nconveniently located too , being right on bedford ave .\n->", + "output": "{\"text\": \"conveniently located too , being right on bedford ave .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'conveniently', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we liked it so much , that we will always make it a point to dine here when we visit new york .\n->we liked it so much , that we will always make it a point to dine here when we visit new york .\n[{'aspect': 'NULL', 'opinion': 'liked', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the battery broke after just 4 months from baying it am so disappointed with the product\n->the battery broke after just 4 months from baying it am so disappointed with the product\n[{'aspect': 'battery', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}, {'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the back garden sitting area is very pleasant , where you can see their personal herb garden .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe back garden sitting area is very pleasant , where you can see their personal herb garden .\n->", + "output": "{\"text\": \"the back garden sitting area is very pleasant , where you can see their personal herb garden .\", \"labels\": \"[{'aspect': 'back garden sitting area', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this laptop !\n->i love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n->the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n[{'aspect': 'tablet mode', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: we had the lobster sandwich and it was fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe had the lobster sandwich and it was fantastic .\n->", + "output": "{\"text\": \"we had the lobster sandwich and it was fantastic .\", \"labels\": \"[{'aspect': 'lobster sandwich', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my screen stayed black more than it was on .\n->my screen stayed black more than it was on .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the hostess was rude and i got a distinct feeling that they did not want to serve us .\n->the hostess was rude and i got a distinct feeling that they did not want to serve us .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: my husband said he could ' ve eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy husband said he could ' ve eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n->", + "output": "{\"text\": \"my husband said he could ' ve eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\", \"labels\": \"[{'aspect': 'portion', 'opinion': 'fine', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'french fries', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n->the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i was pleasantly suprised .\n->i was pleasantly suprised .\n[{'aspect': 'NULL', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'suprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: we had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n->", + "output": "{\"text\": \"we had the scallops as an appetizer and they were delicious and the sauce was wonderful .\", \"labels\": \"[{'aspect': 'scallops', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: screen resolution is good .\n->screen resolution is good .\n[{'aspect': 'screen resolution', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: we waited at the bar and had martinis that were just right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe waited at the bar and had martinis that were just right .\n->", + "output": "{\"text\": \"we waited at the bar and had martinis that were just right .\", \"labels\": \"[{'aspect': 'martinis', 'opinion': 'right', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touch screen never seemed to work properly and now i understand why .\n->the touch screen never seemed to work properly and now i understand why .\n[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: ordered this computer to use in college and also for gaming .\n->ordered this computer to use in college and also for gaming .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: love the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the food .\n->", + "output": "{\"text\": \"love the food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine selection ( by the glass and bottle ) is wonderful and I always recommend that friends make a reservation if they 're going to be in town .\n->The wine selection ( by the glass and bottle ) is wonderful and I always recommend that friends make a reservation if they 're going to be in town .\n[{'aspect': 'wine selection', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'reservation', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when i went .\n->food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when i went .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'view of the new york city skiline', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: it ' s the only place you can get yummy authentic japanese comfort food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s the only place you can get yummy authentic japanese comfort food .\n->", + "output": "{\"text\": \"it ' s the only place you can get yummy authentic japanese comfort food .\", \"labels\": \"[{'aspect': 'japanese comfort food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'japanese comfort food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: buy this thing if you want a cheap , snappy web browser on - the - go .\n->buy this thing if you want a cheap , snappy web browser on - the - go .\n[{'aspect': 'NULL', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: my only complaint is that the trackpad is just awful .\n->my only complaint is that the trackpad is just awful .\n[{'aspect': 'trackpad', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: and you ca n ' t beat the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand you ca n ' t beat the prices .\n->", + "output": "{\"text\": \"and you ca n ' t beat the prices .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the asus chromebook flip 302 fit the bill .\n->the asus chromebook flip 302 fit the bill .\n[{'aspect': 'asus chromebook flip 302', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: just not good at all .\n->just not good at all .\n[{'aspect': 'NULL', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i ' ve lived in ny for 5 years and this place has it all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve lived in ny for 5 years and this place has it all .\n->", + "output": "{\"text\": \"i ' ve lived in ny for 5 years and this place has it all .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n->I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n[{'aspect': 'scallop roll', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i really needed this product for work and had big plans for using it ' s features / versatility .\n->i really needed this product for work and had big plans for using it ' s features / versatility .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: great food , good size menu , great service and an unpretensious setting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food , good size menu , great service and an unpretensious setting .\n->", + "output": "{\"text\": \"great food , good size menu , great service and an unpretensious setting .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'menu', 'opinion': 'good size', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'setting', 'opinion': 'unpretensious', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i absolutely love this chromebook !\n->i absolutely love this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: love the laptop ; great quality ; sent as expected , on time .\n->love the laptop ; great quality ; sent as expected , on time .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: the dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest i ' ve ever had ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest i ' ve ever had ) .\n->", + "output": "{\"text\": \"the dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest i ' ve ever had ) .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb sausages', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sardines with biscuits', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'large whole shrimp', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pistachio ice cream', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s the only place you can get yummy authentic japanese comfort food .\n->it ' s the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'japanese comfort food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i ' m glad i was introduced to this place and this is a rare gem in ny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m glad i was introduced to this place and this is a rare gem in ny .\n->", + "output": "{\"text\": \"i ' m glad i was introduced to this place and this is a rare gem in ny .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'glad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very helpful and it is very fast .\n->it is very helpful and it is very fast .\n[{'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: However , service was as plain as sesame crusted Salmon I had .\n->However , service was as plain as sesame crusted Salmon I had .\n[{'aspect': 'service', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'sesame crusted Salmon', 'opinion': 'plain', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the freshest , best variety , and the fastest delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe freshest , best variety , and the fastest delivery .\n->", + "output": "{\"text\": \"the freshest , best variety , and the fastest delivery .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: short charging cable .\n->short charging cable .\n[{'aspect': 'charging cable', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\nExample:\ntext: for me has been worth the $ 500 for the computer .\n->for me has been worth the $ 500 for the computer .\n[{'aspect': 'computer', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: also very inexpensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso very inexpensive .\n->", + "output": "{\"text\": \"also very inexpensive .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: overall a good chromebook .\n->overall a good chromebook .\n[{'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the service was excellent and the food was delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was excellent and the food was delicious .\n->", + "output": "{\"text\": \"the service was excellent and the food was delicious .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n->If you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you 'll love it here .\n[{'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bottle minimun', 'opinion': 'love', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: even with the fan and heat if you use a cool mat on your lap you will be find especially with the screen being amazing for the price .\n->even with the fan and heat if you use a cool mat on your lap you will be find especially with the screen being amazing for the price .\n[{'aspect': 'fan', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}, {'aspect': 'screen', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\ntext: we are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->", + "output": "{\"text\": \"we are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'two types of sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great bagels made the old - fashioned way .\n->great bagels made the old - fashioned way .\n[{'aspect': 'bagels', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: but $ 500 for a dinner for two that did n ' t include wine ?\n->but $ 500 for a dinner for two that did n ' t include wine ?\n[{'aspect': 'dinner for two', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: definitely a great spot for a nice occasion or date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely a great spot for a nice occasion or date .\n->", + "output": "{\"text\": \"definitely a great spot for a nice occasion or date .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the scallion pancakes and fried dumplings were nothing out of the ordinary .\n->the scallion pancakes and fried dumplings were nothing out of the ordinary .\n[{'aspect': 'scallion pancakes', 'opinion': 'ordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'fried dumplings', 'opinion': 'nothing out of the ordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i find the screen resolution to be very good for video .\n->i find the screen resolution to be very good for video .\n[{'aspect': 'screen resolution', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: average to good thai food , but terrible delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \naverage to good thai food , but terrible delivery .\n->", + "output": "{\"text\": \"average to good thai food , but terrible delivery .\", \"labels\": \"[{'aspect': 'thai food', 'opinion': 'average to good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I got the $ 10 10-piece dim sum combo , every bite of which was great .\n->I got the $ 10 10-piece dim sum combo , every bite of which was great .\n[{'aspect': 'dim sum combo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: biggest gripe , no backlights on the keyboard .\n->biggest gripe , no backlights on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i ' ve waited over one hour for food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve waited over one hour for food .\n->", + "output": "{\"text\": \"i ' ve waited over one hour for food .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n->i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n->i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: they were very abrupt with me when i called and actually claimed the food was late because they were out of rice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey were very abrupt with me when i called and actually claimed the food was late because they were out of rice .\n->", + "output": "{\"text\": \"they were very abrupt with me when i called and actually claimed the food was late because they were out of rice .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'abrupt', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n->This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n[{'aspect': 'Jazz', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: yes , this chromebook comes with the android app store pre - installed .\n->yes , this chromebook comes with the android app store pre - installed .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: a thai restaurant out of rice during dinner ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na thai restaurant out of rice during dinner ?\n->", + "output": "{\"text\": \"a thai restaurant out of rice during dinner ?\", \"labels\": \"[{'aspect': 'thai restaurant', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the inside is great but i feel like it won ' t last long enough before the outside crumbles .\n->the inside is great but i feel like it won ' t last long enough before the outside crumbles .\n[{'aspect': 'inside', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: great indian food and the service is incredible .\n->great indian food and the service is incredible .\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the food arrived 20 minutes after i called , cold and soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food arrived 20 minutes after i called , cold and soggy .\n->", + "output": "{\"text\": \"the food arrived 20 minutes after i called , cold and soggy .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: basically , it is great for work and media as long as you dont need other proprietary programs to do your work .\n->basically , it is great for work and media as long as you dont need other proprietary programs to do your work .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is a fanless processor so there are no vents or openings of any kind which makes this exceptionally quiet as well .\n->it is a fanless processor so there are no vents or openings of any kind which makes this exceptionally quiet as well .\n[{'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#DESIGN_FEATURES'}]\ntext: this is a wonderful place on all stand points especially value ofr money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a wonderful place on all stand points especially value ofr money .\n->", + "output": "{\"text\": \"this is a wonderful place on all stand points especially value ofr money .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'place', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is incredibly lightweight and slim ( ultimately won me over after years of lugging around a whopping 5lb levono ) .\n->this laptop is incredibly lightweight and slim ( ultimately won me over after years of lugging around a whopping 5lb levono ) .\n[{'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: will never buy an asus product again .\n->will never buy an asus product again .\n[{'aspect': 'asus product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: an excellent service\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nan excellent service\n->", + "output": "{\"text\": \"an excellent service\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service is top notch .\n->Service is top notch .\n[{'aspect': 'Service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: truly the mark of an attentive waiter .\n->truly the mark of an attentive waiter .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: we were greeted promptly by the waiter who was very nice and cordial .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were greeted promptly by the waiter who was very nice and cordial .\n->", + "output": "{\"text\": \"we were greeted promptly by the waiter who was very nice and cordial .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'cordial', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n->this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n[{'aspect': 'chromebook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: this is a dreadful little piece of machinery .\n->this is a dreadful little piece of machinery .\n[{'aspect': 'machinery', 'opinion': 'dreadful', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n->", + "output": "{\"text\": \"she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We thought that this place is using too much of MSG cooking in the foods .\n->We thought that this place is using too much of MSG cooking in the foods .\n[{'aspect': 'MSG cooking', 'opinion': 'too much', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: nothing on it feels cheap at all .\n->nothing on it feels cheap at all .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the food looked very appetizing and delicious since it came on a variety of fancy plates .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food looked very appetizing and delicious since it came on a variety of fancy plates .\n->", + "output": "{\"text\": \"the food looked very appetizing and delicious since it came on a variety of fancy plates .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hands down the best pizza on the planet .\n->hands down the best pizza on the planet .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: 1st , i was shocked at how easy it was to set up .\n->1st , i was shocked at how easy it was to set up .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\n->", + "output": "{\"text\": \"we ended our great experience by having gulab jamun ( dessert ) recommended by the waiter .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'gulab jamun ( dessert )', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the m3 processor is * plenty * of power for all chrome , video , and android requirements .\n->- the m3 processor is * plenty * of power for all chrome , video , and android requirements .\n[{'aspect': 'm3 processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#QUALITY'}]\nExample:\ntext: first , it is hard to run more than 10 tabs open at any given time .\n->first , it is hard to run more than 10 tabs open at any given time .\n[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n->", + "output": "{\"text\": \"i thanked my friend who recommended me this restaurant and will certainly recommend it to others .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n->6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n[{'aspect': 'hd touch', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'intel celeron n3150', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'CPU#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#GENERAL'}, {'aspect': 'cb5 - 132t - c1lk', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: its location is good and the fact that hutner college is near and their prices are very reasonable , makes students go back to suan again and again .\n->its location is good and the fact that hutner college is near and their prices are very reasonable , makes students go back to suan again and again .\n[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: service here was great , food was fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice here was great , food was fantastic .\n->", + "output": "{\"text\": \"service here was great , food was fantastic .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff ignored my friends and I the entire time we were there .\n->The staff ignored my friends and I the entire time we were there .\n[{'aspect': 'staff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: guacamole + shrimp appetizer was really great , we both had the filet , very good , did n ' t much like the frites that came with , but the filet was so good , neither of us cared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nguacamole + shrimp appetizer was really great , we both had the filet , very good , did n ' t much like the frites that came with , but the filet was so good , neither of us cared .\n->", + "output": "{\"text\": \"guacamole + shrimp appetizer was really great , we both had the filet , very good , did n ' t much like the frites that came with , but the filet was so good , neither of us cared .\", \"labels\": \"[{'aspect': 'guacamole + shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'frites', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n->Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n[{'aspect': 'seating in the garden', 'opinion': 'lie', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'seats', 'opinion': 'not available', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: my wife had barely touched that mess of a dish .\n->my wife had barely touched that mess of a dish .\n[{'aspect': 'dish', 'opinion': 'mess', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: will absolutely visit again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill absolutely visit again .\n->", + "output": "{\"text\": \"will absolutely visit again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good , fast service .\n->good , fast service .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The white bean brushetta to start was incredible and the pasta was phenomenal .\n->The white bean brushetta to start was incredible and the pasta was phenomenal .\n[{'aspect': 'white bean brushetta', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you can not go wrong with this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can not go wrong with this place .\n->", + "output": "{\"text\": \"you can not go wrong with this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was great as well .\n->The service was great as well .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the start up process was very simple and relatively quick .\n->the start up process was very simple and relatively quick .\n[{'aspect': 'start up', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the food is outstanding and the service is quick , friendly and very professional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is outstanding and the service is quick , friendly and very professional .\n->", + "output": "{\"text\": \"the food is outstanding and the service is quick , friendly and very professional .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .\n->this place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'the', 'opinion': 'is', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'the', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n->We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n[{'aspect': 'waitress', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'servants', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}]\ntext: always a nice crowd , but never loud .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalways a nice crowd , but never loud .\n->", + "output": "{\"text\": \"always a nice crowd , but never loud .\", \"labels\": \"[{'aspect': 'crowd', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'crowd', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - system settings could be more robust and better organized\n->- system settings could be more robust and better organized\n[{'aspect': 'system settings', 'opinion': 'robust', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'system settings', 'opinion': 'better', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}]\nExample:\ntext: Planet Thai is great !\n->Planet Thai is great !\n[{'aspect': 'Planet Thai', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: go here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngo here .\n->", + "output": "{\"text\": \"go here .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s fast at processing , fast for web browsing and has a quick startup .\n->it ' s fast at processing , fast for web browsing and has a quick startup .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n->There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n[{'aspect': 'table', 'opinion': 'long wait', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'insde table', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}]\ntext: good for dates or with friends .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood for dates or with friends .\n->", + "output": "{\"text\": \"good for dates or with friends .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: very happy with this purchase\n->very happy with this purchase\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i am reluctant to write because i would not want my jem of a pizza place to become overcrowded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am reluctant to write because i would not want my jem of a pizza place to become overcrowded .\n->", + "output": "{\"text\": \"i am reluctant to write because i would not want my jem of a pizza place to become overcrowded .\", \"labels\": \"[{'aspect': 'pizza place', 'opinion': 'overcrowded', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they do n ' t concern much of customer ' s health , just want to make money .\n->they do n ' t concern much of customer ' s health , just want to make money .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: let me tell you , this thing is snappy .\n->let me tell you , this thing is snappy .\n[{'aspect': 'NULL', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: however , it is jus too good to not praise it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , it is jus too good to not praise it .\n->", + "output": "{\"text\": \"however , it is jus too good to not praise it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ca n ' t wait for summer , when they serve outside on their gigantic patio .\n->i ca n ' t wait for summer , when they serve outside on their gigantic patio .\n[{'aspect': 'patio', 'opinion': 'gigantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n->Excellent atmosphere , delicious dishes good and friendly service .\n[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: by far , the best pizza in manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nby far , the best pizza in manhattan .\n->", + "output": "{\"text\": \"by far , the best pizza in manhattan .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard feels good .\n->keyboard feels good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\n->Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\n[{'aspect': 'vegetarian dishes', 'opinion': 'not up to par', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the crust is thin , the ingredients are fresh and the staff is friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe crust is thin , the ingredients are fresh and the staff is friendly .\n->", + "output": "{\"text\": \"the crust is thin , the ingredients are fresh and the staff is friendly .\", \"labels\": \"[{'aspect': 'crust', 'opinion': 'thin', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ingredients', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Always popular , always full , always a wait .\n->Always popular , always full , always a wait .\n[{'aspect': 'wait', 'opinion': 'always', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: once i got it set up it has been very nice .\n->once i got it set up it has been very nice .\n[{'aspect': 'set up', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the menu has so many fish items and oysters .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe menu has so many fish items and oysters .\n->", + "output": "{\"text\": \"the menu has so many fish items and oysters .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this by far one of the best laptops i ' ve ever purchased .\n->this by far one of the best laptops i ' ve ever purchased .\n[{'aspect': 'laptops', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n->I must say it 's a little pricey for the food because it was not as spectacular as the view .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the fish was really , really fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fish was really , really fresh .\n->", + "output": "{\"text\": \"the fish was really , really fresh .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just straight up cheap , good food .\n->just straight up cheap , good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: good looking laptop but hardware has several major problems .\n->good looking laptop but hardware has several major problems .\n[{'aspect': 'laptop', 'opinion': 'good looking', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'hardware', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: we all agreed that mare is one of the best seafood restaurants in new york .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe all agreed that mare is one of the best seafood restaurants in new york .\n->", + "output": "{\"text\": \"we all agreed that mare is one of the best seafood restaurants in new york .\", \"labels\": \"[{'aspect': 'mare', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is great if you use half of the brightness .\n->battery life is great if you use half of the brightness .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: food was amazing - i love indian food and eat it quite regularly , but i can say this is one of the best i ' ve had .\n->food was amazing - i love indian food and eat it quite regularly , but i can say this is one of the best i ' ve had .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i stumbled upon this great pizzeria as i explored my new neighborhood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni stumbled upon this great pizzeria as i explored my new neighborhood .\n->", + "output": "{\"text\": \"i stumbled upon this great pizzeria as i explored my new neighborhood .\", \"labels\": \"[{'aspect': 'pizzeria', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is good , i ca n ' t lie .\n->the food is good , i ca n ' t lie .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: who has room for cheesesticks with the best pizza in nyc !\n->who has room for cheesesticks with the best pizza in nyc !\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: all of the pizzas are terrific and the price is even better !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall of the pizzas are terrific and the price is even better !\n->", + "output": "{\"text\": \"all of the pizzas are terrific and the price is even better !\", \"labels\": \"[{'aspect': 'pizzas', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n->i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n[{'aspect': 'NULL', 'opinion': 'biased', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i book a gorgeous white organza tent which included a four course prix fix menu which we enjoyed a lot .\n->i book a gorgeous white organza tent which included a four course prix fix menu which we enjoyed a lot .\n[{'aspect': 'four course prix fix menu', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'white organza tent', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i highly recommend the sophia pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend the sophia pizza .\n->", + "output": "{\"text\": \"i highly recommend the sophia pizza .\", \"labels\": \"[{'aspect': 'sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: another problem i had was when i awakened my computer from sleeping , the wifi would not work .\n->another problem i had was when i awakened my computer from sleeping , the wifi would not work .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: I LOVE their Thai\n->I LOVE their Thai\n[{'aspect': 'Thai', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s to die for !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s to die for !\n->", + "output": "{\"text\": \"it ' s to die for !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can carry both of them in a reasonably sized purse and not hurt my shoulder .\n->i can carry both of them in a reasonably sized purse and not hurt my shoulder .\n[{'aspect': 'NULL', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: it is fast and lightweight .\n->it is fast and lightweight .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'it', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the food was mediocre at best but it was the horrible service that made me vow never to go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was mediocre at best but it was the horrible service that made me vow never to go back .\n->", + "output": "{\"text\": \"the food was mediocre at best but it was the horrible service that made me vow never to go back .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 2 ssd as it will not fit the slot available .\n->2 ssd as it will not fit the slot available .\n[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\nExample:\ntext: all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n->all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nimmediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n->", + "output": "{\"text\": \"immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n->Ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i just got this yesterday and i am very satisfied with the speed .\n->i just got this yesterday and i am very satisfied with the speed .\n[{'aspect': 'speed', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: so rushing us out was absolutely unnecessary !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso rushing us out was absolutely unnecessary !\n->", + "output": "{\"text\": \"so rushing us out was absolutely unnecessary !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and $ 11 for a plate of bland guacamole ?\n->and $ 11 for a plate of bland guacamole ?\n[{'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i ' m no expert on screens but i personally think the panel looks very nice .\n->i ' m no expert on screens but i personally think the panel looks very nice .\n[{'aspect': 'panel', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: for the people who want great food plus great service , roxy is a place to avoid !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the people who want great food plus great service , roxy is a place to avoid !\n->", + "output": "{\"text\": \"for the people who want great food plus great service , roxy is a place to avoid !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n->We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good creative rolls !\n->good creative rolls !\n[{'aspect': 'rolls', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the first time the sushi was outstanding , the second time it was a little bland .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe first time the sushi was outstanding , the second time it was a little bland .\n->", + "output": "{\"text\": \"the first time the sushi was outstanding , the second time it was a little bland .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'outstanding', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was mediocre at best but it was the horrible service that made me vow never to go back .\n->The food was mediocre at best but it was the horrible service that made me vow never to go back .\n[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The fried dumplings are GREAT !\n->The fried dumplings are GREAT !\n[{'aspect': 'fried dumplings', 'opinion': 'GREAT', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m sure i ' ll return for a final judgement tho .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m sure i ' ll return for a final judgement tho .\n->", + "output": "{\"text\": \"i ' m sure i ' ll return for a final judgement tho .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Butter was melted , white wine warm , cheese oozing everywhere .\n->Butter was melted , white wine warm , cheese oozing everywhere .\n[{'aspect': 'Butter', 'opinion': 'melted', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'white wine', 'opinion': 'warm', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'oozing', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The service was attentive , yet discreet .\n->The service was attentive , yet discreet .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the blond wood decor is very soothing , the premium sake is excellent and the service is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe blond wood decor is very soothing , the premium sake is excellent and the service is great .\n->", + "output": "{\"text\": \"the blond wood decor is very soothing , the premium sake is excellent and the service is great .\", \"labels\": \"[{'aspect': 'blond wood decor', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'premium sake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice atmosphere , the service was very pleasant and the desert was good .\n->nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the trackpad was very glitchy .\n->the trackpad was very glitchy .\n[{'aspect': 'trackpad', 'opinion': 'glitchy', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: mizu is the japenese find in grammercy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmizu is the japenese find in grammercy .\n->", + "output": "{\"text\": \"mizu is the japenese find in grammercy .\", \"labels\": \"[{'aspect': 'mizu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * battery - i can not really speak to the battery life yet , but as a power user with lots of tabs and several apps open , it is giving me a days use 5 + hours of use so far .\n->* battery - i can not really speak to the battery life yet , but as a power user with lots of tabs and several apps open , it is giving me a days use 5 + hours of use so far .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n->it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n[{'aspect': 'spinach', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: while their kitchen food is delicious , their sushi is out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile their kitchen food is delicious , their sushi is out of this world .\n->", + "output": "{\"text\": \"while their kitchen food is delicious , their sushi is out of this world .\", \"labels\": \"[{'aspect': 'kitchen food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n->the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n[{'aspect': 'crust', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'light', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it is very good to use in korea .\n->it is very good to use in korea .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: mizu is home to creative and unique rolls not to found anywhere else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmizu is home to creative and unique rolls not to found anywhere else .\n->", + "output": "{\"text\": \"mizu is home to creative and unique rolls not to found anywhere else .\", \"labels\": \"[{'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Salads are a delicious way to begin the meal .\n->Salads are a delicious way to begin the meal .\n[{'aspect': 'Salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the laptop looks beautiful and the 8th gen intel core is a performance powerhouse .\n->the laptop looks beautiful and the 8th gen intel core is a performance powerhouse .\n[{'aspect': 'laptop', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '8th gen intel core', 'opinion': 'powerhouse', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: not only is the cuisine the best around , the service has always been attentive and charming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only is the cuisine the best around , the service has always been attentive and charming .\n->", + "output": "{\"text\": \"not only is the cuisine the best around , the service has always been attentive and charming .\", \"labels\": \"[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer had no answer for that question .\n->acer had no answer for that question .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n->overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n[{'aspect': 'computer', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: warning : you may find it difficult to dine at other japanese restaurants after a visit to mizu !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwarning : you may find it difficult to dine at other japanese restaurants after a visit to mizu !\n->", + "output": "{\"text\": \"warning : you may find it difficult to dine at other japanese restaurants after a visit to mizu !\", \"labels\": \"[{'aspect': 'mizu', 'opinion': 'difficult', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: msi should be ashamed at putting out products they know have issues with no intent of correcting the problem during the building and testing phase .\n->msi should be ashamed at putting out products they know have issues with no intent of correcting the problem during the building and testing phase .\n[{'aspect': 'msi', 'opinion': 'ashamed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'products', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i came across village underground by accident , now i go there all the time .\n->i came across village underground by accident , now i go there all the time .\n[{'aspect': 'village underground', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the entree was bland and small , dessert was not inspired .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe entree was bland and small , dessert was not inspired .\n->", + "output": "{\"text\": \"the entree was bland and small , dessert was not inspired .\", \"labels\": \"[{'aspect': 'entree', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'entree', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'dessert', 'opinion': 'not inspired', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is a must visit !\n->this place is a must visit !\n[{'aspect': 'place', 'opinion': 'must visit', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I loved everythig about it-especially the shows and actors .\n->I loved everythig about it-especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i expected quite a bit more from such an expensive menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni expected quite a bit more from such an expensive menu .\n->", + "output": "{\"text\": \"i expected quite a bit more from such an expensive menu .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recommend it , definitely\n->i recommend it , definitely\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it randomly shuts down all programs running and goes back to the desktop like nothing was going on .\n->it randomly shuts down all programs running and goes back to the desktop like nothing was going on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the view is spectacular , and the food is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe view is spectacular , and the food is great .\n->", + "output": "{\"text\": \"the view is spectacular , and the food is great .\", \"labels\": \"[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Somehow working the italian charm with constant mille grazie does not constitute proper service .\n->Somehow working the italian charm with constant mille grazie does not constitute proper service .\n[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: had it for a week now and still finding things it can not do .\n->had it for a week now and still finding things it can not do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: wonderful strawberry daiquiries as well !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwonderful strawberry daiquiries as well !\n->", + "output": "{\"text\": \"wonderful strawberry daiquiries as well !\", \"labels\": \"[{'aspect': 'strawberry daiquiries', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is so much fun .\n->this place is so much fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: laptop screen goes blank after four weeks minimally used .\n->laptop screen goes blank after four weeks minimally used .\n[{'aspect': 'laptop screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: definitely worth the trip to battery park city !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely worth the trip to battery park city !\n->", + "output": "{\"text\": \"definitely worth the trip to battery park city !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even though the restaurant was packed , we were seated promptly and even asked for a table upstairs with no problems .\n->Even though the restaurant was packed , we were seated promptly and even asked for a table upstairs with no problems .\n[{'aspect': 'seated', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what you are paying for is the environment and the name .\n->what you are paying for is the environment and the name .\n[{'aspect': 'environment', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: one of my favorite places in manhattan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of my favorite places in manhattan .\n->", + "output": "{\"text\": \"one of my favorite places in manhattan .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: back lit keyboard should be a standard by now !\n->back lit keyboard should be a standard by now !\n[{'aspect': 'back lit keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: the food was average or above including some surprising tasty dishes .\n->the food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: authentic taiwanese food that ' s cheap . . . what more could you ask for ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nauthentic taiwanese food that ' s cheap . . . what more could you ask for ?\n->", + "output": "{\"text\": \"authentic taiwanese food that ' s cheap . . . what more could you ask for ?\", \"labels\": \"[{'aspect': 'taiwanese food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'taiwanese food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best Reuben sandwich ever !\n->Best Reuben sandwich ever !\n[{'aspect': 'Reuben sandwich', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery life is phenomenal again .\n->battery life is phenomenal again .\n[{'aspect': 'battery life', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: ( besides that there should be more restaurants like it around the city ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( besides that there should be more restaurants like it around the city ) .\n->", + "output": "{\"text\": \"( besides that there should be more restaurants like it around the city ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': \"ca n't be beat\", 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the level of rudeness was preposterous .\n->the level of rudeness was preposterous .\n[{'aspect': 'NULL', 'opinion': 'preposterous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n->", + "output": "{\"text\": \"the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\", \"labels\": \"[{'aspect': 'cold appetizer dishes', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mac pro is also very fast , and i have only experienced the rainbow wheel once or twice whenever i was on a website that wasn ' t responding .\n->the mac pro is also very fast , and i have only experienced the rainbow wheel once or twice whenever i was on a website that wasn ' t responding .\n[{'aspect': 'mac pro', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n->we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: delicious simple food in nice outdoor atmosphere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicious simple food in nice outdoor atmosphere .\n->", + "output": "{\"text\": \"delicious simple food in nice outdoor atmosphere .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'outdoor atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was not fresh , the sauces were bland and very oily .\n->The food was not fresh , the sauces were bland and very oily .\n[{'aspect': 'food', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauces', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n->needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: kind , attentive wait staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkind , attentive wait staff .\n->", + "output": "{\"text\": \"kind , attentive wait staff .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely a steal at the price i bought this for .\n->definitely a steal at the price i bought this for .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: This is my first time writing a review for a restaurant because the food and service was excellent .\n->This is my first time writing a review for a restaurant because the food and service was excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n->", + "output": "{\"text\": \"i really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\", \"labels\": \"[{'aspect': 'scallops', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mahi mahi ( on saffron risotto', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s pretty light , too , so it ' s easy to travel with .\n->it ' s pretty light , too , so it ' s easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: they bring service up a notch by offerng complementary amuse bouche to all tables and gave us a small dessert for our celebration .\n->they bring service up a notch by offerng complementary amuse bouche to all tables and gave us a small dessert for our celebration .\n[{'aspect': 'amuse bouche', 'opinion': 'complementary', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my friend devoured her chicken and mashed potatos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy friend devoured her chicken and mashed potatos .\n->", + "output": "{\"text\": \"my friend devoured her chicken and mashed potatos .\", \"labels\": \"[{'aspect': 'chicken and mashed potatos', 'opinion': 'devoured', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We were disappointed with the pre-fixe menu of only 2 choices per course ( other restaurants offer 3 choices ) and ended up ordering a la carte .\n->We were disappointed with the pre-fixe menu of only 2 choices per course ( other restaurants offer 3 choices ) and ended up ordering a la carte .\n[{'aspect': 'pre-fixe menu', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'choices per course', 'opinion': 'disappointed', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Best Pastrami I ever had and great portion without being ridiculous .\n->Best Pastrami I ever had and great portion without being ridiculous .\n[{'aspect': 'Pastrami', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: delicious crab cakes too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicious crab cakes too .\n->", + "output": "{\"text\": \"delicious crab cakes too .\", \"labels\": \"[{'aspect': 'crab cakes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The shrimp scampi was excellent and the antipasti were plentiful .\n->The shrimp scampi was excellent and the antipasti were plentiful .\n[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Pizza here is consistently good .\n->Pizza here is consistently good .\n[{'aspect': 'Pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: even if the food was n ' t this good , the garden is a great place to sit outside and relax .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven if the food was n ' t this good , the garden is a great place to sit outside and relax .\n->", + "output": "{\"text\": \"even if the food was n ' t this good , the garden is a great place to sit outside and relax .\", \"labels\": \"[{'aspect': 'garden', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': \"n ' t this good\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service is average .\n->Service is average .\n[{'aspect': 'Service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n->You must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n[{'aspect': 'crabmeat lasagna', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great neighborhood joint .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat neighborhood joint .\n->", + "output": "{\"text\": \"great neighborhood joint .\", \"labels\": \"[{'aspect': 'joint', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Eating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\n->Eating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\n[{'aspect': 'atmosphere', 'opinion': 'saves', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n->Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n[{'aspect': 'space', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is a nice pizza place with good selection of thin crust pizza including the basil slice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a nice pizza place with good selection of thin crust pizza including the basil slice .\n->", + "output": "{\"text\": \"this is a nice pizza place with good selection of thin crust pizza including the basil slice .\", \"labels\": \"[{'aspect': 'selection of thin crust pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'selection of thin crust pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'basil slice', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend this beautiful place .\n->i highly recommend this beautiful place .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: apps open instantly and the ac wifi performance is very nice .\n->apps open instantly and the ac wifi performance is very nice .\n[{'aspect': 'apps', 'opinion': 'instantly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'ac wifi', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: their calzones are horrific , bad , vomit - inducing , yuck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntheir calzones are horrific , bad , vomit - inducing , yuck .\n->", + "output": "{\"text\": \"their calzones are horrific , bad , vomit - inducing , yuck .\", \"labels\": \"[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'calzones', 'opinion': 'vomit - inducing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'calzones', 'opinion': 'yuck', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is between 4 to 7 hours depending on what i ' m doing .\n->battery life is between 4 to 7 hours depending on what i ' m doing .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Judging from previous posts this used to be a good place , but not any longer .\n->Judging from previous posts this used to be a good place , but not any longer .\n[{'aspect': 'place', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: they smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\n->", + "output": "{\"text\": \"they smell like they stuff them with old canned vegetables like the spinach mushroom calzone .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the problem is that it never charged .\n->the problem is that it never charged .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the waitress , seems to be more concerned of looking good than actually waitressing .\n->the waitress , seems to be more concerned of looking good than actually waitressing .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the counter service is bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe counter service is bad .\n->", + "output": "{\"text\": \"the counter service is bad .\", \"labels\": \"[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in preparation for becoming a chromebook user i had purchased a 256 gig memory card and moved all my laptop files to it .\n->in preparation for becoming a chromebook user i had purchased a 256 gig memory card and moved all my laptop files to it .\n[{'aspect': 'memory card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: photoshop also runs very well .\n->photoshop also runs very well .\n[{'aspect': 'photoshop', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: they charge different prices all the time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey charge different prices all the time .\n->", + "output": "{\"text\": \"they charge different prices all the time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love al di la\n->love al di la\n[{'aspect': 'al di la', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: extremely disappointed as this was a gift to my husband .\n->extremely disappointed as this was a gift to my husband .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: they ' re rude at times , and not very friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey ' re rude at times , and not very friendly .\n->", + "output": "{\"text\": \"they ' re rude at times , and not very friendly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'not very friendly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Also good for client lunch meetings , esp .\n->Also good for client lunch meetings , esp .\n[{'aspect': 'lunch meetings', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: for me has been worth the $ 500 for the computer .\n->for me has been worth the $ 500 for the computer .\n[{'aspect': 'computer', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: no pizza 33 for me !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno pizza 33 for me !\n->", + "output": "{\"text\": \"no pizza 33 for me !\", \"labels\": \"[{'aspect': 'pizza 33', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portions are small but being that the food was so good makes up for that .\n->The portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n->Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n[{'aspect': 'turnip soup with pureed basil', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: anybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nanybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .\n->", + "output": "{\"text\": \"anybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place , however , has a lot less pretension than Joya and the Thai food is still above average .\n->This place , however , has a lot less pretension than Joya and the Thai food is still above average .\n[{'aspect': 'Thai food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: so i call asus customer support , and received some of the worst customer service ever .\n->so i call asus customer support , and received some of the worst customer service ever .\n[{'aspect': 'asus customer support', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: the dosas are skimpy , unattractive and drip with grease , and personally i ' d drink popcorn topping before i ' d eat another one of these .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dosas are skimpy , unattractive and drip with grease , and personally i ' d drink popcorn topping before i ' d eat another one of these .\n->", + "output": "{\"text\": \"the dosas are skimpy , unattractive and drip with grease , and personally i ' d drink popcorn topping before i ' d eat another one of these .\", \"labels\": \"[{'aspect': 'dosas', 'opinion': 'skimpy', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'dosas', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not impressed with the food .\n->not impressed with the food .\n[{'aspect': 'food', 'opinion': 'not impressed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I always get the Shabu-Shabu dinner and the beef is always fresh .\n->I always get the Shabu-Shabu dinner and the beef is always fresh .\n[{'aspect': 'beef', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the sandwiches are dry , tasteless and way overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sandwiches are dry , tasteless and way overpriced .\n->", + "output": "{\"text\": \"the sandwiches are dry , tasteless and way overpriced .\", \"labels\": \"[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: adobe apps are also power hogs , but that is to be expected .\n->adobe apps are also power hogs , but that is to be expected .\n[{'aspect': 'adobe apps', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: after really enjoying ourselves at the bar we sat down at a table and had dinner .\n->after really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncalling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n->", + "output": "{\"text\": \"calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'room', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'clerks', 'opinion': 'unhelpful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , I think this place is a good hang out spot .\n->However , I think this place is a good hang out spot .\n[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: price is high but the food is good , so i would come back again .\n->price is high but the food is good , so i would come back again .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'high', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: seriously , this place kicks ass .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nseriously , this place kicks ass .\n->", + "output": "{\"text\": \"seriously , this place kicks ass .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'kicks ass', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keys are a bit thin and have an odd feel to them .\n->keys are a bit thin and have an odd feel to them .\n[{'aspect': 'keys', 'opinion': 'thin', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: this one is horrible , never can connect .\n->this one is horrible , never can connect .\n[{'aspect': 'NULL', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the atmosphere is unheralded , the service impecible , and the food magnificant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe atmosphere is unheralded , the service impecible , and the food magnificant .\n->", + "output": "{\"text\": \"the atmosphere is unheralded , the service impecible , and the food magnificant .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'impecible', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was superb , they treat you like family .\n->The service was superb , they treat you like family .\n[{'aspect': 'service', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n->I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n[{'aspect': 'Areo', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: best italian food i ever had ( and being italian , that means alot ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest italian food i ever had ( and being italian , that means alot ) .\n->", + "output": "{\"text\": \"best italian food i ever had ( and being italian , that means alot ) .\", \"labels\": \"[{'aspect': 'italian food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the unit is whisper quiet and hasn ' t gotten hot no matter how hard i push it .\n->the unit is whisper quiet and hasn ' t gotten hot no matter how hard i push it .\n[{'aspect': 'unit', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the item is good but the sound of speakers is very low .\n->the item is good but the sound of speakers is very low .\n[{'aspect': 'item', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound of speakers', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: it is nearly impossible to get a table , so if you ever have the chance to go here for dinner , do not pass it up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is nearly impossible to get a table , so if you ever have the chance to go here for dinner , do not pass it up .\n->", + "output": "{\"text\": \"it is nearly impossible to get a table , so if you ever have the chance to go here for dinner , do not pass it up .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely glad i purchased my mac .\n->definitely glad i purchased my mac .\n[{'aspect': 'mac', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i plugged it back in , let it fully charge as directed and have had no problems since .\n->i plugged it back in , let it fully charge as directed and have had no problems since .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this is such a lovely , peaceful place to eat outside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is such a lovely , peaceful place to eat outside .\n->", + "output": "{\"text\": \"this is such a lovely , peaceful place to eat outside .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'peaceful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n->I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n[{'aspect': 'Indian dining experience', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it can not .\n->it can not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the restaurant looks out over beautiful green lawns to the hudson river and the statue of liberty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe restaurant looks out over beautiful green lawns to the hudson river and the statue of liberty .\n->", + "output": "{\"text\": \"the restaurant looks out over beautiful green lawns to the hudson river and the statue of liberty .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: back lit keyboard should be a standard by now !\n->back lit keyboard should be a standard by now !\n[{'aspect': 'back lit keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: this place survives on reputation alone .\n->this place survives on reputation alone .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: it is set far from the small street it ' s on , and there is no traffic noise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is set far from the small street it ' s on , and there is no traffic noise .\n->", + "output": "{\"text\": \"it is set far from the small street it ' s on , and there is no traffic noise .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Wine list selection is good and wine-by-the-glass was generously filled to the top .\n->Wine list selection is good and wine-by-the-glass was generously filled to the top .\n[{'aspect': 'Wine list selection', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine-by-the-glass', 'opinion': 'generously filled', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a restaurant that does n ' t try to do anything except serve great food with great service in a pleasant atmosphere .\n->a restaurant that does n ' t try to do anything except serve great food with great service in a pleasant atmosphere .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: this is a great place to take out - of - towners , and perfect for watching the sunset .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great place to take out - of - towners , and perfect for watching the sunset .\n->", + "output": "{\"text\": \"this is a great place to take out - of - towners , and perfect for watching the sunset .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fantastic computer !\n->fantastic computer !\n[{'aspect': 'computer', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this place . . . god where do i begin .\n->this place . . . god where do i begin .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: great sushi experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat sushi experience .\n->", + "output": "{\"text\": \"great sushi experience .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i got there the place was packed but they made sure to seat me quickly .\n->when i got there the place was packed but they made sure to seat me quickly .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n->i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n[{'aspect': 'NULL', 'opinion': 'pleasantly suprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: nice value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice value .\n->", + "output": "{\"text\": \"nice value .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Such nice people working here - but I have to review the food .\n->Such nice people working here - but I have to review the food .\n[{'aspect': 'people', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: no plans to return anytime soon .\n->no plans to return anytime soon .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: unique apppetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunique apppetizers .\n->", + "output": "{\"text\": \"unique apppetizers .\", \"labels\": \"[{'aspect': 'apppetizers', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Horrible food and horrible service .\n->Horrible food and horrible service .\n[{'aspect': 'food', 'opinion': 'Horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i was speechless by the horrible food .\n->i was speechless by the horrible food .\n[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: try sushimi cucumber roll .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry sushimi cucumber roll .\n->", + "output": "{\"text\": \"try sushimi cucumber roll .\", \"labels\": \"[{'aspect': 'sushimi cucumber roll', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n->There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n[{'aspect': 'table', 'opinion': 'long wait', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'insde table', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We asked to be moved ( which took half an hour ) , and then were seated in a high traffic area in the back , even though the rest of the room was practically empty .\n->We asked to be moved ( which took half an hour ) , and then were seated in a high traffic area in the back , even though the rest of the room was practically empty .\n[{'aspect': 'room', 'opinion': 'empty', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: good spreads , great beverage selections and bagels really tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood spreads , great beverage selections and bagels really tasty .\n->", + "output": "{\"text\": \"good spreads , great beverage selections and bagels really tasty .\", \"labels\": \"[{'aspect': 'spreads', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beverage selections', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'bagels', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n->most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n[{'aspect': 'apps', 'opinion': 'ok', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: this is the most well priced laptop for its spec\n->this is the most well priced laptop for its spec\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'spec', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: murray wo n ' t do it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmurray wo n ' t do it .\n->", + "output": "{\"text\": \"murray wo n ' t do it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall i love this machine , and all my computers will probably be chromebooks in the future .\n->overall i love this machine , and all my computers will probably be chromebooks in the future .\n[{'aspect': 'machine', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the service was excellent - friendly and attentive .\n->the service was excellent - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: but who says murray ' s is anything about service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut who says murray ' s is anything about service .\n->", + "output": "{\"text\": \"but who says murray ' s is anything about service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The view is spectacular , and the food is great .\n->The view is spectacular , and the food is great .\n[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: What an amazing meal and experience !\n->What an amazing meal and experience !\n[{'aspect': 'meal', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so close , but not good enough .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso close , but not good enough .\n->", + "output": "{\"text\": \"so close , but not good enough .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not good enough', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n->My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n[{'aspect': 'crabmeat', 'opinion': 'unnecessarily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: atmosphere is nice and relaxed too . . .\n->atmosphere is nice and relaxed too . . .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: do n ' t be fooled by crowds of people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo n ' t be fooled by crowds of people .\n->", + "output": "{\"text\": \"do n ' t be fooled by crowds of people .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fooled', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is good .\n->the keyboard is good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: but i fed up with the price it cost to upgrade the product as well as the software .\n->but i fed up with the price it cost to upgrade the product as well as the software .\n[{'aspect': 'NULL', 'opinion': 'fed up', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: the service is awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service is awful .\n->", + "output": "{\"text\": \"the service is awful .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: choose this one .\n->choose this one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' ve had mine 10 months and the motherboard has crapped out twice already .\n->i ' ve had mine 10 months and the motherboard has crapped out twice already .\n[{'aspect': 'motherboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MOTHERBOARD#OPERATION_PERFORMANCE'}]\ntext: this place is not worth the prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is not worth the prices .\n->", + "output": "{\"text\": \"this place is not worth the prices .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my 2nd issues is with it ' s performance .\n->my 2nd issues is with it ' s performance .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the side mounted speakers are clear .\n->the side mounted speakers are clear .\n[{'aspect': 'side mounted speakers', 'opinion': 'clear', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: also , do n ' t plan on asking for your favorite roll , if it ' s not on the menu , you ca n ' t have it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , do n ' t plan on asking for your favorite roll , if it ' s not on the menu , you ca n ' t have it .\n->", + "output": "{\"text\": \"also , do n ' t plan on asking for your favorite roll , if it ' s not on the menu , you ca n ' t have it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was so stunned , and i left the dinner hungry and majorly disappointing .\n->i was so stunned , and i left the dinner hungry and majorly disappointing .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i am so excited about it and hope to see more of your products in the future !\n->i am so excited about it and hope to see more of your products in the future !\n[{'aspect': 'NULL', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: love pizza 33 . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove pizza 33 . . .\n->", + "output": "{\"text\": \"love pizza 33 . . .\", \"labels\": \"[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n->i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: but let me not recommend it .\n->but let me not recommend it .\n[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n->", + "output": "{\"text\": \"i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'crave', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our server was very helpful and friendly .\n->our server was very helpful and friendly .\n[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: as of this writing , the computer ' s dedicated video card is completely non - functional , the computer routinely switches off in the middle of executing a process , and i can ' t even use the hdmi out port .\n->as of this writing , the computer ' s dedicated video card is completely non - functional , the computer routinely switches off in the middle of executing a process , and i can ' t even use the hdmi out port .\n[{'aspect': \"computer ' s dedicated video card\", 'opinion': 'non - functional', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hdmi out port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: it hits the spot every time\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit hits the spot every time\n->", + "output": "{\"text\": \"it hits the spot every time\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'hits', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is very good for it 's price , better than most fried dumplings I 've had .\n->The food is very good for it 's price , better than most fried dumplings I 've had .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried dumplings', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: they seemed to do nothing : fixing it was apparently my job .\n->they seemed to do nothing : fixing it was apparently my job .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: this tiny williamsburg spot is always pleasantly surprising .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis tiny williamsburg spot is always pleasantly surprising .\n->", + "output": "{\"text\": \"this tiny williamsburg spot is always pleasantly surprising .\", \"labels\": \"[{'aspect': 'williamsburg spot', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n->but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n[{'aspect': 'windows 10', 'opinion': 'worst', 'polarity': 'negative', 'category': 'OS#GENERAL'}, {'aspect': 'windows 10', 'opinion': 'awful', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: much of the time it seems like they do not care about you .\n->much of the time it seems like they do not care about you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the pizza is delicious and the proprietor is one of the nicest in nyc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pizza is delicious and the proprietor is one of the nicest in nyc .\n->", + "output": "{\"text\": \"the pizza is delicious and the proprietor is one of the nicest in nyc .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n->i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n[{'aspect': 'flip', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the food was lousy - too sweet or too salty and the portions tiny .\n->the food was lousy - too sweet or too salty and the portions tiny .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'too salty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: they even scoop it out nice ( for those on a diet ) not too much not to little .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey even scoop it out nice ( for those on a diet ) not too much not to little .\n->", + "output": "{\"text\": \"they even scoop it out nice ( for those on a diet ) not too much not to little .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the menu has so many fish items and oysters .\n->the menu has so many fish items and oysters .\n[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n->the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: the cream cheeses are out of this world and i love that coffee ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe cream cheeses are out of this world and i love that coffee ! !\n->", + "output": "{\"text\": \"the cream cheeses are out of this world and i love that coffee ! !\", \"labels\": \"[{'aspect': 'cream cheeses', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'coffee', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard / mousepad isn ' t super comfortable for casual use ( like on your lap , sitting on a couch ) , so i think it is more meant to be used more or less exclusively for gaming at a desk or table .\n->the keyboard / mousepad isn ' t super comfortable for casual use ( like on your lap , sitting on a couch ) , so i think it is more meant to be used more or less exclusively for gaming at a desk or table .\n[{'aspect': 'keyboard / mousepad', 'opinion': \"' t super comfortable\", 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: We had the lobster sandwich and it was FANTASTIC .\n->We had the lobster sandwich and it was FANTASTIC .\n[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a little crowded but they move that line really fast !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na little crowded but they move that line really fast !\n->", + "output": "{\"text\": \"a little crowded but they move that line really fast !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had the best ravioli ever .\n->I had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: As always we had a great glass of wine while we waited .\n->As always we had a great glass of wine while we waited .\n[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a little pricey but it really hits the spot on a sunday morning !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na little pricey but it really hits the spot on a sunday morning !\n->", + "output": "{\"text\": \"a little pricey but it really hits the spot on a sunday morning !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'hits', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not sure why this restaurant would be rated that highly .\n->not sure why this restaurant would be rated that highly .\n[{'aspect': 'restaurant', 'opinion': 'highly', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: looks brand new and the battery life lasts a long time ( see photos )\n->looks brand new and the battery life lasts a long time ( see photos )\n[{'aspect': 'NULL', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: bagels are ok , but be sure not to make any special requests !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbagels are ok , but be sure not to make any special requests !\n->", + "output": "{\"text\": \"bagels are ok , but be sure not to make any special requests !\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try their chef 's specials -- they are to die for .\n->Try their chef 's specials -- they are to die for .\n[{'aspect': \"chef 's specials\", 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"chef 's specials\", 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this issue happens more frequently when using netflix ( app or through chrome browser ) .\n->this issue happens more frequently when using netflix ( app or through chrome browser ) .\n[{'aspect': 'happens', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: i asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !\n->", + "output": "{\"text\": \"i asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The ceiling is amazing !\n->The ceiling is amazing !\n[{'aspect': 'ceiling', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i am very happy with this item .\n->i am very happy with this item .\n[{'aspect': 'item', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: be sure not to get anything other than bagels ! . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbe sure not to get anything other than bagels ! . .\n->", + "output": "{\"text\": \"be sure not to get anything other than bagels ! . .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - timeout on keyboard backlight not adjustable .\n->- timeout on keyboard backlight not adjustable .\n[{'aspect': 'keyboard', 'opinion': 'not adjustable', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n->the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n[{'aspect': 'battery life', 'opinion': 'solid', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'worth', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'quality', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'worth', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\ntext: the worst excuse for japanese food i ' ve ever encountered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe worst excuse for japanese food i ' ve ever encountered .\n->", + "output": "{\"text\": \"the worst excuse for japanese food i ' ve ever encountered .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - 3 months after purchase , the chromebook has issues with the screen flickering constantly and has been sent in to asus for repairs\n->- 3 months after purchase , the chromebook has issues with the screen flickering constantly and has been sent in to asus for repairs\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Their pad penang is delicious and everything else is fantastic .\n->Their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the soup for the udon was soy sauce and water .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe soup for the udon was soy sauce and water .\n->", + "output": "{\"text\": \"the soup for the udon was soy sauce and water .\", \"labels\": \"[{'aspect': 'soup for the udon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the screen is great in all aspects .\n->- the screen is great in all aspects .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: the mousepad was not very responsive .\n->the mousepad was not very responsive .\n[{'aspect': 'mousepad', 'opinion': 'not very responsive', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: the sushi was awful !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sushi was awful !\n->", + "output": "{\"text\": \"the sushi was awful !\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'awful', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hurley ' s is like cheers where everyone knows your name and they are actually glad you came .\n->hurley ' s is like cheers where everyone knows your name and they are actually glad you came .\n[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n->battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: the rice was poor quality and was cooked so badly it was hard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe rice was poor quality and was cooked so badly it was hard .\n->", + "output": "{\"text\": \"the rice was poor quality and was cooked so badly it was hard .\", \"labels\": \"[{'aspect': 'rice', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rice', 'opinion': 'cooked so badly', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rice', 'opinion': 'hard', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen quality is perfect and matte so no annoying glare !\n->screen quality is perfect and matte so no annoying glare !\n[{'aspect': 'screen quality', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n->received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n[{'aspect': 'cpu', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfurthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n->", + "output": "{\"text\": \"furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\", \"labels\": \"[{'aspect': 'rice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n->The service was terrible , we had to wait for everything and ask several of different people for the same thing before we were allowed to be served .\n[{'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the track pad is one of the best i have seen for a non - apple touch pad .\n->the track pad is one of the best i have seen for a non - apple touch pad .\n[{'aspect': 'track pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the fish was adequate , but inexpertly sliced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fish was adequate , but inexpertly sliced .\n->", + "output": "{\"text\": \"the fish was adequate , but inexpertly sliced .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'adequate', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'fish', 'opinion': 'inexpertly sliced', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\n->Do n't dine at Tamarind for the vegetarian dishes , they are simply not up to par with the non-veg selections .\n[{'aspect': 'vegetarian dishes', 'opinion': 'not up to par', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i recommend the thai popcorn : )\n->i recommend the thai popcorn : )\n[{'aspect': 'thai popcorn', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is obvious that no one in the restaurant has any idea about or experience with japanese cuisine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is obvious that no one in the restaurant has any idea about or experience with japanese cuisine .\n->", + "output": "{\"text\": \"it is obvious that no one in the restaurant has any idea about or experience with japanese cuisine .\", \"labels\": \"[{'aspect': 'japanese cuisine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even in the early rounds the frames are heavily compromised and the entire game feels very sluggish .\n->even in the early rounds the frames are heavily compromised and the entire game feels very sluggish .\n[{'aspect': 'NULL', 'opinion': 'sluggish', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: ca n ' t wait to go back .\n->ca n ' t wait to go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: good , fast service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood , fast service .\n->", + "output": "{\"text\": \"good , fast service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will never return .\n->i will never return .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i would highly recommend .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would highly recommend .\n->", + "output": "{\"text\": \"i would highly recommend .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my computer was used on average a couple hours a day .\n->my computer was used on average a couple hours a day .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this was a repeat visit and we ' ll definitely be back again .\n->this was a repeat visit and we ' ll definitely be back again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: food is great and inexpensive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood is great and inexpensive .\n->", + "output": "{\"text\": \"food is great and inexpensive .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n->if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n[{'aspect': 'sushi', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the service is good and ambience is good for a date or group outing .\n->the service is good and ambience is good for a date or group outing .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the location is perfect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe location is perfect .\n->", + "output": "{\"text\": \"the location is perfect .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was the only thing good about this restaurant .\n->The service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n->We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: give it a try and enjoy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngive it a try and enjoy .\n->", + "output": "{\"text\": \"give it a try and enjoy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The cafe itself was really nice with comfortable outdoor chairs and tables , but the service could have been better .\n->The cafe itself was really nice with comfortable outdoor chairs and tables , but the service could have been better .\n[{'aspect': 'cafe', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor chairs', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: go here .\n->go here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: well , this place is so ghetto its not even funny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwell , this place is so ghetto its not even funny .\n->", + "output": "{\"text\": \"well , this place is so ghetto its not even funny .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'ghetto', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'not even funny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just wanted to make it known my personal experiences with the device .\n->i just wanted to make it known my personal experiences with the device .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Patroon features a nice cigar bar and has great staff .\n->Patroon features a nice cigar bar and has great staff .\n[{'aspect': 'cigar bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n->", + "output": "{\"text\": \"if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\", \"labels\": \"[{'aspect': 'bottle', 'opinion': 'love', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}, {'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pretty fast processor .\n->pretty fast processor .\n[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n->also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n[{'aspect': 'place', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: would never go back there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwould never go back there .\n->", + "output": "{\"text\": \"would never go back there .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lucky strike is a great casual place to just grab a bite to eat .\n->lucky strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'lucky strike', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'lucky strike', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the food was average or above including some surprising tasty dishes .\n->the food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: awsome pizza especially the margheritta slice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nawsome pizza especially the margheritta slice .\n->", + "output": "{\"text\": \"awsome pizza especially the margheritta slice .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margheritta slice', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * the screen is more than adequate for me , although i have not used it outside much yet .\n->* the screen is more than adequate for me , although i have not used it outside much yet .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\n->The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\n[{'aspect': 'dosas', 'opinion': 'skimpy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dosas', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: always busy but fast moving .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalways busy but fast moving .\n->", + "output": "{\"text\": \"always busy but fast moving .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n->the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n[{'aspect': 'form factor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n->i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: great atmoshere and worth every bit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat atmoshere and worth every bit .\n->", + "output": "{\"text\": \"great atmoshere and worth every bit .\", \"labels\": \"[{'aspect': 'atmoshere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The one vegetarian entree ( Abby 's treasure ) was actually quite a surprise - it was delicious and had wintermelon covering an assortment of fresh mushrooms and vegetables .\n->The one vegetarian entree ( Abby 's treasure ) was actually quite a surprise - it was delicious and had wintermelon covering an assortment of fresh mushrooms and vegetables .\n[{'aspect': 'vegetarian entree', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetarian entree', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Abby 's treasure\", 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Abby 's treasure\", 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assortment of fresh mushrooms and vegetables', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: here the hot dog is elevated to the level of a real entree with numerous variations available .\n->here the hot dog is elevated to the level of a real entree with numerous variations available .\n[{'aspect': 'hot dog', 'opinion': 'elevated', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dog', 'opinion': 'numerous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: open late ( well as late as i ever got there and i ' m a night person )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nopen late ( well as late as i ever got there and i ' m a night person )\n->", + "output": "{\"text\": \"open late ( well as late as i ever got there and i ' m a night person )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n->My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n[{'aspect': 'cheese', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i did not expect to have to return this product for an exchange the same day i got it .\n->i did not expect to have to return this product for an exchange the same day i got it .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: winnie and her staff are the best crew you can find serving you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwinnie and her staff are the best crew you can find serving you .\n->", + "output": "{\"text\": \"winnie and her staff are the best crew you can find serving you .\", \"labels\": \"[{'aspect': 'winnie', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my wife had barely touched that mess of a dish .\n->my wife had barely touched that mess of a dish .\n[{'aspect': 'dish', 'opinion': 'mess', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: nevertheless , i finished my plate , and that ' s when i found a maggot in mushroom sauce at the bottom .\n->nevertheless , i finished my plate , and that ' s when i found a maggot in mushroom sauce at the bottom .\n[{'aspect': 'mushroom sauce', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the food is reliable and the price is moderate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is reliable and the price is moderate .\n->", + "output": "{\"text\": \"the food is reliable and the price is moderate .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is so cheap and the waiters are nice .\n->The food is so cheap and the waiters are nice .\n[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n->we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: what more can you ask for ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat more can you ask for ?\n->", + "output": "{\"text\": \"what more can you ask for ?\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will not be buying asus again\n->will not be buying asus again\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the original hdd in this laptop has some speed limitations for load up .\n->the original hdd in this laptop has some speed limitations for load up .\n[{'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: everything about this restaurant was special .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything about this restaurant was special .\n->", + "output": "{\"text\": \"everything about this restaurant was special .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'special', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: super yummy pizza !\n->super yummy pizza !\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it boots in seconds , and i get ~ 10 hours out of the battery .\n->it boots in seconds , and i get ~ 10 hours out of the battery .\n[{'aspect': 'boots', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the service was attentive , yet discreet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was attentive , yet discreet .\n->", + "output": "{\"text\": \"the service was attentive , yet discreet .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s light , but also has a good weight to it .\n->it ' s light , but also has a good weight to it .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Terrific menu full of unique rolls and special dishes .\n->Terrific menu full of unique rolls and special dishes .\n[{'aspect': 'menu', 'opinion': 'Terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the flavors robust and subtle .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe flavors robust and subtle .\n->", + "output": "{\"text\": \"the flavors robust and subtle .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'robust', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'subtle', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My turkey burger was not cooked at all , my friends salmon was completely raw .\n->My turkey burger was not cooked at all , my friends salmon was completely raw .\n[{'aspect': 'turkey burger', 'opinion': 'not cooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'salmon', 'opinion': 'raw', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: easily the worst stir - fried squid i ' ve ever tasted .\n->easily the worst stir - fried squid i ' ve ever tasted .\n[{'aspect': 'stir - fried squid', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->", + "output": "{\"text\": \"the brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\", \"labels\": \"[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was authentic .\n->The food was authentic .\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n->Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n[{'aspect': 'wine selection', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Gigondas', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'worth', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i ' m saving up for my next visit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m saving up for my next visit .\n->", + "output": "{\"text\": \"i ' m saving up for my next visit .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromebook is not a writer ' s friend .\n->chromebook is not a writer ' s friend .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s a small cute restaurant .\n->it ' s a small cute restaurant .\n[{'aspect': 'restaurant', 'opinion': 'small', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'cute', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the place was quiet and delightful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place was quiet and delightful .\n->", + "output": "{\"text\": \"the place was quiet and delightful .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has everything he wanted and needs .\n->it has everything he wanted and needs .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: screen broke 2 weeks after having it .\n->screen broke 2 weeks after having it .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: i did not try the caviar but i tried their salmon and crab salad ( they are all good )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did not try the caviar but i tried their salmon and crab salad ( they are all good )\n->", + "output": "{\"text\": \"i did not try the caviar but i tried their salmon and crab salad ( they are all good )\", \"labels\": \"[{'aspect': 'salmon', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crab salad', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but this asus c302ca has blown me away .\n->but this asus c302ca has blown me away .\n[{'aspect': 'asus c302ca', 'opinion': 'blown me away', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food tasted very good .\n->the food tasted very good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: as a retired hipster , i can say with some degree of certainty that for the last year lucky strike has been the best laid - back late night in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas a retired hipster , i can say with some degree of certainty that for the last year lucky strike has been the best laid - back late night in the city .\n->", + "output": "{\"text\": \"as a retired hipster , i can say with some degree of certainty that for the last year lucky strike has been the best laid - back late night in the city .\", \"labels\": \"[{'aspect': 'lucky strike', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n->i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n[{'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: Priced at upper intermediate range .\n->Priced at upper intermediate range .\n[{'aspect': 'Priced', 'opinion': 'upper intermediate', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she ' s - way - cuter - than - me - that - b @ # $ * way ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she ' s - way - cuter - than - me - that - b @ # $ * way ) .\n->", + "output": "{\"text\": \"the wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she ' s - way - cuter - than - me - that - b @ # $ * way ) .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait staff', 'opinion': 'fun', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait staff', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza is terrific , as is homemade pasta .\n->Pizza is terrific , as is homemade pasta .\n[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: And the prices were way to high for what you get .\n->And the prices were way to high for what you get .\n[{'aspect': 'prices', 'opinion': 'high', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->", + "output": "{\"text\": \"the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fries', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the first laptop i ' ve had that i enjoy so much that i use it when i ' m not working too .\n->this is the first laptop i ' ve had that i enjoy so much that i use it when i ' m not working too .\n[{'aspect': 'laptop', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: but the best part about ls is the late night atmosphere , delightfully free of the bts .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the best part about ls is the late night atmosphere , delightfully free of the bts .\n->", + "output": "{\"text\": \"but the best part about ls is the late night atmosphere , delightfully free of the bts .\", \"labels\": \"[{'aspect': 'late night atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got the $ 10 10 - piece dim sum combo , every bite of which was great .\n->i got the $ 10 10 - piece dim sum combo , every bite of which was great .\n[{'aspect': '$ 10 10 - piece dim sum combo', 'opinion': 'i', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: im returning it .\n->im returning it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: you can get a completely delish martini in a glass ( that ' s about 2 1 / 2 drinks ) for $ 8 . 50 ( i recommend the vanilla shanty , mmmm ! ) in a great homey setting with great music .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can get a completely delish martini in a glass ( that ' s about 2 1 / 2 drinks ) for $ 8 . 50 ( i recommend the vanilla shanty , mmmm ! ) in a great homey setting with great music .\n->", + "output": "{\"text\": \"you can get a completely delish martini in a glass ( that ' s about 2 1 / 2 drinks ) for $ 8 . 50 ( i recommend the vanilla shanty , mmmm ! ) in a great homey setting with great music .\", \"labels\": \"[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}, {'aspect': 'vanilla shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my friends , both female and male , are going to by this set up for their travel needs but for me it is lacking .\n->my friends , both female and male , are going to by this set up for their travel needs but for me it is lacking .\n[{'aspect': 'set up', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: overall for the price range , it ' s a fantastic laptop .\n->overall for the price range , it ' s a fantastic laptop .\n[{'aspect': 'laptop', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the in - house lady dj on saturday nights has outrageously good taste in music , and moreover , takes requests .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe in - house lady dj on saturday nights has outrageously good taste in music , and moreover , takes requests .\n->", + "output": "{\"text\": \"the in - house lady dj on saturday nights has outrageously good taste in music , and moreover , takes requests .\", \"labels\": \"[{'aspect': 'in - house lady dj', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve only utilized the 360 degree opening once and so far i like it .\n->i ' ve only utilized the 360 degree opening once and so far i like it .\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: cons - no light to indicate caps lock .\n->cons - no light to indicate caps lock .\n[{'aspect': 'caps lock', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: you ca n ' t go wrong with this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou ca n ' t go wrong with this place .\n->", + "output": "{\"text\": \"you ca n ' t go wrong with this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': \"ca n ' t go wrong\", 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hot / dead pixels on screen after 4 months use .\n->hot / dead pixels on screen after 4 months use .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n->The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n[{'aspect': 'fillings', 'opinion': 'unconventional', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dosa batter', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: suan is a great place that i often take my friends ( classmates ) too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuan is a great place that i often take my friends ( classmates ) too .\n->", + "output": "{\"text\": \"suan is a great place that i often take my friends ( classmates ) too .\", \"labels\": \"[{'aspect': 'suan', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n->mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n[{'aspect': 'raddichio', 'opinion': 'bitter', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it takes up more than 1 / 3rd of the palm - area horizontal space and makes thumb - tapping mouse tweaks very easy .\n->it takes up more than 1 / 3rd of the palm - area horizontal space and makes thumb - tapping mouse tweaks very easy .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: its location is good and the fact that hutner college is near and their prices are very reasonable , makes students go back to suan again and again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits location is good and the fact that hutner college is near and their prices are very reasonable , makes students go back to suan again and again .\n->", + "output": "{\"text\": \"its location is good and the fact that hutner college is near and their prices are very reasonable , makes students go back to suan again and again .\", \"labels\": \"[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Terrific menu full of unique rolls and special dishes .\n->Terrific menu full of unique rolls and special dishes .\n[{'aspect': 'menu', 'opinion': 'Terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: blackboard works fine for me .\n->blackboard works fine for me .\n[{'aspect': 'blackboard', 'opinion': 'fine', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i love their thai\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love their thai\n->", + "output": "{\"text\": \"i love their thai\", \"labels\": \"[{'aspect': 'thai', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: finally , there are the ports .\n->finally , there are the ports .\n[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n->sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n[{'aspect': 'keyboard', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'android app support', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: noodles with shrimp and chicken and coconut juice is the must !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnoodles with shrimp and chicken and coconut juice is the must !\n->", + "output": "{\"text\": \"noodles with shrimp and chicken and coconut juice is the must !\", \"labels\": \"[{'aspect': 'noodles with shrimp and chicken and coconut juice', 'opinion': 'must', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ll end the review simply saying i ' m very happy overall with my purchase !\n->i ' ll end the review simply saying i ' m very happy overall with my purchase !\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: my fan can get it as low as 82 - 83 degrees , consistently , while idle or gaming .\n->my fan can get it as low as 82 - 83 degrees , consistently , while idle or gaming .\n[{'aspect': 'fan', 'opinion': 'low', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: i will go back to suan soon !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will go back to suan soon !\n->", + "output": "{\"text\": \"i will go back to suan soon !\", \"labels\": \"[{'aspect': 'suan', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the location , the prices are very reasonable .\n->For the location , the prices are very reasonable .\n[{'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'location', 'opinion': 'reasonable', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: a coworker and i tried pacifico after work a few fridays and loved it .\n->a coworker and i tried pacifico after work a few fridays and loved it .\n[{'aspect': 'pacifico', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: in summer - eat outside on a terrace ( another great feature of suan ) ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin summer - eat outside on a terrace ( another great feature of suan ) ! ! !\n->", + "output": "{\"text\": \"in summer - eat outside on a terrace ( another great feature of suan ) ! ! !\", \"labels\": \"[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n->After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n[{'aspect': 'chicken dish', 'opinion': 'complaining', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n->to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n[{'aspect': 'power icon', 'opinion': 'dismay', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'power icon', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i can not imagine a friendlier staff working in a restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can not imagine a friendlier staff working in a restaurant .\n->", + "output": "{\"text\": \"i can not imagine a friendlier staff working in a restaurant .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sushi was awful !\n->The sushi was awful !\n[{'aspect': 'sushi', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: in about 12 minutes , the thing is gone .\n->in about 12 minutes , the thing is gone .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i can not imagine better indian food in all of the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can not imagine better indian food in all of the city .\n->", + "output": "{\"text\": \"i can not imagine better indian food in all of the city .\", \"labels\": \"[{'aspect': 'indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: whether i ' m playing games or video editing , or web design , it doesn ' t hesitate .\n->whether i ' m playing games or video editing , or web design , it doesn ' t hesitate .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: the food is very average . . . the thai fusion stuff is a bit too sweet , every thing they serve is too sweet here .\n->the food is very average . . . the thai fusion stuff is a bit too sweet , every thing they serve is too sweet here .\n[{'aspect': 'food', 'opinion': 'average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai fusion stuff', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i can not imagine you not rushing out to eat there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can not imagine you not rushing out to eat there .\n->", + "output": "{\"text\": \"i can not imagine you not rushing out to eat there .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\n->I also recommend the rice dishes or the different varieties of congee ( rice porridge ) .\n[{'aspect': 'rice dishes', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'congee ( rice porridge )', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: samsung ' s warranty process was not working properly for this device .\n->samsung ' s warranty process was not working properly for this device .\n[{'aspect': \"samsung ' s warranty\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\ntext: during the course of the past 3 months , the chef and staff changed and it was not for the better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nduring the course of the past 3 months , the chef and staff changed and it was not for the better .\n->", + "output": "{\"text\": \"during the course of the past 3 months , the chef and staff changed and it was not for the better .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the bread at the beginning is super tasty and makes you want more - the pizza is delicious and comes in personal sizes , however be warned that the Peter 's Favourite pizza with prosciutto and baby arugula is actually a margarite pizza with cold prosciutto and baby arugula on top , like a salad .\n->- the bread at the beginning is super tasty and makes you want more - the pizza is delicious and comes in personal sizes , however be warned that the Peter 's Favourite pizza with prosciutto and baby arugula is actually a margarite pizza with cold prosciutto and baby arugula on top , like a salad .\n[{'aspect': 'bread', 'opinion': 'super tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n->myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n[{'aspect': 'myagi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i wish they would change back to what it was before .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wish they would change back to what it was before .\n->", + "output": "{\"text\": \"i wish they would change back to what it was before .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 2 ) the touchpad is way too wonky - asus needs to fix this asap .\n->2 ) the touchpad is way too wonky - asus needs to fix this asap .\n[{'aspect': 'touchpad', 'opinion': 'wonky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: overall , it is not horrible , but i wouldn ' t purchase this model again .\n->overall , it is not horrible , but i wouldn ' t purchase this model again .\n[{'aspect': 'model', 'opinion': 'not horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the food now is inconsistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food now is inconsistent .\n->", + "output": "{\"text\": \"the food now is inconsistent .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n->I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n[{'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n->From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caviar', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is the kind of place you ' d like to take all your friends to and still keep a secret .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the kind of place you ' d like to take all your friends to and still keep a secret .\n->", + "output": "{\"text\": \"this is the kind of place you ' d like to take all your friends to and still keep a secret .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n->they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: The dim sum here is only so-so .\n->The dim sum here is only so-so .\n[{'aspect': 'dim sum', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the setting is casual and romantic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe setting is casual and romantic .\n->", + "output": "{\"text\": \"the setting is casual and romantic .\", \"labels\": \"[{'aspect': 'setting', 'opinion': 'casual', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'setting', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n->i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n[{'aspect': 'asus support', 'opinion': 'sloth', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: The ambience was nice , but service was n't so great .\n->The ambience was nice , but service was n't so great .\n[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': \"was n't so great\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: prices are very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprices are very good .\n->", + "output": "{\"text\": \"prices are very good .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use it for streaming with the elgato device and it doesn ' t miss a beat .\n->i use it for streaming with the elgato device and it doesn ' t miss a beat .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i highly recommend this beautiful place .\n->i highly recommend this beautiful place .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the food is excellent !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is excellent !\n->", + "output": "{\"text\": \"the food is excellent !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything lagged and the screen flickered .\n->everything lagged and the screen flickered .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Service was very good - prompt , attentive and non-intrusive .\n->Service was very good - prompt , attentive and non-intrusive .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you ' re daring , try the balsamic vinegar over icecream , it ' s wonderful !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re daring , try the balsamic vinegar over icecream , it ' s wonderful !\n->", + "output": "{\"text\": \"if you ' re daring , try the balsamic vinegar over icecream , it ' s wonderful !\", \"labels\": \"[{'aspect': 'balsamic vinegar over icecream', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'balsamic vinegar over icecream', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pickles were great addition .\n->The pickles were great addition .\n[{'aspect': 'pickles', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: too bad the food was n ' t of the same heritage .\n->too bad the food was n ' t of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n->", + "output": "{\"text\": \"they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n->the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n[{'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\nExample:\ntext: the stock video player misses audio on a lot of movies , and using the vlc app ( android ) is super buggy ( frequently freezes and shuts down ) .\n->the stock video player misses audio on a lot of movies , and using the vlc app ( android ) is super buggy ( frequently freezes and shuts down ) .\n[{'aspect': 'stock video player', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'vlc app', 'opinion': 'buggy', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: the rest of the dim sum , though pricey by chinatown standards , is worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe rest of the dim sum , though pricey by chinatown standards , is worth it .\n->", + "output": "{\"text\": \"the rest of the dim sum , though pricey by chinatown standards , is worth it .\", \"labels\": \"[{'aspect': 'dim sum', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'dim sum', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n->received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: service was quick .\n->service was quick .\n[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n->", + "output": "{\"text\": \"the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n->Raga stands out with an interesting fusion of French and Indian cooking .\n[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: If your favorite Chinese food is General Tao chicken , then this is NOT your place .\n->If your favorite Chinese food is General Tao chicken , then this is NOT your place .\n[{'aspect': 'General Tao chicken', 'opinion': 'favorite', 'polarity': 'negative', 'category': 'NULL'}]\ntext: a few tips : skip the turnip cake , roast pork buns and egg custards .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na few tips : skip the turnip cake , roast pork buns and egg custards .\n->", + "output": "{\"text\": \"a few tips : skip the turnip cake , roast pork buns and egg custards .\", \"labels\": \"[{'aspect': 'turnip cake', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'roast pork buns', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'egg custards', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: paradoxically , this laptop is a ` ` pro ` ` .\n->paradoxically , this laptop is a ` ` pro ` ` .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: these are overpriced and you can get better just around the corner :\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthese are overpriced and you can get better just around the corner :\n->", + "output": "{\"text\": \"these are overpriced and you can get better just around the corner :\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The Thali was small , thoroughly unremarkable , and $ 14.95 .\n->The Thali was small , thoroughly unremarkable , and $ 14.95 .\n[{'aspect': 'Thali', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Thali', 'opinion': 'unremarkable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i was pleasantly suprised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was pleasantly suprised .\n->", + "output": "{\"text\": \"i was pleasantly suprised .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'suprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The seats are uncomfortable if you are sitting against the wall on wooden benches .\n->The seats are uncomfortable if you are sitting against the wall on wooden benches .\n[{'aspect': 'seats', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n->and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n[{'aspect': 'system', 'opinion': 'not worry', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: the food was exceptional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was exceptional .\n->", + "output": "{\"text\": \"the food was exceptional .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really like my chromebook .\n->i really like my chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is my first chromebook , and i ' m absolutely loving it .\n->this is my first chromebook , and i ' m absolutely loving it .\n[{'aspect': 'this', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n->", + "output": "{\"text\": \"i choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\", \"labels\": \"[{'aspect': 'braised lamb shank in red wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n->My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n[{'aspect': 'portion', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french fries', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: As much as I like the food there , I ca n't bring myself to go back .\n->As much as I like the food there , I ca n't bring myself to go back .\n[{'aspect': 'food', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the service was friendly and the atmosphere was casual .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was friendly and the atmosphere was casual .\n->", + "output": "{\"text\": \"the service was friendly and the atmosphere was casual .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was very good - prompt , attentive and non - intrusive .\n->service was very good - prompt , attentive and non - intrusive .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'non - intrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it feels amazing and the travel is perfect .\n->it feels amazing and the travel is perfect .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'travel', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#PORTABILITY'}]\ntext: the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n->", + "output": "{\"text\": \"the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'meal', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'restaurant', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in fact , it appears he is going to go postal at any moment .\n->in fact , it appears he is going to go postal at any moment .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: also , i personally was n ' t a fan of the portobello and asparagus mole .\n->also , i personally was n ' t a fan of the portobello and asparagus mole .\n[{'aspect': 'portobello and asparagus mole', 'opinion': 'fan', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: this little place has a cute interior decor and affordable city prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little place has a cute interior decor and affordable city prices .\n->", + "output": "{\"text\": \"this little place has a cute interior decor and affordable city prices .\", \"labels\": \"[{'aspect': 'interior decor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'little', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n->the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The staff there is very attentive and down to earth .\n->The staff there is very attentive and down to earth .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the pad se ew chicken was delicious , however the pad thai was far too oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pad se ew chicken was delicious , however the pad thai was far too oily .\n->", + "output": "{\"text\": \"the pad se ew chicken was delicious , however the pad thai was far too oily .\", \"labels\": \"[{'aspect': 'pad se ew chicken', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop exceeds my expectations for a mid - price laptop .\n->this laptop exceeds my expectations for a mid - price laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I absolutely love this place ! ! !\n->I absolutely love this place ! ! !\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: have eaten at ginger house several times , and it ' s always good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave eaten at ginger house several times , and it ' s always good .\n->", + "output": "{\"text\": \"have eaten at ginger house several times , and it ' s always good .\", \"labels\": \"[{'aspect': 'ginger house', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: On the other hand , if you are not fooled easily , you will find hundreds of restaurants that will give you service and ambiance that is on par with Alain Ducasse , and food that will outshine in presentaion , taste , choice , quality and quantity .\n->On the other hand , if you are not fooled easily , you will find hundreds of restaurants that will give you service and ambiance that is on par with Alain Ducasse , and food that will outshine in presentaion , taste , choice , quality and quantity .\n[{'aspect': 'service', 'opinion': 'on par', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'on par', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'outshine', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: waitstaff are very friendly .\n->waitstaff are very friendly .\n[{'aspect': 'waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the fried dumplings are great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fried dumplings are great !\n->", + "output": "{\"text\": \"the fried dumplings are great !\", \"labels\": \"[{'aspect': 'fried dumplings', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: laptop screen goes blank after four weeks minimally used .\n->laptop screen goes blank after four weeks minimally used .\n[{'aspect': 'laptop screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The puke green walls leave a lot to be desired , but the food is very good .\n->The puke green walls leave a lot to be desired , but the food is very good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'walls', 'opinion': 'desired', 'polarity': 'negative', 'category': 'NULL'}]\ntext: finally a reliable chinese restaurant !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfinally a reliable chinese restaurant !\n->", + "output": "{\"text\": \"finally a reliable chinese restaurant !\", \"labels\": \"[{'aspect': 'chinese restaurant', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now i ca n ' t even get into my laptop because the startup is all jacked up .\n->now i ca n ' t even get into my laptop because the startup is all jacked up .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n->the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n[{'aspect': 'battery life', 'opinion': 'solid', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'worth', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'quality', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'worth', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\ntext: this place would be so much better served by being run by a group that actually understands customer service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place would be so much better served by being run by a group that actually understands customer service .\n->", + "output": "{\"text\": \"this place would be so much better served by being run by a group that actually understands customer service .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was very good and warm .\n->Service was very good and warm .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i don ' t even think i will be able to work on this it is so distracting .\n->i don ' t even think i will be able to work on this it is so distracting .\n[{'aspect': 'NULL', 'opinion': 'distracting', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the only thing you can do here is walk in and eat . . but planning an event , especially a small , intimate one , forget about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing you can do here is walk in and eat . . but planning an event , especially a small , intimate one , forget about it .\n->", + "output": "{\"text\": \"the only thing you can do here is walk in and eat . . but planning an event , especially a small , intimate one , forget about it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she replied ` ` well it would be more convenient for us if you ordered now , since you are a larger party , and it might get crowded . ' '\n->she replied ` ` well it would be more convenient for us if you ordered now , since you are a larger party , and it might get crowded . ' '\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: good food\n->good food\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: they are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n->", + "output": "{\"text\": \"they are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not helpful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food here is rather good , but only if you like to wait for it .\n->The food here is rather good , but only if you like to wait for it .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We were also seated promptly at the time of our reservation and the service was very quick and professional .\n->We were also seated promptly at the time of our reservation and the service was very quick and professional .\n[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: nobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .\n->", + "output": "{\"text\": \"nobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: With the great variety on the menu , I eat here often and never get bored .\n->With the great variety on the menu , I eat here often and never get bored .\n[{'aspect': 'menu', 'opinion': 'great variety', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: no matter where on earth , you can get your apple product repaired .\n->no matter where on earth , you can get your apple product repaired .\n[{'aspect': 'apple product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: terrible , terrible management - deserves to be shut - down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nterrible , terrible management - deserves to be shut - down .\n->", + "output": "{\"text\": \"terrible , terrible management - deserves to be shut - down .\", \"labels\": \"[{'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is solid , and does a decent job rendering pages , and paired with the extra ram here , it adequately handles streaming content in the background - such as youtube or spotify - and still maintains a decent browsing experience across eight to ten additional tabs ;\n->it is solid , and does a decent job rendering pages , and paired with the extra ram here , it adequately handles streaming content in the background - such as youtube or spotify - and still maintains a decent browsing experience across eight to ten additional tabs ;\n[{'aspect': 'NULL', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the salads are delicious , both refreshing and very spicy .\n->the salads are delicious , both refreshing and very spicy .\n[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: never fails to please .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnever fails to please .\n->", + "output": "{\"text\": \"never fails to please .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'please', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n->overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n[{'aspect': 'asus c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n->not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: delicious bagels , especially when right out of the oven .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicious bagels , especially when right out of the oven .\n->", + "output": "{\"text\": \"delicious bagels , especially when right out of the oven .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service here was great , food was fantastic .\n->Service here was great , food was fantastic .\n[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard and track pad are both quite good , although i always use a real mouse .\n->the keyboard and track pad are both quite good , although i always use a real mouse .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'track pad', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: spreads and toppings are great - though a bit pricey .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspreads and toppings are great - though a bit pricey .\n->", + "output": "{\"text\": \"spreads and toppings are great - though a bit pricey .\", \"labels\": \"[{'aspect': 'spreads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spreads', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not worth the prices .\n->Not worth the prices .\n[{'aspect': 'prices', 'opinion': 'worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i thought this place was totally overrated .\n->i thought this place was totally overrated .\n[{'aspect': 'place', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: service is fast and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is fast and friendly .\n->", + "output": "{\"text\": \"service is fast and friendly .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The location is perfect .\n->The location is perfect .\n[{'aspect': 'location', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: speakers sound tinny .\n->speakers sound tinny .\n[{'aspect': 'speakers', 'opinion': 'tinny', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n->", + "output": "{\"text\": \"only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Aside from the Sea Urchin , the chef recommended an assortment of fish including Fatty Yellow Tail , Boton Shrimp , Blue Fin Torro ( Fatty Tuna ) , Sea Eel , etc .\n->Aside from the Sea Urchin , the chef recommended an assortment of fish including Fatty Yellow Tail , Boton Shrimp , Blue Fin Torro ( Fatty Tuna ) , Sea Eel , etc .\n[{'aspect': 'assortment of fish', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Fatty Yellow Tail', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Boton Shrimp', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Sea Eel', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Sea Urchin', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Blue Fin Torro ( Fatty Tuna )', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n->it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\ntext: but that is highly forgivable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut that is highly forgivable .\n->", + "output": "{\"text\": \"but that is highly forgivable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'forgivable', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: im actually wondering if there is an issue with the speakers , it ' s so bad .\n->im actually wondering if there is an issue with the speakers , it ' s so bad .\n[{'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: the computer is nice , blah blah it has nice features but it stops working after a few months .\n->the computer is nice , blah blah it has nice features but it stops working after a few months .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: been there , done that , and new york , it ' s not that big a deal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeen there , done that , and new york , it ' s not that big a deal .\n->", + "output": "{\"text\": \"been there , done that , and new york , it ' s not that big a deal .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m typing this review in a dark room , and it reminds me how much i love this backlit keyboard .\n->i ' m typing this review in a dark room , and it reminds me how much i love this backlit keyboard .\n[{'aspect': 'backlit keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: the battery life is really pretty comparable ( slightly more with the mbp , and i can obtain info on app energy usage a bit easier imo ) .\n->the battery life is really pretty comparable ( slightly more with the mbp , and i can obtain info on app energy usage a bit easier imo ) .\n[{'aspect': 'battery life', 'opinion': 'comparable', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: the food is decent at best , and the ambience , well , it ' s a matter of opinion , some may consider it to be a sweet thing , i thought it was just annoying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is decent at best , and the ambience , well , it ' s a matter of opinion , some may consider it to be a sweet thing , i thought it was just annoying .\n->", + "output": "{\"text\": \"the food is decent at best , and the ambience , well , it ' s a matter of opinion , some may consider it to be a sweet thing , i thought it was just annoying .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'ambience', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not only is the touchpad not great in use but it also feels poorly made .\n->not only is the touchpad not great in use but it also feels poorly made .\n[{'aspect': 'touchpad', 'opinion': 'not great', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->i was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n->", + "output": "{\"text\": \"if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Tiny dessert was $ 8.00 ... just plain overpriced for what it is .\n->Tiny dessert was $ 8.00 ... just plain overpriced for what it is .\n[{'aspect': 'dessert', 'opinion': 'Tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: excellent sashimi , and the millennium roll is beyond delicious .\n->excellent sashimi , and the millennium roll is beyond delicious .\n[{'aspect': 'sashimi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'millennium roll', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: rao is a good restaurant , but it ' s nothing special .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nrao is a good restaurant , but it ' s nothing special .\n->", + "output": "{\"text\": \"rao is a good restaurant , but it ' s nothing special .\", \"labels\": \"[{'aspect': 'rao', 'opinion': 'good', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'rao', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Always great service !\n->Always great service !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the bagel was huge .\n->the bagel was huge .\n[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: but after last night , spice grill is the only place i ' m eating indian cuisine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut after last night , spice grill is the only place i ' m eating indian cuisine .\n->", + "output": "{\"text\": \"but after last night , spice grill is the only place i ' m eating indian cuisine .\", \"labels\": \"[{'aspect': 'indian cuisine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: transferring files from a non - iphone phone , like android is extremely annoying .\n->transferring files from a non - iphone phone , like android is extremely annoying .\n[{'aspect': 'android', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'SOFTWARE#PORTABILITY'}]\nExample:\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n->Raga stands out with an interesting fusion of French and Indian cooking .\n[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you must try the shrimp appetizers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou must try the shrimp appetizers .\n->", + "output": "{\"text\": \"you must try the shrimp appetizers .\", \"labels\": \"[{'aspect': 'shrimp appetizers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n->the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n[{'aspect': 'ram', 'opinion': 'expandable', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: my chow fun and chow see was really bland and oily .\n->my chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: even my indian friend could n ' t believe how good and tasty everything was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven my indian friend could n ' t believe how good and tasty everything was .\n->", + "output": "{\"text\": \"even my indian friend could n ' t believe how good and tasty everything was .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is good , screen looks fine , and all of the keys and ports are functional .\n->battery life is good , screen looks fine , and all of the keys and ports are functional .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keys', 'opinion': 'functional', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'ports', 'opinion': 'functional', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great pizza and fantastic service .\n->Great pizza and fantastic service .\n[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n->", + "output": "{\"text\": \"this place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\", \"labels\": \"[{'aspect': 'ambience', 'opinion': 'correct', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'staff', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cute place , nice wait staff but would never go there again .\n->cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: We all had the tasting menu and unlike some of the other reviews , I felt there was more than enough food .\n->We all had the tasting menu and unlike some of the other reviews , I felt there was more than enough food .\n[{'aspect': 'food', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great food , great prices , great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food , great prices , great service .\n->", + "output": "{\"text\": \"great food , great prices , great service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as soon as i learned about c302 , i decided top bought it .\n->as soon as i learned about c302 , i decided top bought it .\n[{'aspect': 'c302', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we were charged full price .\n->we were charged full price .\n[{'aspect': 'NULL', 'opinion': 'full', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: if you are looking for a good quality , cheap eats - this is the place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are looking for a good quality , cheap eats - this is the place .\n->", + "output": "{\"text\": \"if you are looking for a good quality , cheap eats - this is the place .\", \"labels\": \"[{'aspect': 'eats', 'opinion': 'good quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n->i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n[{'aspect': 'usb - c charger', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: with ssd lightning fast start up\n->with ssd lightning fast start up\n[{'aspect': 'ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: it ' s a perfect place to have a amazing indian food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a perfect place to have a amazing indian food .\n->", + "output": "{\"text\": \"it ' s a perfect place to have a amazing indian food .\", \"labels\": \"[{'aspect': 'indian food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had the best ravioli ever .\n->I had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n->i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n[{'aspect': 'lenovo ideapad 320', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i really loved the different and inovated touch that ' s the cheff gives to the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really loved the different and inovated touch that ' s the cheff gives to the food .\n->", + "output": "{\"text\": \"i really loved the different and inovated touch that ' s the cheff gives to the food .\", \"labels\": \"[{'aspect': 'cheff', 'opinion': 'loved', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheff', 'opinion': 'inovated', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Halibut was too salty , dessert was so so ( do n't waste any of your calories ) and service was poor .\n->The Halibut was too salty , dessert was so so ( do n't waste any of your calories ) and service was poor .\n[{'aspect': 'Halibut', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'so so', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The waitresses are nice -- also you can just get counter service sit .\n->The waitresses are nice -- also you can just get counter service sit .\n[{'aspect': 'waitresses', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n->", + "output": "{\"text\": \"also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'place', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'the', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'just', 'opinion': \"' re\", 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n->The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n[{'aspect': 'eggplant parmesan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'baked ziti with meatsauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the computer literally blue screened on the second day because system 32 was corrupt .\n->the computer literally blue screened on the second day because system 32 was corrupt .\n[{'aspect': 'system 32', 'opinion': 'corrupt', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: their bagels are fine , but they are a little overcooked , and not really a ' special ' bagel experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntheir bagels are fine , but they are a little overcooked , and not really a ' special ' bagel experience .\n->", + "output": "{\"text\": \"their bagels are fine , but they are a little overcooked , and not really a ' special ' bagel experience .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'fine', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good food .\n->Good food .\n[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the bibimbap was average , but the stone bowl was n ' t even close to sizzling .\n->the bibimbap was average , but the stone bowl was n ' t even close to sizzling .\n[{'aspect': 'bibimbap', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'stone bowl', 'opinion': \"n ' t even close to sizzling\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: great bagels made the old - fashioned way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat bagels made the old - fashioned way .\n->", + "output": "{\"text\": \"great bagels made the old - fashioned way .\", \"labels\": \"[{'aspect': 'bagels', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: An excellent alternative to fast food joints and ordering in but , the food was slightly disappointing .\n->An excellent alternative to fast food joints and ordering in but , the food was slightly disappointing .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: La Rosa waltzes in , and I think they are doing it the best .\n->La Rosa waltzes in , and I think they are doing it the best .\n[{'aspect': 'La Rosa', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: drawbacks : service is slow and they do n ' t toast !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndrawbacks : service is slow and they do n ' t toast !\n->", + "output": "{\"text\": \"drawbacks : service is slow and they do n ' t toast !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got this laptop 2 days ago and it says plugged in , not charged .\n->i got this laptop 2 days ago and it says plugged in , not charged .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: polite acknowledgement is out ;\n->polite acknowledgement is out ;\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the food was absolutely amazing ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was absolutely amazing ! !\n->", + "output": "{\"text\": \"the food was absolutely amazing ! !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n->the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: outstanding laptop .\n->outstanding laptop .\n[{'aspect': 'laptop', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n->", + "output": "{\"text\": \"the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\", \"labels\": \"[{'aspect': 'baked clams octopus', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n->despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n[{'aspect': 'modern japanese food', 'opinion': 'go - to for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mirrors', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: what you are paying for is the environment and the name .\n->what you are paying for is the environment and the name .\n[{'aspect': 'environment', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: the lamb was tender so full of flavor , the dessert was divine ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe lamb was tender so full of flavor , the dessert was divine ! !\n->", + "output": "{\"text\": \"the lamb was tender so full of flavor , the dessert was divine ! !\", \"labels\": \"[{'aspect': 'lamb', 'opinion': 'tender', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb', 'opinion': 'full of flavor', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessert', 'opinion': 'divine', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n->as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n[{'aspect': 'chromebook device', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n->i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the waiter was attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waiter was attentive .\n->", + "output": "{\"text\": \"the waiter was attentive .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have to say , this is a very nice product .\n->i have to say , this is a very nice product .\n[{'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'never had a problem', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the place itself is beautiful the bar scene seems to be happening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place itself is beautiful the bar scene seems to be happening .\n->", + "output": "{\"text\": \"the place itself is beautiful the bar scene seems to be happening .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'bar scene', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i love the chromebook overall\n->- i love the chromebook overall\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The sake menu should not be overlooked !\n->The sake menu should not be overlooked !\n[{'aspect': 'sake menu', 'opinion': 'overlooked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndowntown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n->", + "output": "{\"text\": \"downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\", \"labels\": \"[{'aspect': 'appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n->Raga stands out with an interesting fusion of French and Indian cooking .\n[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i don ' t like this .\n->i don ' t like this .\n[{'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: dessert is a joke . . . dont bother\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndessert is a joke . . . dont bother\n->", + "output": "{\"text\": \"dessert is a joke . . . dont bother\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our phones are usb c so one cable does everything for me .\n->our phones are usb c so one cable does everything for me .\n[{'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n->The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n[{'aspect': 'three course meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: volare virgins or weekly regulars , everyone gets treated the same and you ca n ' t ask for more than that when the service is this friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvolare virgins or weekly regulars , everyone gets treated the same and you ca n ' t ask for more than that when the service is this friendly .\n->", + "output": "{\"text\": \"volare virgins or weekly regulars , everyone gets treated the same and you ca n ' t ask for more than that when the service is this friendly .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n->i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\nExample:\ntext: i highly recommend this laptop for anyone looking for a great performing machine with an outstanding price ( just to be clear , it won ' t be running the newest high - end games on ultra - high graphics settings , but it still performs phenomenally for its price range and usage category ) .\n->i highly recommend this laptop for anyone looking for a great performing machine with an outstanding price ( just to be clear , it won ' t be running the newest high - end games on ultra - high graphics settings , but it still performs phenomenally for its price range and usage category ) .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'phenomenally', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n->", + "output": "{\"text\": \"the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'family feel', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'portions', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'veal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: easy to use .\n->easy to use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Even though I made the reservation at 3pm for the same night through Dinnerbroker , we were seated at a table with one of the best view !\n->Even though I made the reservation at 3pm for the same night through Dinnerbroker , we were seated at a table with one of the best view !\n[{'aspect': 'table', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the anti - pasta was excellent , especially the calamari , as were the filling pasta mains .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe anti - pasta was excellent , especially the calamari , as were the filling pasta mains .\n->", + "output": "{\"text\": \"the anti - pasta was excellent , especially the calamari , as were the filling pasta mains .\", \"labels\": \"[{'aspect': 'anti - pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n->first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n[{'aspect': 'place', 'opinion': '* not * romantic', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: everything was good for a few days after receiving the product .\n->everything was good for a few days after receiving the product .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n->", + "output": "{\"text\": \"the wine list is extensive and can easily hike up an otherwise reasonably priced meal .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'meal', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'two types of sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Much more reasonably priced too !\n->Much more reasonably priced too !\n[{'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\ntext: still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstill , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n->", + "output": "{\"text\": \"still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\", \"labels\": \"[{'aspect': 'measures of liquers', 'opinion': 'pour - your - own', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'measures of liquers', 'opinion': 'courtesey', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: update : i repaired it myself for $ 12 .\n->update : i repaired it myself for $ 12 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food was well prepared and the service impecable .\n->The food was well prepared and the service impecable .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: fantastic place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfantastic place .\n->", + "output": "{\"text\": \"fantastic place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n->What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n[{'aspect': 'sichuan cooking', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chongqing hotpot', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n->i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: a pleasant surprise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na pleasant surprise .\n->", + "output": "{\"text\": \"a pleasant surprise .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: much of the time it seems like they do not care about you .\n->much of the time it seems like they do not care about you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n->The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n[{'aspect': 'spot lights', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: go there to relax and feel like your somewhere else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngo there to relax and feel like your somewhere else .\n->", + "output": "{\"text\": \"go there to relax and feel like your somewhere else .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'relax', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had this for 3 days and so far the laptop is fine .\n->i ' ve had this for 3 days and so far the laptop is fine .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - performance can be stuttering when under heavy load .\n->- performance can be stuttering when under heavy load .\n[{'aspect': 'performance', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: lucky strike is a great casual place to just grab a bite to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlucky strike is a great casual place to just grab a bite to eat .\n->", + "output": "{\"text\": \"lucky strike is a great casual place to just grab a bite to eat .\", \"labels\": \"[{'aspect': 'lucky strike', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'lucky strike', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: people are rude bit again it 's new york !\n->people are rude bit again it 's new york !\n[{'aspect': 'people', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n->Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n[{'aspect': 'waiters', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'busy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great food , great decor , great service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food , great decor , great service .\n->", + "output": "{\"text\": \"great food , great decor , great service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i found the food , service and value exceptional everytime i have been there .\n->i found the food , service and value exceptional everytime i have been there .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n->the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n[{'aspect': 'space', 'opinion': 'limited', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'indo - chinese food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i recommend it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recommend it !\n->", + "output": "{\"text\": \"i recommend it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not because you are ` ` the four seasons ` ` . . . \u2013 you are allowed to charge an arm and a leg for a romatic dinner .\n->not because you are ` ` the four seasons ` ` . . . \u2013 you are allowed to charge an arm and a leg for a romatic dinner .\n[{'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: I love the atmorphere @ peep !\n->I love the atmorphere @ peep !\n[{'aspect': 'atmorphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is the perfect spot for meeting friends , having lunch , dinner , pre - theatre or after - theatre drinks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the perfect spot for meeting friends , having lunch , dinner , pre - theatre or after - theatre drinks !\n->", + "output": "{\"text\": \"this is the perfect spot for meeting friends , having lunch , dinner , pre - theatre or after - theatre drinks !\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was good not great not worth the wait or another visit\n->Food was good not great not worth the wait or another visit\n[{'aspect': 'wait', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: at all and it took several minutes to boot up .\n->at all and it took several minutes to boot up .\n[{'aspect': 'boot up', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: ( always ask the bartender for the seasonal beer ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( always ask the bartender for the seasonal beer ! ! !\n->", + "output": "{\"text\": \"( always ask the bartender for the seasonal beer ! ! !\", \"labels\": \"[{'aspect': 'seasonal beer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n->first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'graphics', 'opinion': 'failed', 'polarity': 'negative', 'category': 'GRAPHICS#GENERAL'}]\nExample:\ntext: runs good , poor battery life .\n->runs good , poor battery life .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'poor', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: guaranteed to be a tasty experience ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nguaranteed to be a tasty experience ! )\n->", + "output": "{\"text\": \"guaranteed to be a tasty experience ! )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve lived in ny for 5 years and this place has it all .\n->i ' ve lived in ny for 5 years and this place has it all .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: very disappointed in very bad tech support .\n->very disappointed in very bad tech support .\n[{'aspect': 'tech support', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'tech support', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: i have been doing all of the above at the heartland brewery for over 5 years now and i have never been disappointed !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been doing all of the above at the heartland brewery for over 5 years now and i have never been disappointed !\n->", + "output": "{\"text\": \"i have been doing all of the above at the heartland brewery for over 5 years now and i have never been disappointed !\", \"labels\": \"[{'aspect': 'heartland brewery', 'opinion': 'never been disappointed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Orsay , is without a doubt one of the best values for authentic French food in NYC .\n->Orsay , is without a doubt one of the best values for authentic French food in NYC .\n[{'aspect': 'French food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my boyfriend had the new england chowder it was good but i think the award should go to the lobster bisque .\n->my boyfriend had the new england chowder it was good but i think the award should go to the lobster bisque .\n[{'aspect': 'new england chowder', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster bisque', 'opinion': 'award', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: all the people that i bring there go back on their own and bring their friends !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall the people that i bring there go back on their own and bring their friends !\n->", + "output": "{\"text\": \"all the people that i bring there go back on their own and bring their friends !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first time writing a review for a restaurant because the food and service was excellent .\n->this is my first time writing a review for a restaurant because the food and service was excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n->all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n[{'aspect': 'web browsing', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: go there once and oh yes . . . you will go back . . . you will . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngo there once and oh yes . . . you will go back . . . you will . . .\n->", + "output": "{\"text\": \"go there once and oh yes . . . you will go back . . . you will . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n->If your visiting , you 'll enjoy the ambiance and the fact that it 's in Time Sq ...\n[{'aspect': 'ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the monitor is good , and graphic chip is enough for my office work , internet browsing and video streaming , don ' t think about what game it can play , i won ' t expect intel graphic chip can do a lot , if you want a gaming laptop , find some model with independent graphic chip , if you want a cheap laptop but can play computer game , you should wake up from the dream .\n->the monitor is good , and graphic chip is enough for my office work , internet browsing and video streaming , don ' t think about what game it can play , i won ' t expect intel graphic chip can do a lot , if you want a gaming laptop , find some model with independent graphic chip , if you want a cheap laptop but can play computer game , you should wake up from the dream .\n[{'aspect': 'monitor', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'graphic chip', 'opinion': 'enough', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: a cool bar with great food , and tons of excellent beer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na cool bar with great food , and tons of excellent beer .\n->", + "output": "{\"text\": \"a cool bar with great food , and tons of excellent beer .\", \"labels\": \"[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so kudos to acer for the keyboard !\n->so kudos to acer for the keyboard !\n[{'aspect': 'keyboard', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'acer', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the screen is bright and color spread is good .\n->the screen is bright and color spread is good .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'color spread', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: and even with it ' s pub atmosphere they were great to my kids too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand even with it ' s pub atmosphere they were great to my kids too !\n->", + "output": "{\"text\": \"and even with it ' s pub atmosphere they were great to my kids too !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Excellent dumplings served amid clean , chic decor .\n->Excellent dumplings served amid clean , chic decor .\n[{'aspect': 'dumplings', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'clean', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'chic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I love the fact that the pizza tastes so good and is so cheap .\n->I love the fact that the pizza tastes so good and is so cheap .\n[{'aspect': 'pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\ntext: loved it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nloved it !\n->", + "output": "{\"text\": \"loved it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n->the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The white bean brushetta to start was incredible and the pasta was phenomenal .\n->The white bean brushetta to start was incredible and the pasta was phenomenal .\n[{'aspect': 'white bean brushetta', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the shrimp scampi was excellent and the antipasti were plentiful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe shrimp scampi was excellent and the antipasti were plentiful .\n->", + "output": "{\"text\": \"the shrimp scampi was excellent and the antipasti were plentiful .\", \"labels\": \"[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza was really good .\n->The pizza was really good .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: typing is responsive , the touchescreen is a joy and it ' s fast .\n->typing is responsive , the touchescreen is a joy and it ' s fast .\n[{'aspect': 'typing', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchescreen', 'opinion': 'joy', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it is expensive but well worth the money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is expensive but well worth the money .\n->", + "output": "{\"text\": \"it is expensive but well worth the money .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n->it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n[{'aspect': 'screen', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'bizarre', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the charge cord is very short , about 1 / 2 the size of a regular charging cord\n->the charge cord is very short , about 1 / 2 the size of a regular charging cord\n[{'aspect': 'charge cord', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\ntext: if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\n->", + "output": "{\"text\": \"if you venture off the island of manhattan and ca n ' t seem to find a great italian restaurant , drive to corona .\", \"labels\": \"[{'aspect': 'corona', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in essence , if you want a gaming pc , this one will do the job .\n->in essence , if you want a gaming pc , this one will do the job .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n->the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot sauce', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the only thing more wonderful than the food ( which is exceptional ) is the service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing more wonderful than the food ( which is exceptional ) is the service .\n->", + "output": "{\"text\": \"the only thing more wonderful than the food ( which is exceptional ) is the service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve been there three times and have always had wonderful experiences .\n->i ' ve been there three times and have always had wonderful experiences .\n[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: this establishment is the real deal .\n->this establishment is the real deal .\n[{'aspect': 'establishment', 'opinion': 'real deal', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the only thing the waiters do n ' t do for you is wipe your chin when you leave .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing the waiters do n ' t do for you is wipe your chin when you leave .\n->", + "output": "{\"text\": \"the only thing the waiters do n ' t do for you is wipe your chin when you leave .\", \"labels\": \"[{'aspect': 'waiters', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n->with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n[{'aspect': 'cpu', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'solid', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'good', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: pros : nice size , clear screen , quick on start up , very functional and easy to use .\n->pros : nice size , clear screen , quick on start up , very functional and easy to use .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: wonderful at holiday time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwonderful at holiday time .\n->", + "output": "{\"text\": \"wonderful at holiday time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bison was quite excellent however .\n->bison was quite excellent however .\n[{'aspect': 'bison', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'never had a problem', 'polarity': 'positive', 'category': 'NULL'}]\ntext: cozy romantic atomosphere with only around 15 tables at most .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncozy romantic atomosphere with only around 15 tables at most .\n->", + "output": "{\"text\": \"cozy romantic atomosphere with only around 15 tables at most .\", \"labels\": \"[{'aspect': 'atomosphere', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atomosphere', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is fast and simple .\n->it is fast and simple .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n->ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: food was very good , but not what i would consider out of this world .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was very good , but not what i would consider out of this world .\n->", + "output": "{\"text\": \"food was very good , but not what i would consider out of this world .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good luck getting a table .\n->Good luck getting a table .\n[{'aspect': 'getting a table', 'opinion': 'Good luck', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: go here for a romantic dinner but not for an all out wow dining experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngo here for a romantic dinner but not for an all out wow dining experience .\n->", + "output": "{\"text\": \"go here for a romantic dinner but not for an all out wow dining experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'wow', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their pad penang is delicious and everything else is fantastic .\n->Their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: do n ' t get me started on the margaritas , either .\n->do n ' t get me started on the margaritas , either .\n[{'aspect': 'margaritas', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}]\ntext: my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\n->", + "output": "{\"text\": \"my friend from milan and myself were pleasantly surprised when we arrived and everyone spoke italian .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n->Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n[{'aspect': 'waiters', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'busy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: too bad the food was n ' t of the same heritage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntoo bad the food was n ' t of the same heritage .\n->", + "output": "{\"text\": \"too bad the food was n ' t of the same heritage .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A must for all the Dosa lovers .\n->A must for all the Dosa lovers .\n[{'aspect': 'Dosa', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i would definitely recommend sea if you like thai cuisine !\n->i would definitely recommend sea if you like thai cuisine !\n[{'aspect': 'thai cuisine', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->", + "output": "{\"text\": \"the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\", \"labels\": \"[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n->with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n[{'aspect': 'machine', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: good battery life\n->good battery life\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: a real dissapointment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na real dissapointment .\n->", + "output": "{\"text\": \"a real dissapointment .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'dissapointment', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n->I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n[{'aspect': 'braised lamb shank in red wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it served my needs of web browsing and word processing for a number of years , but its battery life had dwindled and neither the screen nor the processor can match this asus .\n->it served my needs of web browsing and word processing for a number of years , but its battery life had dwindled and neither the screen nor the processor can match this asus .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\ntext: but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n->", + "output": "{\"text\": \"but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\", \"labels\": \"[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was an excellent machine for the money and it also got me spoiled with its touch screen .\n->it was an excellent machine for the money and it also got me spoiled with its touch screen .\n[{'aspect': 'machine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'touch screen', 'opinion': 'spoiled', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n->I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n[{'aspect': 'scallop roll', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they should have called it mascarpone with chocolate chips - good but a far cry from what the name implies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey should have called it mascarpone with chocolate chips - good but a far cry from what the name implies .\n->", + "output": "{\"text\": \"they should have called it mascarpone with chocolate chips - good but a far cry from what the name implies .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: has lots of issues , screen freezing , i was hoping to use it for banking etc .\n->has lots of issues , screen freezing , i was hoping to use it for banking etc .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the service was extremely fast and attentive ( thanks to the service button on your table ) but i barely understood 1 word when the waiter took our order .\n->the service was extremely fast and attentive ( thanks to the service button on your table ) but i barely understood 1 word when the waiter took our order .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service button', 'opinion': 'thanks to', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: priced at upper intermediate range .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npriced at upper intermediate range .\n->", + "output": "{\"text\": \"priced at upper intermediate range .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the machine looks amazing doesn ' t it !\n->the machine looks amazing doesn ' t it !\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Luckily we saved room for the BBQ Salmon , Sea Bass and Crispy Duck .\n->Luckily we saved room for the BBQ Salmon , Sea Bass and Crispy Duck .\n[{'aspect': 'BBQ Salmon', 'opinion': 'Luckily', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Sea Bass', 'opinion': 'Luckily', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Crispy Duck', 'opinion': 'Luckily', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it was pretty inexpensive too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was pretty inexpensive too .\n->", + "output": "{\"text\": \"it was pretty inexpensive too .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\n->I come from a family of pizzeria owners , and I 'm almost ashamed to say that the pizza in Fornino 's blows my families receipies away .\n[{'aspect': 'pizza', 'opinion': 'ashamed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Taxan delicious !\n->Taxan delicious !\n[{'aspect': 'Taxan', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this place has the best chinese style bbq ribs in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place has the best chinese style bbq ribs in the city .\n->", + "output": "{\"text\": \"this place has the best chinese style bbq ribs in the city .\", \"labels\": \"[{'aspect': 'bbq ribs', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bbq ribs', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n->admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n[{'aspect': 'open kitchen', 'opinion': 'charm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Nice atmosphere , the service was very pleasant and the desert was good .\n->Nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'Nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s great to go for a quick lunch either alone or with a friend .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s great to go for a quick lunch either alone or with a friend .\n->", + "output": "{\"text\": \"it ' s great to go for a quick lunch either alone or with a friend .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n->now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'bummed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Very affordable and excellent ambient !\n->Very affordable and excellent ambient !\n[{'aspect': 'ambient', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambient', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s definately not a place to go if you want to impress someone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s definately not a place to go if you want to impress someone .\n->", + "output": "{\"text\": \"it ' s definately not a place to go if you want to impress someone .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'impress', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: People are always friendly .\n->People are always friendly .\n[{'aspect': 'People', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i have to say that the keyboard is my favorite feature .\n->i have to say that the keyboard is my favorite feature .\n[{'aspect': 'keyboard', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: however , if you want great food at a great price and do n ' t mind the decor , you ca n ' t beat this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , if you want great food at a great price and do n ' t mind the decor , you ca n ' t beat this place .\n->", + "output": "{\"text\": \"however , if you want great food at a great price and do n ' t mind the decor , you ca n ' t beat this place .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'decor', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love it .\n->i love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: never got an explanation as to what was going on .\n->never got an explanation as to what was going on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: quick and friendly service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nquick and friendly service .\n->", + "output": "{\"text\": \"quick and friendly service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tech support is useless .\n->tech support is useless .\n[{'aspect': 'tech support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: customer service told me it ' s faulty\n->customer service told me it ' s faulty\n[{'aspect': 'customer service', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: this is one of the best comfort food places in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is one of the best comfort food places in the city .\n->", + "output": "{\"text\": \"this is one of the best comfort food places in the city .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'comfort', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not the typical NYC gimmick theme restaurant .\n->Not the typical NYC gimmick theme restaurant .\n[{'aspect': 'restaurant', 'opinion': 'Not the typical', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: speakers sound tinny .\n->speakers sound tinny .\n[{'aspect': 'speakers', 'opinion': 'tinny', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: it ' s somewhere you can eat and be happy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s somewhere you can eat and be happy .\n->", + "output": "{\"text\": \"it ' s somewhere you can eat and be happy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish the camera was a little better , but it ' s great otherwise .\n->i wish the camera was a little better , but it ' s great otherwise .\n[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n->Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n[{'aspect': 'wine selection', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Gigondas', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'worth', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: when you ' re sitting in their main dining room ( which has a spectacular , hand - painted high ceiling ) you ' d never know there was a world outside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen you ' re sitting in their main dining room ( which has a spectacular , hand - painted high ceiling ) you ' d never know there was a world outside .\n->", + "output": "{\"text\": \"when you ' re sitting in their main dining room ( which has a spectacular , hand - painted high ceiling ) you ' d never know there was a world outside .\", \"labels\": \"[{'aspect': 'main dining room', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ceiling', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ceiling', 'opinion': 'hand - painted high', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen sits too close to the keyboard and you will end up with scratches on the screen .\n->the screen sits too close to the keyboard and you will end up with scratches on the screen .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: this samsung works as expected and is a good , basic chromebook .\n->this samsung works as expected and is a good , basic chromebook .\n[{'aspect': 'samsung', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the food is wonderful , tasty and filling , and the service is professional and friendly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is wonderful , tasty and filling , and the service is professional and friendly .\n->", + "output": "{\"text\": \"the food is wonderful , tasty and filling , and the service is professional and friendly .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'filling', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n->my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n[{'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the laptop works well , i have no complaints .\n->the laptop works well , i have no complaints .\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i ca n ' t wait for summer , when they serve outside on their gigantic patio .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ca n ' t wait for summer , when they serve outside on their gigantic patio .\n->", + "output": "{\"text\": \"i ca n ' t wait for summer , when they serve outside on their gigantic patio .\", \"labels\": \"[{'aspect': 'patio', 'opinion': 'gigantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff are attentive , and have smiles on their faces .\n->The staff are attentive , and have smiles on their faces .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n->my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->", + "output": "{\"text\": \"this little place definitely exceeded my expectations and you sure get a lot of food for your money .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'place', 'opinion': 'exceeded my expectations', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had the cod with paella ( spicy and very filling , I 'm a big eater and could only eat half ) while my boyfriend had the classic fish and chips ( again , a big serving - at least 5 pieces of fish and a basketful of fries ) .\n->I had the cod with paella ( spicy and very filling , I 'm a big eater and could only eat half ) while my boyfriend had the classic fish and chips ( again , a big serving - at least 5 pieces of fish and a basketful of fries ) .\n[{'aspect': 'cod with paella', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cod with paella', 'opinion': 'filling', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'classic', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'big', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'serving', 'opinion': 'big', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the screen is plenty big and the visual very nice .\n->the screen is plenty big and the visual very nice .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n->", + "output": "{\"text\": \"the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot sauce', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was great as well .\n->The service was great as well .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we have gone for dinner only a few times but the same great quality and service is given .\n->we have gone for dinner only a few times but the same great quality and service is given .\n[{'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n->", + "output": "{\"text\": \"i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pleasantly suprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what an excellent computer .\n->what an excellent computer .\n[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s basically a useless brick , with shoddy hardware .\n->it ' s basically a useless brick , with shoddy hardware .\n[{'aspect': 'NULL', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'shoddy', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: they all know you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey all know you .\n->", + "output": "{\"text\": \"they all know you .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works great for what i need .\n->it works great for what i need .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it printed easily to our wireless printer too !\n->it printed easily to our wireless printer too !\n[{'aspect': 'NULL', 'opinion': 'easily', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood food .\n->", + "output": "{\"text\": \"good food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n->the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n[{'aspect': 'hot dogs', 'opinion': 'juicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dogs', 'opinion': 'tender', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The best burger I have had in the Village .\n->The best burger I have had in the Village .\n[{'aspect': 'burger', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: good drink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood drink .\n->", + "output": "{\"text\": \"good drink .\", \"labels\": \"[{'aspect': 'drink', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s okay for email , facebook , surfing the net , etc .\n->it ' s okay for email , facebook , surfing the net , etc .\n[{'aspect': 'email', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'facebook', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: The place is sleek , modern and playful and i will return again frequently .\n->The place is sleek , modern and playful and i will return again frequently .\n[{'aspect': 'place', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'playful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: how do you rate home ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhow do you rate home ?\n->", + "output": "{\"text\": \"how do you rate home ?\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great indian food !\n->great indian food !\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: unfortunately , the downfall for me are the speakers .\n->unfortunately , the downfall for me are the speakers .\n[{'aspect': 'speakers', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: i have never had cheescake like this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have never had cheescake like this .\n->", + "output": "{\"text\": \"i have never had cheescake like this .\", \"labels\": \"[{'aspect': 'cheescake', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->the service is excellent , the decor is great , and the food is delicious and comes in large portions .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the prices are wonderfully low .\n->the prices are wonderfully low .\n[{'aspect': 'NULL', 'opinion': 'wonderfully low', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: i thought i had died and gone to heaven .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni thought i had died and gone to heaven .\n->", + "output": "{\"text\": \"i thought i had died and gone to heaven .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the perfect date spot for Williamsburg couples .\n->This is the perfect date spot for Williamsburg couples .\n[{'aspect': 'date spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the machine is easy to use , snappy , and everything the reviewers say .\n->the machine is easy to use , snappy , and everything the reviewers say .\n[{'aspect': 'machine', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'machine', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: great spot , whether looking for a couple of drinks or quiet dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat spot , whether looking for a couple of drinks or quiet dinner .\n->", + "output": "{\"text\": \"great spot , whether looking for a couple of drinks or quiet dinner .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fine dining restaurant quality .\n->fine dining restaurant quality .\n[{'aspect': 'quality', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but i fed up with the price it cost to upgrade the product as well as the software .\n->but i fed up with the price it cost to upgrade the product as well as the software .\n[{'aspect': 'NULL', 'opinion': 'fed up', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: warm and friendly in the winter and terrific outdoor seating in the warmer months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwarm and friendly in the winter and terrific outdoor seating in the warmer months .\n->", + "output": "{\"text\": \"warm and friendly in the winter and terrific outdoor seating in the warmer months .\", \"labels\": \"[{'aspect': 'outdoor seating', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just do n ' t understand all the hype . . .\n->i just do n ' t understand all the hype . . .\n[{'aspect': 'NULL', 'opinion': 'hype', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n->myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n[{'aspect': 'myagi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the food is great and they have a good selection of wines at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is great and they have a good selection of wines at reasonable prices .\n->", + "output": "{\"text\": \"the food is great and they have a good selection of wines at reasonable prices .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wines', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is a wonderful place on all stand points especially value ofr money .\n->This is a wonderful place on all stand points especially value ofr money .\n[{'aspect': 'place', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the display clarity is outstanding .\n->the display clarity is outstanding .\n[{'aspect': 'display clarity', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: we were less than impressed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were less than impressed .\n->", + "output": "{\"text\": \"we were less than impressed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'less than impressed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a little pricey but it really hits the spot on a sunday morning !\n->a little pricey but it really hits the spot on a sunday morning !\n[{'aspect': 'NULL', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'hits', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: while the ambiance and atmosphere were great , the food and service could have been a lot better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile the ambiance and atmosphere were great , the food and service could have been a lot better .\n->", + "output": "{\"text\": \"while the ambiance and atmosphere were great , the food and service could have been a lot better .\", \"labels\": \"[{'aspect': 'ambiance', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is something about their atmosphere that makes me come back nearly every week .\n->there is something about their atmosphere that makes me come back nearly every week .\n[{'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: - 360 degrees flipping is actually pretty practical\n->- 360 degrees flipping is actually pretty practical\n[{'aspect': '360 degrees flipping', 'opinion': 'practical', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n->", + "output": "{\"text\": \"we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\", \"labels\": \"[{'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a little crowded but they move that line really fast !\n->a little crowded but they move that line really fast !\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: probably would not go again . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprobably would not go again . . .\n->", + "output": "{\"text\": \"probably would not go again . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The atmosphere is great ! ! !\n->The atmosphere is great ! ! !\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - android apps and google play store are real game changers for the chromeos landscape .\n->- android apps and google play store are real game changers for the chromeos landscape .\n[{'aspect': 'google play store', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: i ' ve been to sapphire twice and both times the food was fine , if not good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been to sapphire twice and both times the food was fine , if not good .\n->", + "output": "{\"text\": \"i ' ve been to sapphire twice and both times the food was fine , if not good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fine', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n->i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the camera sucks .\n->the camera sucks .\n[{'aspect': 'camera', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: stick with the chicken , beef , and lamb dishes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstick with the chicken , beef , and lamb dishes .\n->", + "output": "{\"text\": \"stick with the chicken , beef , and lamb dishes .\", \"labels\": \"[{'aspect': 'chicken', 'opinion': 'stick', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beef', 'opinion': 'stick', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb dishes', 'opinion': 'stick', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great sushi experience .\n->Great sushi experience .\n[{'aspect': 'sushi', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .\n->Myagi is one of my favorite restaurants in the City ; the place the negative reviews describe sound like they were somewhere else .\n[{'aspect': 'Myagi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\ntext: service is friendly , and never had a problem walking in and getting a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is friendly , and never had a problem walking in and getting a table .\n->", + "output": "{\"text\": \"service is friendly , and never had a problem walking in and getting a table .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n->it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: skip dessert .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nskip dessert .\n->", + "output": "{\"text\": \"skip dessert .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it !\n->love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: They are often crowded on the weekends but they are efficient and accurate with their service .\n->They are often crowded on the weekends but they are efficient and accurate with their service .\n[{'aspect': 'service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crowded', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\ntext: best reuben sandwich ever !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest reuben sandwich ever !\n->", + "output": "{\"text\": \"best reuben sandwich ever !\", \"labels\": \"[{'aspect': 'reuben sandwich', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n->i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n[{'aspect': 'flip', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i had never had edamame pureed before but i thought it was innovative and tasty ( could ' ve used a bit more salt ) .\n->i had never had edamame pureed before but i thought it was innovative and tasty ( could ' ve used a bit more salt ) .\n[{'aspect': 'edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: a classic !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na classic !\n->", + "output": "{\"text\": \"a classic !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'classic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cons : delete key near the power button ( oops ! )\n->cons : delete key near the power button ( oops ! )\n[{'aspect': 'delete key', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: Only wine and beer are served , but the house varities are actually quite good .\n->Only wine and beer are served , but the house varities are actually quite good .\n[{'aspect': 'house varities', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: do n ' t miss bloom ' s on your next trip to manhatten .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo n ' t miss bloom ' s on your next trip to manhatten .\n->", + "output": "{\"text\": \"do n ' t miss bloom ' s on your next trip to manhatten .\", \"labels\": \"[{'aspect': \"bloom ' s\", 'opinion': \"n ' t miss\", 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: works great and looks great .\n->works great and looks great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i say this device is worth no more than $ 300 .\n->i say this device is worth no more than $ 300 .\n[{'aspect': 'device', 'opinion': 'worth', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: it was the first place we ate on our first trip to new york , and it will be the last place we stop as we head out of town on our next trip to new york .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was the first place we ate on our first trip to new york , and it will be the last place we stop as we head out of town on our next trip to new york .\n->", + "output": "{\"text\": \"it was the first place we ate on our first trip to new york , and it will be the last place we stop as we head out of town on our next trip to new york .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bottom plastic piece - this is not a unibody machine - will be too hot to rest on bare skin .\n->the bottom plastic piece - this is not a unibody machine - will be too hot to rest on bare skin .\n[{'aspect': 'plastic piece', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: everything is very smooth and fast .\n->everything is very smooth and fast .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: thanks bloom ' s for a lovely trip .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthanks bloom ' s for a lovely trip .\n->", + "output": "{\"text\": \"thanks bloom ' s for a lovely trip .\", \"labels\": \"[{'aspect': \"bloom ' s\", 'opinion': 'lovely', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if only they delivered , they ' d make a mint !\n->if only they delivered , they ' d make a mint !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: when my dessert came , there was a candle in it - not because anyone asked for one - but because the waiter must have seen me opening my birthday card and gift , and said he knew it was a special occassion of some sort .\n->when my dessert came , there was a candle in it - not because anyone asked for one - but because the waiter must have seen me opening my birthday card and gift , and said he knew it was a special occassion of some sort .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the food was not fresh , the sauces were bland and very oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was not fresh , the sauces were bland and very oily .\n->", + "output": "{\"text\": \"the food was not fresh , the sauces were bland and very oily .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauces', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauces', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n->the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n[{'aspect': 'form factor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: I absolutely Loved this place .\n->I absolutely Loved this place .\n[{'aspect': 'place', 'opinion': 'Loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it just was n ' t thai .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit just was n ' t thai .\n->", + "output": "{\"text\": \"it just was n ' t thai .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we asked for beverages and never received them .\n->we asked for beverages and never received them .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: decent wine at reasonable prices .\n->decent wine at reasonable prices .\n[{'aspect': 'wine', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\ntext: was surprisingly disappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwas surprisingly disappointed .\n->", + "output": "{\"text\": \"was surprisingly disappointed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n->My suggestion is to eat family style because you 'll want to try the other dishes .\n[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Only complaint would be that at an average cost of $ 12- $ 15 per meal , I 'd like not to have to worry about finding a seat !\n->Only complaint would be that at an average cost of $ 12- $ 15 per meal , I 'd like not to have to worry about finding a seat !\n[{'aspect': 'cost', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seat', 'opinion': 'not to have to worry', 'polarity': 'negative', 'category': 'NULL'}]\ntext: pizza was a little soggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npizza was a little soggy .\n->", + "output": "{\"text\": \"pizza was a little soggy .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Highly recommend this as great value for excellent sushi and service .\n->Highly recommend this as great value for excellent sushi and service .\n[{'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ravioli was good . . . but i have to say that i found everything a bit overpriced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nravioli was good . . . but i have to say that i found everything a bit overpriced .\n->", + "output": "{\"text\": \"ravioli was good . . . but i have to say that i found everything a bit overpriced .\", \"labels\": \"[{'aspect': 'ravioli', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: win for the ports , win for the price , and win for a brand new unopened macbook .\n->win for the ports , win for the price , and win for a brand new unopened macbook .\n[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Good luck getting a table .\n->Good luck getting a table .\n[{'aspect': 'getting a table', 'opinion': 'Good luck', 'polarity': 'negative', 'category': 'NULL'}]\ntext: not enough wines by the glass either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot enough wines by the glass either .\n->", + "output": "{\"text\": \"not enough wines by the glass either .\", \"labels\": \"[{'aspect': 'wines by the glass', 'opinion': 'not enough', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n->The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n[{'aspect': 'plain slice', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is can became on e of the ny italian food fare institutions .\n->this is can became on e of the ny italian food fare institutions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice , however , was excellent . . . and i liked the setting / atmosphere a lot .\n->", + "output": "{\"text\": \"service , however , was excellent . . . and i liked the setting / atmosphere a lot .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'setting / atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff is very good .\n->the staff is very good .\n[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: ca n ' t wait to go back .\n->ca n ' t wait to go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: food was just average . . . if they lowered the prices just a bit , it would be a bigger draw .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was just average . . . if they lowered the prices just a bit , it would be a bigger draw .\n->", + "output": "{\"text\": \"food was just average . . . if they lowered the prices just a bit , it would be a bigger draw .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far i really love this product !\n->so far i really love this product !\n[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great price - i always buy the warranty .\n->great price - i always buy the warranty .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'warranty', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}]\ntext: authentic pakistani food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nauthentic pakistani food .\n->", + "output": "{\"text\": \"authentic pakistani food .\", \"labels\": \"[{'aspect': 'pakistani food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bagel was huge .\n->the bagel was huge .\n[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: just straight up cheap , good food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust straight up cheap , good food .\n->", + "output": "{\"text\": \"just straight up cheap , good food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was prompt and courteous .\n->Service was prompt and courteous .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: No free drink .\n->No free drink .\n[{'aspect': 'drink', 'opinion': 'No free', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: faan is sooo good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfaan is sooo good .\n->", + "output": "{\"text\": \"faan is sooo good .\", \"labels\": \"[{'aspect': 'faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love their drink menu .\n->love their drink menu .\n[{'aspect': 'drink menu', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: they wouldnt even let me finish my glass of wine before offering another .\n->they wouldnt even let me finish my glass of wine before offering another .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the design and atmosphere is just as good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe design and atmosphere is just as good .\n->", + "output": "{\"text\": \"the design and atmosphere is just as good .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it would not start up after 2 months of purchase and then re - set button didn ' t work .\n->it would not start up after 2 months of purchase and then re - set button didn ' t work .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 're - set button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: On the other hand , if you are not fooled easily , you will find hundreds of restaurants that will give you service and ambiance that is on par with Alain Ducasse , and food that will outshine in presentaion , taste , choice , quality and quantity .\n->On the other hand , if you are not fooled easily , you will find hundreds of restaurants that will give you service and ambiance that is on par with Alain Ducasse , and food that will outshine in presentaion , taste , choice , quality and quantity .\n[{'aspect': 'service', 'opinion': 'on par', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'on par', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'outshine', 'polarity': 'negative', 'category': 'NULL'}]\ntext: bottles of wine are cheap and good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbottles of wine are cheap and good .\n->", + "output": "{\"text\": \"bottles of wine are cheap and good .\", \"labels\": \"[{'aspect': 'bottles of wine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}, {'aspect': 'bottles of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worst customer service experience in years .\n->worst customer service experience in years .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: love pizza 33 . . .\n->love pizza 33 . . .\n[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: what more could you want ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat more could you want ?\n->", + "output": "{\"text\": \"what more could you want ?\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i enjoy that it has 10 key .\n->i enjoy that it has 10 key .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: my first time with a solid state drive , very nice quick and quiet .\n->my first time with a solid state drive , very nice quick and quiet .\n[{'aspect': 'solid state drive', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'solid state drive', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: the food was actually aweful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was actually aweful .\n->", + "output": "{\"text\": \"the food was actually aweful .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'aweful', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n->at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n[{'aspect': 'model', 'opinion': 'afraid', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'amazon', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: My son and his girlfriend both wanted cheeseburgers and they were huge !\n->My son and his girlfriend both wanted cheeseburgers and they were huge !\n[{'aspect': 'cheeseburgers', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i ' m not picky - but it was actually gross .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m not picky - but it was actually gross .\n->", + "output": "{\"text\": \"i ' m not picky - but it was actually gross .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sound is .\n->sound is .\n[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: the food was amazing , and the service was prompt and helpful , but not over - bearing or rushed .\n->the food was amazing , and the service was prompt and helpful , but not over - bearing or rushed .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'not over -', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'or', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the mussles were the fishiest things i ' ve ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w / chicken had bones in it . . . it was disgusting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mussles were the fishiest things i ' ve ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w / chicken had bones in it . . . it was disgusting .\n->", + "output": "{\"text\": \"the mussles were the fishiest things i ' ve ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w / chicken had bones in it . . . it was disgusting .\", \"labels\": \"[{'aspect': 'mussles', 'opinion': 'fishiest', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'seabass', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'goat cheese salad', 'opinion': 'missing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'penne w / chicken', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I did n't take a look at the rest menu , but the oysters were fantastic .\n->I did n't take a look at the rest menu , but the oysters were fantastic .\n[{'aspect': 'oysters', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was great .\n->The food was great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: nice atmosphere , the service was very pleasant and the desert was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice atmosphere , the service was very pleasant and the desert was good .\n->", + "output": "{\"text\": \"nice atmosphere , the service was very pleasant and the desert was good .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but for the shabu shabu , you wo n ' t find much better in ny .\n->but for the shabu shabu , you wo n ' t find much better in ny .\n[{'aspect': 'shabu shabu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n->The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is the perfect date spot for williamsburg couples .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the perfect date spot for williamsburg couples .\n->", + "output": "{\"text\": \"this is the perfect date spot for williamsburg couples .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just received the computer back from repairs and it worked for about 2 days and the same problem started happening again .\n->i just received the computer back from repairs and it worked for about 2 days and the same problem started happening again .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The sangria was pretty tasty and good on a hot muggy day .\n->The sangria was pretty tasty and good on a hot muggy day .\n[{'aspect': 'sangria', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sangria', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the jukebox plays everything from italian opera to the strokes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe jukebox plays everything from italian opera to the strokes .\n->", + "output": "{\"text\": \"the jukebox plays everything from italian opera to the strokes .\", \"labels\": \"[{'aspect': 'jukebox', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very easy to set up and light to carry .\n->very easy to set up and light to carry .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: the battery seriously lasts as long as i need it , and i ' m one to forget to charge it .\n->the battery seriously lasts as long as i need it , and i ' m one to forget to charge it .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the food is amazing , rich pastas and fresh doughy pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is amazing , rich pastas and fresh doughy pizza .\n->", + "output": "{\"text\": \"the food is amazing , rich pastas and fresh doughy pizza .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pastas', 'opinion': 'rich', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'pizza', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'doughy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great and reasonably priced .\n->The food is great and reasonably priced .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: possibly the most romantic restaurant in the city\n->possibly the most romantic restaurant in the city\n[{'aspect': 'restaurant', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: best of all is the warm vibe , the owner is super friendly and service is fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest of all is the warm vibe , the owner is super friendly and service is fast .\n->", + "output": "{\"text\": \"best of all is the warm vibe , the owner is super friendly and service is fast .\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food , great decor , great service .\n->great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: This place is a great bargain .\n->This place is a great bargain .\n[{'aspect': 'place', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if only they delivered , they ' d make a mint !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif only they delivered , they ' d make a mint !\n->", + "output": "{\"text\": \"if only they delivered , they ' d make a mint !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very happy with my purchase , fast delivery , package well .\n->very happy with my purchase , fast delivery , package well .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'package', 'opinion': 'well', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: They have a very diverse menu so its something for everybody .\n->They have a very diverse menu so its something for everybody .\n[{'aspect': 'menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}]\ntext: go here for the drinks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngo here for the drinks !\n->", + "output": "{\"text\": \"go here for the drinks !\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thius is a must for anyone who loves shabu - shabu .\n->thius is a must for anyone who loves shabu - shabu .\n[{'aspect': 'shabu - shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the keyboard is easy to use , and there is no external noise to contend with .\n->the keyboard is easy to use , and there is no external noise to contend with .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: the drinks are amazing and half off till 8pm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe drinks are amazing and half off till 8pm .\n->", + "output": "{\"text\": \"the drinks are amazing and half off till 8pm .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'drinks', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n->i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n[{'aspect': 'chrome os', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'simple', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food has been consistant for years and it never lets you down .\n->The food has been consistant for years and it never lets you down .\n[{'aspect': 'food', 'opinion': 'consistant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my fav was the sassy lassi . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy fav was the sassy lassi . . .\n->", + "output": "{\"text\": \"my fav was the sassy lassi . . .\", \"labels\": \"[{'aspect': 'sassy lassi', 'opinion': 'fav', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .\n->If you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .\n[{'aspect': 'marinara/arrabiatta sauce', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'marinara/arrabiatta sauce', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mozzarella en Carozza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it works well for internet browsing and e - mail but i was hoping for much more .\n->it works well for internet browsing and e - mail but i was hoping for much more .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: this is an amazing place to try some roti rolls .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is an amazing place to try some roti rolls .\n->", + "output": "{\"text\": \"this is an amazing place to try some roti rolls .\", \"labels\": \"[{'aspect': 'roti rolls', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in the summer months , the back garden area is really nice .\n->in the summer months , the back garden area is really nice .\n[{'aspect': 'back garden area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: no backlit keyboard is kinda a bummer but i digress .\n->no backlit keyboard is kinda a bummer but i digress .\n[{'aspect': 'backlit keyboard', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i really recommend the very simple unda ( egg ) rolls .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really recommend the very simple unda ( egg ) rolls .\n->", + "output": "{\"text\": \"i really recommend the very simple unda ( egg ) rolls .\", \"labels\": \"[{'aspect': 'unda ( egg ) rolls', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'unda ( egg ) rolls', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fried dumplings are GREAT !\n->The fried dumplings are GREAT !\n[{'aspect': 'fried dumplings', 'opinion': 'GREAT', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Service is not what one would expect from a joint in this price category .\n->Service is not what one would expect from a joint in this price category .\n[{'aspect': 'Service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'NULL'}]\ntext: delicate spices , onions , eggs and a kick - ass roti .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicate spices , onions , eggs and a kick - ass roti .\n->", + "output": "{\"text\": \"delicate spices , onions , eggs and a kick - ass roti .\", \"labels\": \"[{'aspect': 'spices', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'onions', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'eggs', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'roti', 'opinion': 'kick - ass', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend to anyone to give this place a try .\n->i highly recommend to anyone to give this place a try .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n->he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n[{'aspect': 'uni hand roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: amazing !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazing !\n->", + "output": "{\"text\": \"amazing !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A great place to meet up for some food and drinks ...\n->A great place to meet up for some food and drinks ...\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: our family never expected such incredible entertainment in a restaurant .\n->our family never expected such incredible entertainment in a restaurant .\n[{'aspect': 'entertainment', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: toons has recently been redone , so it ' s now a very attractive space .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntoons has recently been redone , so it ' s now a very attractive space .\n->", + "output": "{\"text\": \"toons has recently been redone , so it ' s now a very attractive space .\", \"labels\": \"[{'aspect': 'toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - excellent build quality - aluminium case is solid and has a premium feel\n->- excellent build quality - aluminium case is solid and has a premium feel\n[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminium case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminium case', 'opinion': 'premium', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: for the price , it ' s a solid laptop .\n->for the price , it ' s a solid laptop .\n[{'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: in an area sadly lacking in decent thai food , this is one of the best spots .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin an area sadly lacking in decent thai food , this is one of the best spots .\n->", + "output": "{\"text\": \"in an area sadly lacking in decent thai food , this is one of the best spots .\", \"labels\": \"[{'aspect': 'thai food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n->not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i did n ' t complain , i liked the atmosphere so much .\n->i did n ' t complain , i liked the atmosphere so much .\n[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: went here last night - nice decor , good service , but the food was surprisingly excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwent here last night - nice decor , good service , but the food was surprisingly excellent .\n->", + "output": "{\"text\": \"went here last night - nice decor , good service , but the food was surprisingly excellent .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was fully charged and i only turned it on a total of 3 times before the screen went blank .\n->it was fully charged and i only turned it on a total of 3 times before the screen went blank .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'the screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: excellent dumplings served amid clean , chic decor .\n->excellent dumplings served amid clean , chic decor .\n[{'aspect': 'dumplings', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'chic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the portions are huge , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe portions are huge , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n->", + "output": "{\"text\": \"the portions are huge , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is ok but could be better .\n->The service is ok but could be better .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'could be better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the pizza was pretty good and huge .\n->the pizza was pretty good and huge .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: ca n ' t wait to go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nca n ' t wait to go back .\n->", + "output": "{\"text\": \"ca n ' t wait to go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first of all , the battery life on it is insane .\n->first of all , the battery life on it is insane .\n[{'aspect': 'battery life', 'opinion': 'insane', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: in seconds , even with an hdd it ' s still starts up in seconds .\n->in seconds , even with an hdd it ' s still starts up in seconds .\n[{'aspect': 'starts up', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\ntext: beef noodle soup is good as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeef noodle soup is good as well .\n->", + "output": "{\"text\": \"beef noodle soup is good as well .\", \"labels\": \"[{'aspect': 'beef noodle soup', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unable to contact asus support for help .\n->unable to contact asus support for help .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the sashimi is always fresh and the rolls are innovative and delicious .\n->the sashimi is always fresh and the rolls are innovative and delicious .\n[{'aspect': 'sashimi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the service is good and the resturant is clean .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service is good and the resturant is clean .\n->", + "output": "{\"text\": \"the service is good and the resturant is clean .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'resturant', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n->I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the pro is by far the best .\n->the pro is by far the best .\n[{'aspect': 'pro', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: best taiwanese food in ny !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest taiwanese food in ny !\n->", + "output": "{\"text\": \"best taiwanese food in ny !\", \"labels\": \"[{'aspect': 'taiwanese food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu is limited but almost all of the dishes are excellent .\n->The menu is limited but almost all of the dishes are excellent .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is working really well .\n->it is working really well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i have been to rao ' s probably 15 times the past 3 years and it keeps getting better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been to rao ' s probably 15 times the past 3 years and it keeps getting better .\n->", + "output": "{\"text\": \"i have been to rao ' s probably 15 times the past 3 years and it keeps getting better .\", \"labels\": \"[{'aspect': \"rao ' s\", 'opinion': 'better', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .\n->nobody at this restaurant will give firm answers about anything and in the end , not one person takes responsibility for anything .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: downloading is very fast over wifi .\n->downloading is very fast over wifi .\n[{'aspect': 'wifi', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: rao ' s has the best service and atmosphere in nyc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nrao ' s has the best service and atmosphere in nyc .\n->", + "output": "{\"text\": \"rao ' s has the best service and atmosphere in nyc .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i knocked off a star for build quality control .\n->i knocked off a star for build quality control .\n[{'aspect': 'build quality control', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i ' ve had this for about 3 weeks , and i ' m loving it .\n->i ' ve had this for about 3 weeks , and i ' m loving it .\n[{'aspect': 'NULL', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: good luck getting a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood luck getting a table .\n->", + "output": "{\"text\": \"good luck getting a table .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n->the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n[{'aspect': '4gb / 32gb version', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': '4gb / 32gb version', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage space', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: A touch more jalapeno heat for contrast and it would have been very good indeed .\n->A touch more jalapeno heat for contrast and it would have been very good indeed .\n[{'aspect': 'jalapeno', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my roommate and i love this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy roommate and i love this place .\n->", + "output": "{\"text\": \"my roommate and i love this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s terrible .\n->it ' s terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: if i could give 0 stars i would do so for this place .\n->if i could give 0 stars i would do so for this place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n->", + "output": "{\"text\": \"we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\", \"labels\": \"[{'aspect': 'outdoor seating', 'opinion': 'delight', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is more than sufficient for non - gaming , business use , and it seems as fast as expected .\n->the screen is more than sufficient for non - gaming , business use , and it seems as fast as expected .\n[{'aspect': 'screen', 'opinion': 'sufficient', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Friendly and informative staff , very attentive and prompt raw bar service .\n->Friendly and informative staff , very attentive and prompt raw bar service .\n[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'informative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar service', 'opinion': 'raw', 'polarity': 'positive', 'category': 'NULL'}]\ntext: indoor was very cozy and cute .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nindoor was very cozy and cute .\n->", + "output": "{\"text\": \"indoor was very cozy and cute .\", \"labels\": \"[{'aspect': 'indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very poor experience .\n->very poor experience .\n[{'aspect': 'NULL', 'opinion': 'poor', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: This place would be so much better served by being run by a group that actually understands customer service .\n->This place would be so much better served by being run by a group that actually understands customer service .\n[{'aspect': 'service', 'opinion': 'would be so much better', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the portion sizes here are huge , and the sushi is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe portion sizes here are huge , and the sushi is good .\n->", + "output": "{\"text\": \"the portion sizes here are huge , and the sushi is good .\", \"labels\": \"[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fine dining restaurant quality .\n->fine dining restaurant quality .\n[{'aspect': 'quality', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: loved it for an hour than it went black and we got a chrome os missing or damage message .\n->loved it for an hour than it went black and we got a chrome os missing or damage message .\n[{'aspect': 'chrome os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#QUALITY'}]\ntext: staff is very accomodating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstaff is very accomodating .\n->", + "output": "{\"text\": \"staff is very accomodating .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my main gripe is incompatibility with amazon prime videos and gogo .\n->my main gripe is incompatibility with amazon prime videos and gogo .\n[{'aspect': 'amazon prime videos and gogo', 'opinion': 'gripe', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: i ' ve had my fair share of modern japanese and this spot delivers .\n->i ' ve had my fair share of modern japanese and this spot delivers .\n[{'aspect': 'modern japanese', 'opinion': 'delivers', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: excellent dumplings served amid clean , chic decor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent dumplings served amid clean , chic decor .\n->", + "output": "{\"text\": \"excellent dumplings served amid clean , chic decor .\", \"labels\": \"[{'aspect': 'dumplings', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'chic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I liked the food at this quasi-thai restaurant .\n->I liked the food at this quasi-thai restaurant .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the problem is a complete lack of local hardware support in the us unless you happen to live in temple , tx .\n->the problem is a complete lack of local hardware support in the us unless you happen to live in temple , tx .\n[{'aspect': 'hardware support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: i got the $ 10 10 - piece dim sum combo , every bite of which was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got the $ 10 10 - piece dim sum combo , every bite of which was great .\n->", + "output": "{\"text\": \"i got the $ 10 10 - piece dim sum combo , every bite of which was great .\", \"labels\": \"[{'aspect': '$ 10 10 - piece dim sum combo', 'opinion': 'i', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they honored reservation on sunday afternoon very well .\n->they honored reservation on sunday afternoon very well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n->This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but $ 1 for each small piece ? ? ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut $ 1 for each small piece ? ? ?\n->", + "output": "{\"text\": \"but $ 1 for each small piece ? ? ?\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n->While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n[{'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the battery broke after just 4 months from baying it am so disappointed with the product\n->the battery broke after just 4 months from baying it am so disappointed with the product\n[{'aspect': 'battery', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}, {'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i wo n ' t go back unless someone else is footing the bill .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wo n ' t go back unless someone else is footing the bill .\n->", + "output": "{\"text\": \"i wo n ' t go back unless someone else is footing the bill .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i generally like this place .\n->i generally like this place .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: highly recommend it !\n->highly recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the decor is very simple but comfortable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe decor is very simple but comfortable .\n->", + "output": "{\"text\": \"the decor is very simple but comfortable .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'simple', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a few hours of use later , i noticed that my battery was very low , around 10 % .\n->a few hours of use later , i noticed that my battery was very low , around 10 % .\n[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n->certain apps ( especially flash based apps ) will get the machine very hot .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: the food was delicious but do not come here on a empty stomach .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was delicious but do not come here on a empty stomach .\n->", + "output": "{\"text\": \"the food was delicious but do not come here on a empty stomach .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n->However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n[{'aspect': 'ambiance', 'opinion': 'drawn', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 5 pound laptop with its nine hour battery life .\n->5 pound laptop with its nine hour battery life .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the portions are small but being that the food was so good makes up for that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe portions are small but being that the food was so good makes up for that .\n->", + "output": "{\"text\": \"the portions are small but being that the food was so good makes up for that .\", \"labels\": \"[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then about 3 months in , it quit charging from the supplied fast charger .\n->then about 3 months in , it quit charging from the supplied fast charger .\n[{'aspect': 'fast charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n->The miso soup lacked flavor and the fish was unfortunately not as well prepared as in the past .\n[{'aspect': 'miso soup', 'opinion': 'lacked flavor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'unfortunately', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: you must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\n->", + "output": "{\"text\": \"you must have the crabmeat lasagna which is out of this world and the chocolate bread pudding for dessert .\", \"labels\": \"[{'aspect': 'crabmeat lasagna', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chocolate bread pudding', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , the power button placement is not very good .\n->also , the power button placement is not very good .\n[{'aspect': 'power button', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: 2 ) slow start up and performance given\n->2 ) slow start up and performance given\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the staff there is very attentive and down to earth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff there is very attentive and down to earth .\n->", + "output": "{\"text\": \"the staff there is very attentive and down to earth .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: performance - wise , i can easily have 12 - 24 tabs open simultaneously and see no slow - down in performance .\n->performance - wise , i can easily have 12 - 24 tabs open simultaneously and see no slow - down in performance .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: great product ( perfect for student use ) but did n ' t last past 2 months .\n->great product ( perfect for student use ) but did n ' t last past 2 months .\n[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i loved it and would go again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved it and would go again .\n->", + "output": "{\"text\": \"i loved it and would go again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n->The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n[{'aspect': 'decor', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'low', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"food 's presentation\", 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: bottles of wine are cheap and good .\n->bottles of wine are cheap and good .\n[{'aspect': 'bottles of wine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bottles of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great indian food and the service is incredible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat indian food and the service is incredible .\n->", + "output": "{\"text\": \"great indian food and the service is incredible .\", \"labels\": \"[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it gets used everyday for work all day long and some light gaming in the evening .\n->it gets used everyday for work all day long and some light gaming in the evening .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n->I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the owner truly caters to all your needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe owner truly caters to all your needs .\n->", + "output": "{\"text\": \"the owner truly caters to all your needs .\", \"labels\": \"[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n->i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'boot speed', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'OS#GENERAL'}, {'aspect': 'cooling fan', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: i did not find the battery to last a full ten hours .\n->i did not find the battery to last a full ten hours .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: when family came in he gave them apps to test their palets , and then ordered for them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen family came in he gave them apps to test their palets , and then ordered for them .\n->", + "output": "{\"text\": \"when family came in he gave them apps to test their palets , and then ordered for them .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m not a huge gamer , but it can run crysis with full mods on ultra settings and doesn ' t make so much as a light hum .\n->i ' m not a huge gamer , but it can run crysis with full mods on ultra settings and doesn ' t make so much as a light hum .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: everyone was more then happy with his choices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neveryone was more then happy with his choices .\n->", + "output": "{\"text\": \"everyone was more then happy with his choices .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: doing general internet surfing is a breeze on this laptop .\n->doing general internet surfing is a breeze on this laptop .\n[{'aspect': 'laptop', 'opinion': 'breeze', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: great food and the prices are very reasonable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food and the prices are very reasonable .\n->", + "output": "{\"text\": \"great food and the prices are very reasonable .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food came out wrong , the waiter was no where to be found and the wine showed up at the end of the meal .\n->The food came out wrong , the waiter was no where to be found and the wine showed up at the end of the meal .\n[{'aspect': 'food', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it is fantastic for the things that i need a computer for .\n->it is fantastic for the things that i need a computer for .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the food here does a great service to the name ( cantonese that is . . . ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food here does a great service to the name ( cantonese that is . . . ) .\n->", + "output": "{\"text\": \"the food here does a great service to the name ( cantonese that is . . . ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am able to write , look up facts on the internet , and it has a pretty good battery life .\n->i am able to write , look up facts on the internet , and it has a pretty good battery life .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: I have never before eaten 40 pieces of relatively good nigiri .\n->I have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n->", + "output": "{\"text\": \"i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\", \"labels\": \"[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my only issue is the wifi likes to randomly turn off then back on .\n->my only issue is the wifi likes to randomly turn off then back on .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: so rude ! ! !\n->so rude ! ! !\n[{'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: try the congee and the donut like deep fried dough they call ow ley soh , a delicious and sweet tasting bread .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry the congee and the donut like deep fried dough they call ow ley soh , a delicious and sweet tasting bread .\n->", + "output": "{\"text\": \"try the congee and the donut like deep fried dough they call ow ley soh , a delicious and sweet tasting bread .\", \"labels\": \"[{'aspect': 'congee', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ow ley soh', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ow ley soh', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s lightweight , sleek , and sexy as hell .\n->it ' s lightweight , sleek , and sexy as hell .\n[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sexy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it was well worth the wait .\n->it was well worth the wait .\n[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: simply some good tasting chinese food at incredible prices . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsimply some good tasting chinese food at incredible prices . . .\n->", + "output": "{\"text\": \"simply some good tasting chinese food at incredible prices . . .\", \"labels\": \"[{'aspect': 'chinese food', 'opinion': 'good tasting', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chinese food', 'opinion': 'good tasting', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We were seated and ignored by waitstaff .\n->We were seated and ignored by waitstaff .\n[{'aspect': 'waitstaff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: really happy with this laptop !\n->really happy with this laptop !\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: service is not what you are coming here for . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice is not what you are coming here for . . .\n->", + "output": "{\"text\": \"service is not what you are coming here for . . .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a nice place to relax and have conversation .\n->it ' s a nice place to relax and have conversation .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: while their kitchen food is delicious , their sushi is out of this world .\n->while their kitchen food is delicious , their sushi is out of this world .\n[{'aspect': 'kitchen food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: whoever the jazz duo was , they were on point .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhoever the jazz duo was , they were on point .\n->", + "output": "{\"text\": \"whoever the jazz duo was , they were on point .\", \"labels\": \"[{'aspect': 'jazz duo', 'opinion': 'on point', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the waitress came to check in on us every few minutes , and began to clear the plates while half of us were still eating ( a big pet peeve of mine that happens almost everywhere , so i try to ignore it ) .\n->the waitress came to check in on us every few minutes , and began to clear the plates while half of us were still eating ( a big pet peeve of mine that happens almost everywhere , so i try to ignore it ) .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: nice screen and keyboard , touch pad is great .\n->nice screen and keyboard , touch pad is great .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: good music , great food , speedy service affordable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood music , great food , speedy service affordable prices .\n->", + "output": "{\"text\": \"good music , great food , speedy service affordable prices .\", \"labels\": \"[{'aspect': 'music', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ca n ' t believe that it was , but please put the bag down before delivering food !\n->i ca n ' t believe that it was , but please put the bag down before delivering food !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: even the wine by the glass was good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven the wine by the glass was good .\n->", + "output": "{\"text\": \"even the wine by the glass was good .\", \"labels\": \"[{'aspect': 'wine by the glass', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i showed it to the manager , and he smilingly apologized and brought us two free desserts ( but did not ask us what we wanted and so brought the last two desserts we would have asked for ) .\n->i showed it to the manager , and he smilingly apologized and brought us two free desserts ( but did not ask us what we wanted and so brought the last two desserts we would have asked for ) .\n[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: none of their android versions were what i would call usable .\n->none of their android versions were what i would call usable .\n[{'aspect': 'android versions', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: its a little out of the way if you do n ' t live in the neighborhood , but definitely worth the trip from wherever you are .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits a little out of the way if you do n ' t live in the neighborhood , but definitely worth the trip from wherever you are .\n->", + "output": "{\"text\": \"its a little out of the way if you do n ' t live in the neighborhood , but definitely worth the trip from wherever you are .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LOCATION#GENERAL'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: probably would not go again . . .\n->probably would not go again . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: consistently good japanese tapas .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nconsistently good japanese tapas .\n->", + "output": "{\"text\": \"consistently good japanese tapas .\", \"labels\": \"[{'aspect': 'japanese tapas', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is great .\n->this place is great .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the new pro model is very light and compact , and can easily be carried around with you every day .\n->the new pro model is very light and compact , and can easily be carried around with you every day .\n[{'aspect': 'pro model', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'easily be carried', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: always good drinks and service is pretty good ;\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalways good drinks and service is pretty good ;\n->", + "output": "{\"text\": \"always good drinks and service is pretty good ;\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n->i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n->my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n[{'aspect': 'meal', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: atmosphere is nice and relaxed too . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \natmosphere is nice and relaxed too . . .\n->", + "output": "{\"text\": \"atmosphere is nice and relaxed too . . .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is fast and simple .\n->it is fast and simple .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the built - in speaker is below average .\n->the built - in speaker is below average .\n[{'aspect': 'built - in speaker', 'opinion': 'below average', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: a great place to meet up for some food and drinks . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na great place to meet up for some food and drinks . . .\n->", + "output": "{\"text\": \"a great place to meet up for some food and drinks . . .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chrome os is like the chrome browser + apps taken up a level .\n->chrome os is like the chrome browser + apps taken up a level .\n[{'aspect': 'chrome os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n->The food was amazing , and the service was prompt and helpful , but not over-bearing or rushed .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'not over-bearing or rushed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: yakitori ( bbq meats ) is tasty too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyakitori ( bbq meats ) is tasty too .\n->", + "output": "{\"text\": \"yakitori ( bbq meats ) is tasty too .\", \"labels\": \"[{'aspect': 'yakitori ( bbq meats )', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was authentic .\n->The food was authentic .\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: ( i had to check that the caps lock was off after typing that last word . )\n->( i had to check that the caps lock was off after typing that last word . )\n[{'aspect': 'caps lock', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: if you do n ' t mind pre - sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you do n ' t mind pre - sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n->", + "output": "{\"text\": \"if you do n ' t mind pre - sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'low quality', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'sushi chef', 'opinion': 'miserable', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff ignored my friends and I the entire time we were there .\n->The staff ignored my friends and I the entire time we were there .\n[{'aspect': 'staff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i use this laptop for work .\n->i use this laptop for work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: price and quality of fish alone will keep us from making a return visit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprice and quality of fish alone will keep us from making a return visit .\n->", + "output": "{\"text\": \"price and quality of fish alone will keep us from making a return visit .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'fish', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'fish', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bukhara grill , the tagline says it all . . ` ` indian spice rave ' '\n->bukhara grill , the tagline says it all . . ` ` indian spice rave ' '\n[{'aspect': 'bukhara grill', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i ' d say the only drawback might be the speakers .\n->i ' d say the only drawback might be the speakers .\n[{'aspect': 'speakers', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwas n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n->", + "output": "{\"text\": \"was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\", \"labels\": \"[{'aspect': 'chef', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far it ' s running smooth with no issues as if it was new .\n->so far it ' s running smooth with no issues as if it was new .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: My boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\n->My boyfriend had the New England Chowder it was good but I think the award should go to the Lobster Bisque .\n[{'aspect': 'New England Chowder', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Lobster Bisque', 'opinion': 'award', 'polarity': 'positive', 'category': 'NULL'}]\ntext: no thanks ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno thanks ! ! !\n->", + "output": "{\"text\": \"no thanks ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The waiters were not attentive except that the bill turned up on the table before we were finished .\n->The waiters were not attentive except that the bill turned up on the table before we were finished .\n[{'aspect': 'waiters', 'opinion': 'attentive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The portions are large and the servers always surprise us with a different starter .\n->The portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this place , which is only a few months old , is perhaps queens ' biggest secret !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place , which is only a few months old , is perhaps queens ' biggest secret !\n->", + "output": "{\"text\": \"this place , which is only a few months old , is perhaps queens ' biggest secret !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the thing that made me return it was the trackpad .\n->the thing that made me return it was the trackpad .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n->The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\ntext: fabulous decor - makes you feel like you ' re in a trendy manhattan restaurant , very very good food , cheaply - priced , generally friendly staff , and if you ' re a manhattanite , or spend most of your time in manhattan , rice avenue will make you feel at home . . . . . very soho / village / upper west side minus the expensive prices and pretentious clientele . . . . . all on roosevelt avenue !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfabulous decor - makes you feel like you ' re in a trendy manhattan restaurant , very very good food , cheaply - priced , generally friendly staff , and if you ' re a manhattanite , or spend most of your time in manhattan , rice avenue will make you feel at home . . . . . very soho / village / upper west side minus the expensive prices and pretentious clientele . . . . . all on roosevelt avenue !\n->", + "output": "{\"text\": \"fabulous decor - makes you feel like you ' re in a trendy manhattan restaurant , very very good food , cheaply - priced , generally friendly staff , and if you ' re a manhattanite , or spend most of your time in manhattan , rice avenue will make you feel at home . . . . . very soho / village / upper west side minus the expensive prices and pretentious clientele . . . . . all on roosevelt avenue !\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheaply - priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are often crowded on the weekends but they are efficient and accurate with their service .\n->They are often crowded on the weekends but they are efficient and accurate with their service .\n[{'aspect': 'service', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crowded', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n->i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'android apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: we were seated outside and the waiter spilled red wine and hot tea on myself and my date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were seated outside and the waiter spilled red wine and hot tea on myself and my date .\n->", + "output": "{\"text\": \"we were seated outside and the waiter spilled red wine and hot tea on myself and my date .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You must try the shrimp appetizers .\n->You must try the shrimp appetizers .\n[{'aspect': 'shrimp appetizers', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i added an sd card which has expanded on the 16gb of storage .\n->i added an sd card which has expanded on the 16gb of storage .\n[{'aspect': 'sd card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\ntext: we were then shooed inside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were then shooed inside .\n->", + "output": "{\"text\": \"we were then shooed inside .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'shooed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Until you realize that their five minutes is meaningless and your wait may be anywhere from two to twenty minutes it may be frustrating .\n->Until you realize that their five minutes is meaningless and your wait may be anywhere from two to twenty minutes it may be frustrating .\n[{'aspect': 'wait', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i ' m partial to the gnocchi .\n->i ' m partial to the gnocchi .\n[{'aspect': 'gnocchi', 'opinion': 'partial', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: one would think we ' d get an apology or complimentary drinks - instead , we got a snobby waiter would n ' t even take our order for 15 minutes and gave us lip when we asked him to do so .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none would think we ' d get an apology or complimentary drinks - instead , we got a snobby waiter would n ' t even take our order for 15 minutes and gave us lip when we asked him to do so .\n->", + "output": "{\"text\": \"one would think we ' d get an apology or complimentary drinks - instead , we got a snobby waiter would n ' t even take our order for 15 minutes and gave us lip when we asked him to do so .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'snobby', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is a cute place and could be good but they need to get their act together .\n->This is a cute place and could be good but they need to get their act together .\n[{'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: first of all , the battery life on it is insane .\n->first of all , the battery life on it is insane .\n[{'aspect': 'battery life', 'opinion': 'insane', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: we left , never to return .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe left , never to return .\n->", + "output": "{\"text\": \"we left , never to return .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not a very fancy place but very good chinese style indian food .\n->not a very fancy place but very good chinese style indian food .\n[{'aspect': 'place', 'opinion': 'fancy', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'chinese style indian food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: with so many good restaurants on the uws , i do n ' t need overpriced food , absurdly arrogant wait - staff who do n ' t recognize they work at a glorified diner , clumsy service , and management that does n ' t care .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith so many good restaurants on the uws , i do n ' t need overpriced food , absurdly arrogant wait - staff who do n ' t recognize they work at a glorified diner , clumsy service , and management that does n ' t care .\n->", + "output": "{\"text\": \"with so many good restaurants on the uws , i do n ' t need overpriced food , absurdly arrogant wait - staff who do n ' t recognize they work at a glorified diner , clumsy service , and management that does n ' t care .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'wait - staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza here is delicious .\n->The pizza here is delicious .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The staff is also attentive and friendly .\n->The staff is also attentive and friendly .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n->", + "output": "{\"text\": \"i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop is in great physical conditions , no scratches or anything , but certain actions run slowly , specifically any file read or write , like copying a document , uploading an image , creating a new file , etc .\n->the laptop is in great physical conditions , no scratches or anything , but certain actions run slowly , specifically any file read or write , like copying a document , uploading an image , creating a new file , etc .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slowly', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is o.k. , but not any better than what you get at a good neighborhood restaurant .\n->The food is o.k. , but not any better than what you get at a good neighborhood restaurant .\n[{'aspect': 'food', 'opinion': 'o.k. ,', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not any better', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: baluchi ' s has solid food and a nice decor at reasonable prices .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbaluchi ' s has solid food and a nice decor at reasonable prices .\n->", + "output": "{\"text\": \"baluchi ' s has solid food and a nice decor at reasonable prices .\", \"labels\": \"[{'aspect': \"baluchi ' s\", 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sauces used are also not that exciting .\n->The sauces used are also not that exciting .\n[{'aspect': 'sauces', 'opinion': 'not that exciting', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Their calzones are horrific , bad , vomit-inducing , YUCK .\n->Their calzones are horrific , bad , vomit-inducing , YUCK .\n[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'vomit-inducing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'YUCK', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the only problem is that the manager is a complete incompetent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only problem is that the manager is a complete incompetent .\n->", + "output": "{\"text\": \"the only problem is that the manager is a complete incompetent .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was very good - prompt , attentive and non-intrusive .\n->Service was very good - prompt , attentive and non-intrusive .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: As we were sitting eating the subpar food the manager proceeded to berate a couple of his employees for putting out the wrong containers for condiments and explained to them how expensive these containers were .\n->As we were sitting eating the subpar food the manager proceeded to berate a couple of his employees for putting out the wrong containers for condiments and explained to them how expensive these containers were .\n[{'aspect': 'food', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'containers', 'opinion': 'expensive', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: he offers subpar service and has no personality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhe offers subpar service and has no personality .\n->", + "output": "{\"text\": \"he offers subpar service and has no personality .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The waiter was attentive .\n->The waiter was attentive .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n->they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: in fact , it appears he is going to go postal at any moment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin fact , it appears he is going to go postal at any moment .\n->", + "output": "{\"text\": \"in fact , it appears he is going to go postal at any moment .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we did n ' t look like the other patrons in there so unfortunately i think that may have been part of the problem .\n->we did n ' t look like the other patrons in there so unfortunately i think that may have been part of the problem .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: I have to say I have never had a disapointing meal here .\n->I have to say I have never had a disapointing meal here .\n[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: there is no excuse for such lousy service !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is no excuse for such lousy service !\n->", + "output": "{\"text\": \"there is no excuse for such lousy service !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n->While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n[{'aspect': 'room', 'opinion': 'not particularly comfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: below medium build quality\n->below medium build quality\n[{'aspect': 'build quality', 'opinion': 'below medium', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i have never before eaten 40 pieces of relatively good nigiri .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have never before eaten 40 pieces of relatively good nigiri .\n->", + "output": "{\"text\": \"i have never before eaten 40 pieces of relatively good nigiri .\", \"labels\": \"[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: honestly , i ' m debating returning this laptop .\n->honestly , i ' m debating returning this laptop .\n[{'aspect': 'laptop', 'opinion': 'debating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n->the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard response', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: you can do it here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can do it here .\n->", + "output": "{\"text\": \"you can do it here .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Moules were excellent , lobster ravioli was VERY salty !\n->Moules were excellent , lobster ravioli was VERY salty !\n[{'aspect': 'Moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Delicious crab cakes too .\n->Delicious crab cakes too .\n[{'aspect': 'crab cakes', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: $ 20 for all you can eat sushi can not be beaten .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n$ 20 for all you can eat sushi can not be beaten .\n->", + "output": "{\"text\": \"$ 20 for all you can eat sushi can not be beaten .\", \"labels\": \"[{'aspect': 'all you can eat sushi', 'opinion': 'beaten', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'back room', 'opinion': 'secret', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Been going here since it opened have seen the quality value decrease considerably .\n->Been going here since it opened have seen the quality value decrease considerably .\n[{'aspect': 'quality value', 'opinion': 'decrease', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i went to areo on a sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni went to areo on a sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n->", + "output": "{\"text\": \"i went to areo on a sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\", \"labels\": \"[{'aspect': 'areo', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is great for a lot of modern games .\n->this laptop is great for a lot of modern games .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the second the screen did not rotate .\n->the second the screen did not rotate .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: most of the servers are very attentive , friendly and quite attractive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmost of the servers are very attentive , friendly and quite attractive .\n->", + "output": "{\"text\": \"most of the servers are very attentive , friendly and quite attractive .\", \"labels\": \"[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no caps lock on the keyboard .\n->no caps lock on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the intel i3 processor simply flys .\n->the intel i3 processor simply flys .\n[{'aspect': 'intel i3 processor', 'opinion': 'flys', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: the vibe is very relaxed and cozy , service was great and the food was excellent !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe vibe is very relaxed and cozy , service was great and the food was excellent !\n->", + "output": "{\"text\": \"the vibe is very relaxed and cozy , service was great and the food was excellent !\", \"labels\": \"[{'aspect': 'vibe', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'vibe', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: extremely disappointed as this was a gift to my husband .\n->extremely disappointed as this was a gift to my husband .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I thought this place was totally overrated .\n->I thought this place was totally overrated .\n[{'aspect': 'place', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'NULL'}]\ntext: food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when i went .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when i went .\n->", + "output": "{\"text\": \"food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when i went .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'view of the new york city skiline', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve only utilized the 360 degree opening once and so far i like it .\n->i ' ve only utilized the 360 degree opening once and so far i like it .\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Only complaint would be that at an average cost of $ 12- $ 15 per meal , I 'd like not to have to worry about finding a seat !\n->Only complaint would be that at an average cost of $ 12- $ 15 per meal , I 'd like not to have to worry about finding a seat !\n[{'aspect': 'cost', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seat', 'opinion': 'not to have to worry', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i would highly recommand requesting a table by the window .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would highly recommand requesting a table by the window .\n->", + "output": "{\"text\": \"i would highly recommand requesting a table by the window .\", \"labels\": \"[{'aspect': 'table by the window', 'opinion': 'recommand', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Indoor was very cozy and cute .\n->Indoor was very cozy and cute .\n[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Zero ambiance to boot .\n->Zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'Zero', 'polarity': 'negative', 'category': 'NULL'}]\ntext: although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\n->", + "output": "{\"text\": \"although they do the typical what kind of water would you like questions the service was good and overall very relaxing to place to eat .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fast boot up ( 3 seconds )\n->- fast boot up ( 3 seconds )\n[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: - acceptable amount of flex\n->- acceptable amount of flex\n[{'aspect': 'flex', 'opinion': 'acceptable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: personal pans are the perfect size for those hungry nights .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npersonal pans are the perfect size for those hungry nights .\n->", + "output": "{\"text\": \"personal pans are the perfect size for those hungry nights .\", \"labels\": \"[{'aspect': 'personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great item !\n->great item !\n[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I recommend their Pad See Ew , Pork Chops or Tofu plates .\n->I recommend their Pad See Ew , Pork Chops or Tofu plates .\n[{'aspect': 'Pad See Ew', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pork Chops', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Tofu plates', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i heartily recommend .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni heartily recommend .\n->", + "output": "{\"text\": \"i heartily recommend .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n->with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n[{'aspect': 'machine', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: keyboard feels firm and no flex , screen is nice for the price range .\n->keyboard feels firm and no flex , screen is nice for the price range .\n[{'aspect': 'keyboard', 'opinion': 'firm', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\ntext: there is a downside if you ' re ordering in - - the delivery guys have major attitude .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is a downside if you ' re ordering in - - the delivery guys have major attitude .\n->", + "output": "{\"text\": \"there is a downside if you ' re ordering in - - the delivery guys have major attitude .\", \"labels\": \"[{'aspect': 'delivery guys', 'opinion': 'downside', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it so far very nice product and it do what it is set out to do\n->love it so far very nice product and it do what it is set out to do\n[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n->the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: never have i had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b / c i live in a walkup ) who always seem disappointed with their tips .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnever have i had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b / c i live in a walkup ) who always seem disappointed with their tips .\n->", + "output": "{\"text\": \"never have i had such dramatic delivery guys ( a lot of huffing and panting and muttering under breath b / c i live in a walkup ) who always seem disappointed with their tips .\", \"labels\": \"[{'aspect': 'delivery guys', 'opinion': 'dramatic', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Truly the mark of an attentive waiter .\n->Truly the mark of an attentive waiter .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: prices are in line .\n->prices are in line .\n[{'aspect': 'NULL', 'opinion': 'in line', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\ntext: love the scene first off - the place has a character and nice light to it . . very fortunate , location wise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the scene first off - the place has a character and nice light to it . . very fortunate , location wise .\n->", + "output": "{\"text\": \"love the scene first off - the place has a character and nice light to it . . very fortunate , location wise .\", \"labels\": \"[{'aspect': 'scene', 'opinion': 'love', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'location', 'opinion': 'fortunate', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n->i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: whether it ' s the parmesean porcini souffle or the lamb glazed with balsamic vinegar , you will surely be transported to northern italy with one bite .\n->whether it ' s the parmesean porcini souffle or the lamb glazed with balsamic vinegar , you will surely be transported to northern italy with one bite .\n[{'aspect': 'parmesean porcini souffle', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb glazed with balsamic vinegar', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the pizza was pretty good and huge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pizza was pretty good and huge .\n->", + "output": "{\"text\": \"the pizza was pretty good and huge .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use it mostly when traveling it works well for that purpose .\n->i use it mostly when traveling it works well for that purpose .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n->update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n[{'aspect': 'keyboard cover', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: the price very reasonable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe price very reasonable .\n->", + "output": "{\"text\": \"the price very reasonable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n->the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n[{'aspect': 'battery life', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'longevity', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: my dad says it works extremely well !\n->my dad says it works extremely well !\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: we were 4 and got the family size penne a la vodka which was tremendously gigantic portion . . . a bucket of food literally .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were 4 and got the family size penne a la vodka which was tremendously gigantic portion . . . a bucket of food literally .\n->", + "output": "{\"text\": \"we were 4 and got the family size penne a la vodka which was tremendously gigantic portion . . . a bucket of food literally .\", \"labels\": \"[{'aspect': 'penne a la vodka', 'opinion': 'gigantic', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but it lost the coil whine roulette - - badly .\n->but it lost the coil whine roulette - - badly .\n[{'aspect': 'NULL', 'opinion': 'badly', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it boots in seconds , and i get ~ 10 hours out of the battery .\n->it boots in seconds , and i get ~ 10 hours out of the battery .\n[{'aspect': 'boots', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n->", + "output": "{\"text\": \"i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'heaviness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good spreads , great beverage selections and bagels really tasty .\n->Good spreads , great beverage selections and bagels really tasty .\n[{'aspect': 'spreads', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beverage selections', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: nice screen , nice feel .\n->nice screen , nice feel .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: the pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne . . . got a little moody afterwards cause was stuffed . . . lol\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne . . . got a little moody afterwards cause was stuffed . . . lol\n->", + "output": "{\"text\": \"the pasta penne was pretty extra buttery , creamy which means a big task to diggest . . tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne . . . got a little moody afterwards cause was stuffed . . . lol\", \"labels\": \"[{'aspect': 'pasta penne', 'opinion': 'buttery', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pasta penne', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tasty dog !\n->tasty dog !\n[{'aspect': 'dog', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i never write on these sites but this restaurant is def worth commending !\n->i never write on these sites but this restaurant is def worth commending !\n[{'aspect': 'restaurant', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love it .\n->", + "output": "{\"text\": \"i love it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only wish the power button was somewhere else , its too easy to hit accidentally .\n->only wish the power button was somewhere else , its too easy to hit accidentally .\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\nExample:\ntext: Drinks way over priced .\n->Drinks way over priced .\n[{'aspect': 'Drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'over', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i plan on stopping by next week as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni plan on stopping by next week as well .\n->", + "output": "{\"text\": \"i plan on stopping by next week as well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to sum it up : service varies from good to mediorce , depending on which waiter you get ; generally it is just average ok .\n->to sum it up : service varies from good to mediorce , depending on which waiter you get ; generally it is just average ok .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'mediorce', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'average ok', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: had it for a week now and still finding things it can not do .\n->had it for a week now and still finding things it can not do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i found it on a cold night , the perfect spot to warm up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni found it on a cold night , the perfect spot to warm up .\n->", + "output": "{\"text\": \"i found it on a cold night , the perfect spot to warm up .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got this laptop 2 days ago and it says plugged in , not charged .\n->i got this laptop 2 days ago and it says plugged in , not charged .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n->it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'insane', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i recieved prompt service with a smile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recieved prompt service with a smile .\n->", + "output": "{\"text\": \"i recieved prompt service with a smile .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n->It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n[{'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n->the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n[{'aspect': 'meal', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'meal', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'restaurant', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: to me it exemplifies soho , cute , artsy , interesting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto me it exemplifies soho , cute , artsy , interesting .\n->", + "output": "{\"text\": \"to me it exemplifies soho , cute , artsy , interesting .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'artsy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n->it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n[{'aspect': 'vibe', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'french food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The wine list is extensive and impressive .\n->The wine list is extensive and impressive .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: definately check it out ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinately check it out ! ! !\n->", + "output": "{\"text\": \"definately check it out ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We walked in on a Wednesday night and were seated promptly .\n->We walked in on a Wednesday night and were seated promptly .\n[{'aspect': 'seated', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the cream cheeses are out of this world and i love that coffee ! !\n->the cream cheeses are out of this world and i love that coffee ! !\n[{'aspect': 'cream cheeses', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'coffee', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: this place blew me away . . . by far my new favorite restaurant on the uppereast side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place blew me away . . . by far my new favorite restaurant on the uppereast side .\n->", + "output": "{\"text\": \"this place blew me away . . . by far my new favorite restaurant on the uppereast side .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Raga 's is a romantic , cozy restaurant .\n->Raga 's is a romantic , cozy restaurant .\n[{'aspect': \"Raga 's\", 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Raga 's\", 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n->most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the wine list is extensive and impressive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wine list is extensive and impressive .\n->", + "output": "{\"text\": \"the wine list is extensive and impressive .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n->they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: the food is decent .\n->the food is decent .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: love the atmosphere - felt like i was in paris .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the atmosphere - felt like i was in paris .\n->", + "output": "{\"text\": \"love the atmosphere - felt like i was in paris .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , for using it as a tablet / computer with streaming , it seems to only work half the time .\n->however , for using it as a tablet / computer with streaming , it seems to only work half the time .\n[{'aspect': 'tablet / computer with streaming', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The steak was very fatty and the sauce was overpowering and not very tasty .\n->The steak was very fatty and the sauce was overpowering and not very tasty .\n[{'aspect': 'steak', 'opinion': 'fatty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'overpowering', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n->", + "output": "{\"text\": \"the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\", \"labels\": \"[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the ambience was nice , but service was n ' t so great .\n->the ambience was nice , but service was n ' t so great .\n[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': \"was n ' t so great\", 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - the build quality is great but not necessarily impressive .\n->- the build quality is great but not necessarily impressive .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'build quality', 'opinion': 'not necessarily impressive', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i have been coming here for years and have nothing but good things to say about the service and the great staff at la lanterna .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been coming here for years and have nothing but good things to say about the service and the great staff at la lanterna .\n->", + "output": "{\"text\": \"i have been coming here for years and have nothing but good things to say about the service and the great staff at la lanterna .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: support got quite unpleasant when i ask about replacement .\n->support got quite unpleasant when i ask about replacement .\n[{'aspect': 'support', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: The design and atmosphere is just as good .\n->The design and atmosphere is just as good .\n[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: over the years the host , vittorio , and his crew , have always treated me as family - - although with all the business this not - so - little gem does , it amazing he ' s even able to remember a consistent but not - so - frequent visitor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nover the years the host , vittorio , and his crew , have always treated me as family - - although with all the business this not - so - little gem does , it amazing he ' s even able to remember a consistent but not - so - frequent visitor .\n->", + "output": "{\"text\": \"over the years the host , vittorio , and his crew , have always treated me as family - - although with all the business this not - so - little gem does , it amazing he ' s even able to remember a consistent but not - so - frequent visitor .\", \"labels\": \"[{'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop itself seemed fine at first .\n->the laptop itself seemed fine at first .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' m loving this thing .\n->i ' m loving this thing .\n[{'aspect': 'NULL', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n->", + "output": "{\"text\": \"i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\", \"labels\": \"[{'aspect': 'dining garden', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'jazz bar', 'opinion': 'new', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'thin crust pizzas', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lasagna menu', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was boring and expensive .\n->The food was boring and expensive .\n[{'aspect': 'food', 'opinion': 'boring', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The atmosphere is much better than Sripraphai ( more modern and sleek ) .\n->The atmosphere is much better than Sripraphai ( more modern and sleek ) .\n[{'aspect': 'atmosphere', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love this place !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this place !\n->", + "output": "{\"text\": \"i love this place !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All of the pizzas are terrific and the price is even better !\n->All of the pizzas are terrific and the price is even better !\n[{'aspect': 'pizzas', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is a lot of fun with live entertainment and all kinds of disney type special effects .\n->it is a lot of fun with live entertainment and all kinds of disney type special effects .\n[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: keep up the good work guys !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeep up the good work guys !\n->", + "output": "{\"text\": \"keep up the good work guys !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: IT is the best deal in town for a Monday night dinner at a fine restaurant .\n->IT is the best deal in town for a Monday night dinner at a fine restaurant .\n[{'aspect': 'dinner', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this makes it uncomfortable holding it in tablet mode .\n->this makes it uncomfortable holding it in tablet mode .\n[{'aspect': 'tablet mode', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i have to say i have never had a disapointing meal here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have to say i have never had a disapointing meal here .\n->", + "output": "{\"text\": \"i have to say i have never had a disapointing meal here .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is pleasantly satisfying with the touch screen and foldability .\n->the screen is pleasantly satisfying with the touch screen and foldability .\n[{'aspect': 'screen', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Ambience is delightful , service impeccable .\n->Ambience is delightful , service impeccable .\n[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we could have made a meal of the yummy dumplings from the dumpling menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe could have made a meal of the yummy dumplings from the dumpling menu .\n->", + "output": "{\"text\": \"we could have made a meal of the yummy dumplings from the dumpling menu .\", \"labels\": \"[{'aspect': 'dumplings', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love the feel of a lighter os and can do many tasks using google / web based apps .\n->i love the feel of a lighter os and can do many tasks using google / web based apps .\n[{'aspect': 'os', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The pizza is overpriced and soggy .\n->The pizza is overpriced and soggy .\n[{'aspect': 'pizza', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: luckily we saved room for the bbq salmon , sea bass and crispy duck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nluckily we saved room for the bbq salmon , sea bass and crispy duck .\n->", + "output": "{\"text\": \"luckily we saved room for the bbq salmon , sea bass and crispy duck .\", \"labels\": \"[{'aspect': 'bbq salmon', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sea bass', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crispy duck', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n->a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n[{'aspect': 'server', 'opinion': 'enhanced', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Even though its good seafood , the prices are too high .\n->Even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love al di la\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove al di la\n->", + "output": "{\"text\": \"love al di la\", \"labels\": \"[{'aspect': 'al di la', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza here is consistently good .\n->Pizza here is consistently good .\n[{'aspect': 'Pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: authentic pakistani food .\n->authentic pakistani food .\n[{'aspect': 'pakistani food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i recommend this place to everyone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recommend this place to everyone .\n->", + "output": "{\"text\": \"i recommend this place to everyone .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wait staff is very friendly , if not overly efficient .\n->The wait staff is very friendly , if not overly efficient .\n[{'aspect': 'wait staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'not overly efficient', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i wish i could like this place more , and i wish someone would retrain the staff .\n->i wish i could like this place more , and i wish someone would retrain the staff .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: great food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food .\n->", + "output": "{\"text\": \"great food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen is nice .\n->screen is nice .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n->was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n[{'aspect': 'chef', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: one of my favorite places in brooklyn .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of my favorite places in brooklyn .\n->", + "output": "{\"text\": \"one of my favorite places in brooklyn .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do n ' t be fooled by crowds of people .\n->do n ' t be fooled by crowds of people .\n[{'aspect': 'NULL', 'opinion': 'fooled', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: also audio is pretty decent .\n->also audio is pretty decent .\n[{'aspect': 'audio', 'opinion': 'decent', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: the pastas are incredible , the risottos ( particularly the sepia ) are fantastic and the braised rabbit is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pastas are incredible , the risottos ( particularly the sepia ) are fantastic and the braised rabbit is amazing .\n->", + "output": "{\"text\": \"the pastas are incredible , the risottos ( particularly the sepia ) are fantastic and the braised rabbit is amazing .\", \"labels\": \"[{'aspect': 'pastas', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'risottos', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sepia', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'braised rabbit', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s one of the worst laptops i ' ve ever had .\n->it ' s one of the worst laptops i ' ve ever had .\n[{'aspect': 'laptops', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard is easy to use , and there is no external noise to contend with .\n->the keyboard is easy to use , and there is no external noise to contend with .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: overpriced and not tasty\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverpriced and not tasty\n->", + "output": "{\"text\": \"overpriced and not tasty\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'not tasty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My husband and I enjoy Sangria .\n->My husband and I enjoy Sangria .\n[{'aspect': 'Sangria', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i never write on these sites but this restaurant is def worth commending !\n->i never write on these sites but this restaurant is def worth commending !\n[{'aspect': 'restaurant', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it was totally overpriced - fish and chips was about $ 15 . . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was totally overpriced - fish and chips was about $ 15 . . . .\n->", + "output": "{\"text\": \"it was totally overpriced - fish and chips was about $ 15 . . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'fish and chips', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Tasty steak , pork loin , the works .\n->Tasty steak , pork loin , the works .\n[{'aspect': 'steak', 'opinion': 'Tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork loin', 'opinion': 'Tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I expected quite a bit more from such an expensive menu .\n->I expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: tasty dog !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntasty dog !\n->", + "output": "{\"text\": \"tasty dog !\", \"labels\": \"[{'aspect': 'dog', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little place has a cute interior decor and affordable city prices .\n->this little place has a cute interior decor and affordable city prices .\n[{'aspect': 'interior decor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'little', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: comes with hdd and this makes this laptop very slow\n->comes with hdd and this makes this laptop very slow\n[{'aspect': 'hdd', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: an awesome organic dog , and a conscious eco friendly establishment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nan awesome organic dog , and a conscious eco friendly establishment .\n->", + "output": "{\"text\": \"an awesome organic dog , and a conscious eco friendly establishment .\", \"labels\": \"[{'aspect': 'dog', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dog', 'opinion': 'organic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'establishment', 'opinion': 'eco friendly', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n->The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I liked the food at this quasi-thai restaurant .\n->I liked the food at this quasi-thai restaurant .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: one of the best hot dogs i have ever eaten .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of the best hot dogs i have ever eaten .\n->", + "output": "{\"text\": \"one of the best hot dogs i have ever eaten .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n->the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n[{'aspect': 'keyboard', 'opinion': 'large', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: I think I 've had some the best meals of my life at minnow .\n->I think I 've had some the best meals of my life at minnow .\n[{'aspect': 'meals', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ca n ' t wait to go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nca n ' t wait to go back .\n->", + "output": "{\"text\": \"ca n ' t wait to go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n->ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n->The last two times I ordered from here my food was soo spicy that I could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: but the best pork souvlaki i ever had is the main thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the best pork souvlaki i ever had is the main thing .\n->", + "output": "{\"text\": \"but the best pork souvlaki i ever had is the main thing .\", \"labels\": \"[{'aspect': 'pork souvlaki', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n->My suggestion is to eat family style because you 'll want to try the other dishes .\n[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the sangria was pretty tasty and good on a hot muggy day .\n->the sangria was pretty tasty and good on a hot muggy day .\n[{'aspect': 'sangria', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'sangria', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: run do n ' t walk .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nrun do n ' t walk .\n->", + "output": "{\"text\": \"run do n ' t walk .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never swaying , never a bad meal , never bad service . . .\n->never swaying , never a bad meal , never bad service . . .\n[{'aspect': 'meal', 'opinion': 'never a bad', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'never bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: you will find yourself returning quite often .\n->you will find yourself returning quite often .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: super yummy pizza !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuper yummy pizza !\n->", + "output": "{\"text\": \"super yummy pizza !\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n->everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: great price - i always buy the warranty .\n->great price - i always buy the warranty .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'warranty', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}]\ntext: i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n->", + "output": "{\"text\": \"i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'inviting', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n->While I quite liked the food and the ambience , I 'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this little chromebook is amazing .\n->this little chromebook is amazing .\n[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i looove their eggplant pizza , as well as their pastas !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni looove their eggplant pizza , as well as their pastas !\n->", + "output": "{\"text\": \"i looove their eggplant pizza , as well as their pastas !\", \"labels\": \"[{'aspect': 'eggplant pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing that my friend left out is that when we sat down at the bar the bartender disappeared .\n->the only thing that my friend left out is that when we sat down at the bar the bartender disappeared .\n[{'aspect': 'bartender', 'opinion': 'disappeared', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: everything about the experience has been terrible .\n->everything about the experience has been terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: we had half / half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe had half / half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !\n->", + "output": "{\"text\": \"we had half / half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !\", \"labels\": \"[{'aspect': 'half / half pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is one of the best purchases i have made in years .\n->this is one of the best purchases i have made in years .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we were worried we would have trouble getting in , but somehow managed to have a short wait .\n->we were worried we would have trouble getting in , but somehow managed to have a short wait .\n[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: we had fun eating in there , we were there like around 3 a . m . in the morning !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe had fun eating in there , we were there like around 3 a . m . in the morning !\n->", + "output": "{\"text\": \"we had fun eating in there , we were there like around 3 a . m . in the morning !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: moderate prices .\n->moderate prices .\n[{'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: a few tips : skip the turnip cake , roast pork buns and egg custards .\n->a few tips : skip the turnip cake , roast pork buns and egg custards .\n[{'aspect': 'turnip cake', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'roast pork buns', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'egg custards', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: excellent food , although the interior could use some help .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent food , although the interior could use some help .\n->", + "output": "{\"text\": \"excellent food , although the interior could use some help .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'interior', 'opinion': 'help', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the hot dogs too , they 're snappy and delicious .\n->Try the hot dogs too , they 're snappy and delicious .\n[{'aspect': 'hot dogs', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hot dogs', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hot dogs', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n->kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: great sake !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat sake !\n->", + "output": "{\"text\": \"great sake !\", \"labels\": \"[{'aspect': 'sake', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tasty dog !\n->tasty dog !\n[{'aspect': 'dog', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: wonderful machine fast , clean , solid i have to said that this guy i fell from my hands the first day i use , goes to the floor and nothing happens screen is perfect and nothing is damage !\n->wonderful machine fast , clean , solid i have to said that this guy i fell from my hands the first day i use , goes to the floor and nothing happens screen is perfect and nothing is damage !\n[{'aspect': 'machine', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'machine', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: reliable , fresh sushi\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreliable , fresh sushi\n->", + "output": "{\"text\": \"reliable , fresh sushi\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n->what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n[{'aspect': 'NULL', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fail', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this chromebook is awesome .\n->this chromebook is awesome .\n[{'aspect': 'chromebook', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the sashimi is always fresh and the rolls are innovative and delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sashimi is always fresh and the rolls are innovative and delicious .\n->", + "output": "{\"text\": \"the sashimi is always fresh and the rolls are innovative and delicious .\", \"labels\": \"[{'aspect': 'sashimi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best of all is the warm vibe , the owner is super friendly and service is fast .\n->Best of all is the warm vibe , the owner is super friendly and service is fast .\n[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it works like it is made to run linux .\n->it works like it is made to run linux .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: have never had a problem with service save a missing rice once .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave never had a problem with service save a missing rice once .\n->", + "output": "{\"text\": \"have never had a problem with service save a missing rice once .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'problem', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n->I would recommend putting your name down and then getting a drink at a local bar first though because of the wait time .\n[{'aspect': 'drink', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: easiest computer to start up ever .\n->easiest computer to start up ever .\n[{'aspect': 'computer', 'opinion': 'easiest', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'start up', 'opinion': 'easiest', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: delivery can be spot on or lacking depending on the weather and the day of the week .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelivery can be spot on or lacking depending on the weather and the day of the week .\n->", + "output": "{\"text\": \"delivery can be spot on or lacking depending on the weather and the day of the week .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .\n->We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the portions are huge , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n->the portions are huge , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n[{'aspect': 'portions', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: delivery guy sometimes get upset if you do n ' t tip more than 10 % .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelivery guy sometimes get upset if you do n ' t tip more than 10 % .\n->", + "output": "{\"text\": \"delivery guy sometimes get upset if you do n ' t tip more than 10 % .\", \"labels\": \"[{'aspect': 'delivery guy', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is one of the best purchases i have made in years .\n->this is one of the best purchases i have made in years .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it does indeed look excellent !\n->it does indeed look excellent !\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: best . sushi . ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest . sushi . ever .\n->", + "output": "{\"text\": \"best . sushi . ever .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the restaurant looks out over beautiful green lawns to the hudson river and the statue of liberty .\n->the restaurant looks out over beautiful green lawns to the hudson river and the statue of liberty .\n[{'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\nExample:\ntext: the keyboard is small , a little weird and takes some getting used to .\n->the keyboard is small , a little weird and takes some getting used to .\n[{'aspect': 'keyboard', 'opinion': 'small', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'weird', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: this place has ruined me for neighborhood sushi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place has ruined me for neighborhood sushi .\n->", + "output": "{\"text\": \"this place has ruined me for neighborhood sushi .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'ruined', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s pretty fast even with heavy use and multiple applications running at once .\n->it ' s pretty fast even with heavy use and multiple applications running at once .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Metrazur has a beautiful spot overlooking the main terminal .\n->Metrazur has a beautiful spot overlooking the main terminal .\n[{'aspect': 'spot', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: creative , consistent , fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncreative , consistent , fresh .\n->", + "output": "{\"text\": \"creative , consistent , fresh .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'consistent', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend this beautiful place .\n->i highly recommend this beautiful place .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: you would think they would make up for it with service , sadly , no .\n->you would think they would make up for it with service , sadly , no .\n[{'aspect': 'service', 'opinion': 'sadly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: excellent sashimi , and the millennium roll is beyond delicious .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent sashimi , and the millennium roll is beyond delicious .\n->", + "output": "{\"text\": \"excellent sashimi , and the millennium roll is beyond delicious .\", \"labels\": \"[{'aspect': 'sashimi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'millennium roll', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n->i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n[{'aspect': 'keyboards', 'opinion': 'worst', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: Ingredients are organic which is a real plus for me .\n->Ingredients are organic which is a real plus for me .\n[{'aspect': 'Ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\ntext: not cheap but very yummy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot cheap but very yummy .\n->", + "output": "{\"text\": \"not cheap but very yummy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not cheap', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything we had was good or ok . . . . but definitely nothing great .\n->everything we had was good or ok . . . . but definitely nothing great .\n[{'aspect': 'NULL', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it was nice when it was working .\n->it was nice when it was working .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: very , very nice\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery , very nice\n->", + "output": "{\"text\": \"very , very nice\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked great .\n->it worked great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: do n ' t buy this laptop or brand .\n->do n ' t buy this laptop or brand .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'brand', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: not only is the food\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only is the food\n->", + "output": "{\"text\": \"not only is the food\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their calzones are horrific , bad , vomit-inducing , YUCK .\n->Their calzones are horrific , bad , vomit-inducing , YUCK .\n[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'vomit-inducing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'YUCK', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is a great device if your main goal is to check email , surf the internet listen to music or watch videos .\n->this is a great device if your main goal is to check email , surf the internet listen to music or watch videos .\n[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: loved it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nloved it\n->", + "output": "{\"text\": \"loved it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent for those uses .\n->excellent for those uses .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n->The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n[{'aspect': 'mussaman curry', 'opinion': 'thin', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fried tofu', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato', 'opinion': 'poorly cooked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: we had a very nice time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe had a very nice time .\n->", + "output": "{\"text\": \"we had a very nice time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never has it run out of power while on battery .\n->never has it run out of power while on battery .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the acer is similar but bigger and heavier .\n->the acer is similar but bigger and heavier .\n[{'aspect': 'acer', 'opinion': 'bigger', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'acer', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the waiter was attentive , the food was delicious and the views of the city were great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waiter was attentive , the food was delicious and the views of the city were great .\n->", + "output": "{\"text\": \"the waiter was attentive , the food was delicious and the views of the city were great .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'views of the city', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , it ' s a great machine .\n->overall , it ' s a great machine .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: What is even better , is that the prices are very affordable as well , and the food is really good .\n->What is even better , is that the prices are very affordable as well , and the food is really good .\n[{'aspect': 'prices', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' d definitely go back again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' d definitely go back again .\n->", + "output": "{\"text\": \"i ' d definitely go back again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: possibly the most romantic restaurant in the city\n->possibly the most romantic restaurant in the city\n[{'aspect': 'restaurant', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: nice little computer for the price .\n->nice little computer for the price .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: great place to relax and enjoy your dinner\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat place to relax and enjoy your dinner\n->", + "output": "{\"text\": \"great place to relax and enjoy your dinner\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen quality is excellent , and i am fussy due to my interest in digital imagery .\n->the screen quality is excellent , and i am fussy due to my interest in digital imagery .\n[{'aspect': 'screen quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': \"chef ' s tasting menu\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: there is something about their atmosphere that makes me come back nearly every week .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is something about their atmosphere that makes me come back nearly every week .\n->", + "output": "{\"text\": \"there is something about their atmosphere that makes me come back nearly every week .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n->you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it ' s an easy one for me : the 302 offers a better experience overall and it ' s not close .\n->it ' s an easy one for me : the 302 offers a better experience overall and it ' s not close .\n[{'aspect': '302', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': '302', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: place is open till late , no dress code .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplace is open till late , no dress code .\n->", + "output": "{\"text\": \"place is open till late , no dress code .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n->I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n[{'aspect': 'upstairs', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and how many times can you pick up the same perfectly aligned set of napkins , inspect them vapidly and plonk them down in exactly the same place instead of venturing a glance at people who are there to help you make the rent ?\n->and how many times can you pick up the same perfectly aligned set of napkins , inspect them vapidly and plonk them down in exactly the same place instead of venturing a glance at people who are there to help you make the rent ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: good food : my favorite is the seafood spaghetti .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood food : my favorite is the seafood spaghetti .\n->", + "output": "{\"text\": \"good food : my favorite is the seafood spaghetti .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood spaghetti', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall this chromebook worked well and was reliable .\n->overall this chromebook worked well and was reliable .\n[{'aspect': 'chromebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: after reading a lot of the reviews on here , i was unsure about laptop .\n->after reading a lot of the reviews on here , i was unsure about laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: excellent food for great prices\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent food for great prices\n->", + "output": "{\"text\": \"excellent food for great prices\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try sushimi cucumber roll .\n->Try sushimi cucumber roll .\n[{'aspect': 'sushimi cucumber roll', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is not a great build .\n->it is not a great build .\n[{'aspect': 'NULL', 'opinion': 'not a great', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: my husband and i have been sold on this from the first visit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy husband and i have been sold on this from the first visit .\n->", + "output": "{\"text\": \"my husband and i have been sold on this from the first visit .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n->For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n->As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n[{'aspect': 'Lucky Strike', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the wait staff is very courteous and accomodating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wait staff is very courteous and accomodating .\n->", + "output": "{\"text\": \"the wait staff is very courteous and accomodating .\", \"labels\": \"[{'aspect': 'wait staff', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am so coming back here again , as much as i can .\n->i am so coming back here again , as much as i can .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: My fav was the sassy lassi ...\n->My fav was the sassy lassi ...\n[{'aspect': 'sassy lassi', 'opinion': 'fav', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n->", + "output": "{\"text\": \"the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\", \"labels\": \"[{'aspect': 'space', 'opinion': 'limited', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'indo - chinese food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is the best chromebook that i have ever used .\n->it is the best chromebook that i have ever used .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i returned it twice .\n->i returned it twice .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: there are no negatives to speak of .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere are no negatives to speak of .\n->", + "output": "{\"text\": \"there are no negatives to speak of .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'no negatives', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I love and I know gourmet food by excellence !\n->I love and I know gourmet food by excellence !\n[{'aspect': 'gourmet food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'gourmet food', 'opinion': 'excellence', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The all-Italian staff is warm and engaging from the start .\n->The all-Italian staff is warm and engaging from the start .\n[{'aspect': 'staff', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'engaging', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the bestt !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bestt !\n->", + "output": "{\"text\": \"the bestt !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'bestt', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: somehow working the italian charm with constant mille grazie does not constitute proper service .\n->somehow working the italian charm with constant mille grazie does not constitute proper service .\n[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it was easy to set up .\n->it was easy to set up .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: my favorite place lol\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy favorite place lol\n->", + "output": "{\"text\": \"my favorite place lol\", \"labels\": \"[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have never before eaten 40 pieces of relatively good nigiri .\n->i have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: everything about the experience has been terrible .\n->everything about the experience has been terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: its alright\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits alright\n->", + "output": "{\"text\": \"its alright\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n->i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n[{'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'processor', 'opinion': 'faster', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'google play store', 'opinion': 'compatibility', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n->We took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: im not necessarily fanatical about this place , but it was a fun time for low pirces .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nim not necessarily fanatical about this place , but it was a fun time for low pirces .\n->", + "output": "{\"text\": \"im not necessarily fanatical about this place , but it was a fun time for low pirces .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'fanatical', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'low', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n->even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n->it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n[{'aspect': 'equipment', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'equipment', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: lobster was good , nothing spectacular .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlobster was good , nothing spectacular .\n->", + "output": "{\"text\": \"lobster was good , nothing spectacular .\", \"labels\": \"[{'aspect': 'lobster', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'nothing spectacular', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in contrast of that , this laptop ' s cpu is very powerful .\n->in contrast of that , this laptop ' s cpu is very powerful .\n[{'aspect': \"laptop ' s cpu\", 'opinion': 'powerful', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: this place is worth an one - hour drive .\n->this place is worth an one - hour drive .\n[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: its just a fun place to go , not a five star restaraunt .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits just a fun place to go , not a five star restaraunt .\n->", + "output": "{\"text\": \"its just a fun place to go , not a five star restaraunt .\", \"labels\": \"[{'aspect': 'restaraunt', 'opinion': 'five star', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n->The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n->We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n[{'aspect': 'dining', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: had no flavor and the staff is rude and not attentive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhad no flavor and the staff is rude and not attentive .\n->", + "output": "{\"text\": \"had no flavor and the staff is rude and not attentive .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'not attentive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n->On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i can ' t really testify to its battery - life as i have not used it to the point where the battery is totally dissipated .\n->i can ' t really testify to its battery - life as i have not used it to the point where the battery is totally dissipated .\n[{'aspect': 'battery - life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: would never go back\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwould never go back\n->", + "output": "{\"text\": \"would never go back\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Also , do n't plan on asking for your favorite roll , if it 's not on the menu , you ca n't have it .\n->Also , do n't plan on asking for your favorite roll , if it 's not on the menu , you ca n't have it .\n[{'aspect': 'roll', 'opinion': 'favorite', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n->kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n->", + "output": "{\"text\": \"i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n->while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n[{'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: everyone was smiling so that made me feel welcome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neveryone was smiling so that made me feel welcome .\n->", + "output": "{\"text\": \"everyone was smiling so that made me feel welcome .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'welcome', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great aesthetics .\n->great aesthetics .\n[{'aspect': 'aesthetics', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: I was very disappointed with this restaurant .\n->I was very disappointed with this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i ordered the vitello alla marsala and i was pretty impressed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ordered the vitello alla marsala and i was pretty impressed .\n->", + "output": "{\"text\": \"i ordered the vitello alla marsala and i was pretty impressed .\", \"labels\": \"[{'aspect': 'vitello alla marsala', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n->Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n[{'aspect': 'fresh mozzerella slices', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozzerella slices', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Plain Cheese slice', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n->She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the veal and the mushrooms were cooked perfectly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe veal and the mushrooms were cooked perfectly .\n->", + "output": "{\"text\": \"the veal and the mushrooms were cooked perfectly .\", \"labels\": \"[{'aspect': 'veal', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mushrooms', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n->the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'halibut special', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'steak', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'top - notch', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n->Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n[{'aspect': 'meats', 'opinion': 'thin', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'various', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the potato balls were not dry at all . . . in fact it was buttery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe potato balls were not dry at all . . . in fact it was buttery .\n->", + "output": "{\"text\": \"the potato balls were not dry at all . . . in fact it was buttery .\", \"labels\": \"[{'aspect': 'potato balls', 'opinion': 'not dry', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'potato balls', 'opinion': 'buttery', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n->Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Quite frankly , this is some of the worst sushi I have ever tried .\n->Quite frankly , this is some of the worst sushi I have ever tried .\n[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the only downside . . . they only take cash which is ok if you know about it ahead of time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only downside . . . they only take cash which is ok if you know about it ahead of time .\n->", + "output": "{\"text\": \"the only downside . . . they only take cash which is ok if you know about it ahead of time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'downside', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: skip dessert .\n->skip dessert .\n[{'aspect': 'dessert', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: was very easy to add memory .\n->was very easy to add memory .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: i ' ll be back for sure .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ll be back for sure .\n->", + "output": "{\"text\": \"i ' ll be back for sure .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , i feel this is the best laptop i ' ve ever purchased or used .\n->overall , i feel this is the best laptop i ' ve ever purchased or used .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: my chow fun and chow see was really bland and oily .\n->my chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: worst place on smith street in brooklyn\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworst place on smith street in brooklyn\n->", + "output": "{\"text\": \"worst place on smith street in brooklyn\", \"labels\": \"[{'aspect': 'place', 'opinion': 'worst', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 5 pound laptop with its nine hour battery life .\n->5 pound laptop with its nine hour battery life .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Service was very good - prompt , attentive and non-intrusive .\n->Service was very good - prompt , attentive and non-intrusive .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .\n->", + "output": "{\"text\": \"very immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .\", \"labels\": \"[{'aspect': 'bartender', 'opinion': 'immature', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'slowwwww', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'not fresh or warm', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'waitresses', 'opinion': 'werent very attentive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they all know you .\n->they all know you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: after some googling i realized that the wifi issue was related to bluetooth being on .\n->after some googling i realized that the wifi issue was related to bluetooth being on .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: i would never recommend this place to anybody even for a casual dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would never recommend this place to anybody even for a casual dinner .\n->", + "output": "{\"text\": \"i would never recommend this place to anybody even for a casual dinner .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'never recommend', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'never recommend', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i especially loved the high ( er ) resolution display , compared to most other chromebooks .\n->i especially loved the high ( er ) resolution display , compared to most other chromebooks .\n[{'aspect': 'display', 'opinion': 'loved', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: the large screen and integral numeric pad are exactly what i need to easily process documents .\n->the large screen and integral numeric pad are exactly what i need to easily process documents .\n[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'integral numeric pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: awesome\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nawesome\n->", + "output": "{\"text\": \"awesome\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t know that i would want to use for work but it ' s perfect for personal use .\n->i don ' t know that i would want to use for work but it ' s perfect for personal use .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n->if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n[{'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the food is always fresh . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is always fresh . . .\n->", + "output": "{\"text\": \"the food is always fresh . . .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had been a regular due to the consistently good food and ease of getting a table .\n->I had been a regular due to the consistently good food and ease of getting a table .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'ease', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: overpriced japanese food with mediocre service\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverpriced japanese food with mediocre service\n->", + "output": "{\"text\": \"overpriced japanese food with mediocre service\", \"labels\": \"[{'aspect': 'japanese food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'service', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This quaint and romantic trattoria is at the top of my Manhattan restaurant list .\n->This quaint and romantic trattoria is at the top of my Manhattan restaurant list .\n[{'aspect': 'trattoria', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'trattoria', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: try the crunchy tuna , it is to die for .\n->try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: went here on sat 1 / 26 and was disappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwent here on sat 1 / 26 and was disappointed .\n->", + "output": "{\"text\": \"went here on sat 1 / 26 and was disappointed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it stutters on a 10 year old game .\n->it stutters on a 10 year old game .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: worst place on smith street in brooklyn\n->worst place on smith street in brooklyn\n[{'aspect': 'place', 'opinion': 'worst', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: food was luke warm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was luke warm .\n->", + "output": "{\"text\": \"food was luke warm .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'luke warm', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n->if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: do not buy this computer !\n->do not buy this computer !\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i was looking for banana tempura for dessert and they dont have .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was looking for banana tempura for dessert and they dont have .\n->", + "output": "{\"text\": \"i was looking for banana tempura for dessert and they dont have .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this asus chromebook in may of 2018 and initially loved it .\n->i purchased this asus chromebook in may of 2018 and initially loved it .\n[{'aspect': 'asus chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the problem with it is that it freezes from time to time .\n->the problem with it is that it freezes from time to time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: not because you are ` ` the four seasons ` ` . . . \u2013 you are allowed to charge an arm and a leg for a romatic dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot because you are ` ` the four seasons ` ` . . . \u2013 you are allowed to charge an arm and a leg for a romatic dinner .\n->", + "output": "{\"text\": \"not because you are ` ` the four seasons ` ` . . . \u2013 you are allowed to charge an arm and a leg for a romatic dinner .\", \"labels\": \"[{'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the overall unit has a slim profile , and is light weight .\n->the overall unit has a slim profile , and is light weight .\n[{'aspect': 'unit', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Everything looks great , the drinks , the decor , the food , even the people .\n->Everything looks great , the drinks , the decor , the food , even the people .\n[{'aspect': 'drinks', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food was excellent as well as service , however , i left the four seasons very dissappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was excellent as well as service , however , i left the four seasons very dissappointed .\n->", + "output": "{\"text\": \"the food was excellent as well as service , however , i left the four seasons very dissappointed .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'the four seasons', 'opinion': 'dissappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: too bad the food was n ' t of the same heritage .\n->too bad the food was n ' t of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: service not the friendliest to our ` ` large party ' ' !\n->service not the friendliest to our ` ` large party ' ' !\n[{'aspect': 'service', 'opinion': 'not the friendliest', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i do not think dinner in manhattan should cost $ 400 . 00 where i am not swept off my feet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do not think dinner in manhattan should cost $ 400 . 00 where i am not swept off my feet .\n->", + "output": "{\"text\": \"i do not think dinner in manhattan should cost $ 400 . 00 where i am not swept off my feet .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have the iced tea .\n->have the iced tea .\n[{'aspect': 'iced tea', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n->i really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n[{'aspect': 'scallops', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mahi mahi ( on saffron risotto', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: red dragon roll - my favorite thing to eat , of any food group - hands down\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nred dragon roll - my favorite thing to eat , of any food group - hands down\n->", + "output": "{\"text\": \"red dragon roll - my favorite thing to eat , of any food group - hands down\", \"labels\": \"[{'aspect': 'red dragon roll', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - port minimalism .\n->- port minimalism .\n[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#PORTABILITY'}]\nExample:\ntext: the owner truly caters to all your needs .\n->the owner truly caters to all your needs .\n[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: if you do n ' t like it , i do n ' t know what to tell you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you do n ' t like it , i do n ' t know what to tell you .\n->", + "output": "{\"text\": \"if you do n ' t like it , i do n ' t know what to tell you .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it holds up well .\n->it holds up well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: elegant design combined good performance , this laptop is almost flawless .\n->elegant design combined good performance , this laptop is almost flawless .\n[{'aspect': 'design', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'this laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the seafood dynamite is also otherworldly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe seafood dynamite is also otherworldly .\n->", + "output": "{\"text\": \"the seafood dynamite is also otherworldly .\", \"labels\": \"[{'aspect': 'seafood dynamite', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chennai garden is my favorite indian restaurant in the city .\n->chennai garden is my favorite indian restaurant in the city .\n[{'aspect': 'chennai garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n->Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n[{'aspect': 'waiters', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'busy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m not even going to bother to describe it ; it speaks for itself .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m not even going to bother to describe it ; it speaks for itself .\n->", + "output": "{\"text\": \"i ' m not even going to bother to describe it ; it speaks for itself .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard lighting is primitive and keeps shutting off .\n->keyboard lighting is primitive and keeps shutting off .\n[{'aspect': 'keyboard lighting', 'opinion': 'primitive', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: I would highly recommend requesting a table by the window .\n->I would highly recommend requesting a table by the window .\n[{'aspect': 'table by the window', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nan unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n->", + "output": "{\"text\": \"an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'unpretentious', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'effective', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love yuka .\n->love yuka .\n[{'aspect': 'yuka', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: very poor battery life .\n->very poor battery life .\n[{'aspect': 'battery life', 'opinion': 'poor', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: in the summer months , the back garden area is really nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin the summer months , the back garden area is really nice .\n->", + "output": "{\"text\": \"in the summer months , the back garden area is really nice .\", \"labels\": \"[{'aspect': 'back garden area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n->great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: eating in , the atmosphere saves it , but at your desk , it ' s a very disappointing experience .\n->eating in , the atmosphere saves it , but at your desk , it ' s a very disappointing experience .\n[{'aspect': 'atmosphere', 'opinion': 'saves', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the dancing , white river and millenium rolls are musts .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dancing , white river and millenium rolls are musts .\n->", + "output": "{\"text\": \"the dancing , white river and millenium rolls are musts .\", \"labels\": \"[{'aspect': 'dancing , white river and millenium rolls', 'opinion': 'musts', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price you can ' t beat a chromebook .\n->for the price you can ' t beat a chromebook .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n->Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n[{'aspect': 'waitstaff', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so good\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso good\n->", + "output": "{\"text\": \"so good\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n->There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n[{'aspect': 'table', 'opinion': 'long wait', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'insde table', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n->As a retired hipster , I can say with some degree of certainty that for the last year Lucky Strike has been the best laid-back late night in the city .\n[{'aspect': 'Lucky Strike', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i can eat here every day of the week really lol love this place . . . )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can eat here every day of the week really lol love this place . . . )\n->", + "output": "{\"text\": \"i can eat here every day of the week really lol love this place . . . )\", \"labels\": \"[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recommend this place to everyone .\n->i recommend this place to everyone .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the support website is incompetent .\n->the support website is incompetent .\n[{'aspect': 'support website', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: gross food \u2013 wow -\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngross food \u2013 wow -\n->", + "output": "{\"text\": \"gross food \u2013 wow -\", \"labels\": \"[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n->The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n[{'aspect': 'bhelpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sevpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'samosa chaats', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bombay style chaat', 'opinion': 'famous scrumptious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i found it on a cold night , the perfect spot to warm up .\n->i found it on a cold night , the perfect spot to warm up .\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i ca n ' t remember the last time i had such gross food in new york .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ca n ' t remember the last time i had such gross food in new york .\n->", + "output": "{\"text\": \"i ca n ' t remember the last time i had such gross food in new york .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only draw back with this pc is the battery life which lasts about 3 hrs before needing to be charged .\n->the only draw back with this pc is the battery life which lasts about 3 hrs before needing to be charged .\n[{'aspect': 'battery life', 'opinion': 'draw back', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the build - quality is pretty good .\n->the build - quality is pretty good .\n[{'aspect': 'build - quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: and $ 11 for a plate of bland guacamole ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand $ 11 for a plate of bland guacamole ?\n->", + "output": "{\"text\": \"and $ 11 for a plate of bland guacamole ?\", \"labels\": \"[{'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A narrow corridor leads to a tiny space where there are three tiny white tiled counters , a great deal of mess ( stacks of bottles , cans ) and a small counter holding 12-14 entrees .\n->A narrow corridor leads to a tiny space where there are three tiny white tiled counters , a great deal of mess ( stacks of bottles , cans ) and a small counter holding 12-14 entrees .\n[{'aspect': 'corridor', 'opinion': 'narrow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'space', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'counters', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the side mounted speakers are clear .\n->the side mounted speakers are clear .\n[{'aspect': 'side mounted speakers', 'opinion': 'clear', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: do n ' t get me started on the margaritas , either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo n ' t get me started on the margaritas , either .\n->", + "output": "{\"text\": \"do n ' t get me started on the margaritas , either .\", \"labels\": \"[{'aspect': 'margaritas', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is delicious .\n->The food is delicious .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n->it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: mine tasted like the bartender had forgotten to add the tequila .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmine tasted like the bartender had forgotten to add the tequila .\n->", + "output": "{\"text\": \"mine tasted like the bartender had forgotten to add the tequila .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For years , I thought Tuscan cuisine was the best , but Salvatore converted me to the hearty Neapolitan fare on my first visit .\n->For years , I thought Tuscan cuisine was the best , but Salvatore converted me to the hearty Neapolitan fare on my first visit .\n[{'aspect': 'Neapolitan fare', 'opinion': 'hearty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Amma is nothing special .\n->Amma is nothing special .\n[{'aspect': 'Amma', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: oh , and i never write reviews - - i just was so moved by how bad this place was , i felt it was my duty to spread the word .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noh , and i never write reviews - - i just was so moved by how bad this place was , i felt it was my duty to spread the word .\n->", + "output": "{\"text\": \"oh , and i never write reviews - - i just was so moved by how bad this place was , i felt it was my duty to spread the word .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'bad', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will not recommend it .\n->will not recommend it .\n[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the most recent incident is the sound wo n ' t work .\n->the most recent incident is the sound wo n ' t work .\n[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: great indian food !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat indian food !\n->", + "output": "{\"text\": \"great indian food !\", \"labels\": \"[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We go on Mondays for the prix fixe and our experience with the food has been comparable to Blue Ribbon .\n->We go on Mondays for the prix fixe and our experience with the food has been comparable to Blue Ribbon .\n[{'aspect': 'food', 'opinion': 'comparable', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n->and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n[{'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'congee', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'noodles', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'rice dishes', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: when i got there the place was packed but they made sure to seat me quickly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i got there the place was packed but they made sure to seat me quickly .\n->", + "output": "{\"text\": \"when i got there the place was packed but they made sure to seat me quickly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Total hipster-wannabe attitude in an otherwise sweet spot .\n->Total hipster-wannabe attitude in an otherwise sweet spot .\n[{'aspect': 'spot', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i am fully satisfied with the purchase and communication with the seller was great .\n->i am fully satisfied with the purchase and communication with the seller was great .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'seller', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: the food was good , the place was clean and affordable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was good , the place was clean and affordable .\n->", + "output": "{\"text\": \"the food was good , the place was clean and affordable .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if something happens within 30 days i will return it but i will get another one .\n->if something happens within 30 days i will return it but i will get another one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The menu choices are similar but the taste lacked more flavor than it looked .\n->The menu choices are similar but the taste lacked more flavor than it looked .\n[{'aspect': 'taste', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu choices', 'opinion': 'similar', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i noticed alot of indian people eatting there which is a great sign for an indian place !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni noticed alot of indian people eatting there which is a great sign for an indian place !\n->", + "output": "{\"text\": \"i noticed alot of indian people eatting there which is a great sign for an indian place !\", \"labels\": \"[{'aspect': 'indian place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sandwiches are dry , tasteless and way overpriced .\n->The sandwiches are dry , tasteless and way overpriced .\n[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: sometimes had to do several times , but thought it might be an idiosyncrady of this model .\n->sometimes had to do several times , but thought it might be an idiosyncrady of this model .\n[{'aspect': 'model', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: this is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .\n->", + "output": "{\"text\": \"this is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had the Pad Thai and the noodles were sticky .\n->I had the Pad Thai and the noodles were sticky .\n[{'aspect': 'Pad Thai', 'opinion': 'sticky', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'noodles', 'opinion': 'sticky', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i found the food to be outstanding , particulary the salmon dish i had .\n->i found the food to be outstanding , particulary the salmon dish i had .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon dish', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the staff is very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe staff is very good .\n->", + "output": "{\"text\": \"the staff is very good .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it feels sturdy and reliable , both hardware and software - wise .\n->it feels sturdy and reliable , both hardware and software - wise .\n[{'aspect': 'hardware', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'hardware', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'software', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'software', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n->Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n[{'aspect': 'food', 'opinion': 'loving', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dining experiences', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love their drink menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove their drink menu .\n->", + "output": "{\"text\": \"love their drink menu .\", \"labels\": \"[{'aspect': 'drink menu', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will never buy an asus product again .\n->will never buy an asus product again .\n[{'aspect': 'asus product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food was so-so .\n->The food was so-so .\n[{'aspect': 'food', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i highly recommend this beautiful place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend this beautiful place .\n->", + "output": "{\"text\": \"i highly recommend this beautiful place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is awful .\n->the service is awful .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this laptop exceeds my expectations for a mid - price laptop .\n->this laptop exceeds my expectations for a mid - price laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: nice for one time special occasion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice for one time special occasion .\n->", + "output": "{\"text\": \"nice for one time special occasion .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n->original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n[{'aspect': 'screen', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: we had fun eating in there , we were there like around 3 a . m . in the morning !\n->we had fun eating in there , we were there like around 3 a . m . in the morning !\n[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: they honored reservation on sunday afternoon very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey honored reservation on sunday afternoon very well .\n->", + "output": "{\"text\": \"they honored reservation on sunday afternoon very well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everyone must come here at least once .\n->everyone must come here at least once .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: google is amazing .\n->google is amazing .\n[{'aspect': 'google', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n->", + "output": "{\"text\": \"we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\", \"labels\": \"[{'aspect': 'voss bottles of water', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for $ 389 on cyber monday 2017 .\n->i bought this for $ 389 on cyber monday 2017 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: good display\n->good display\n[{'aspect': 'display', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: food was ok .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was ok .\n->", + "output": "{\"text\": \"food was ok .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->we are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'two types of sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: located at the end of a magnificent block .\n->located at the end of a magnificent block .\n[{'aspect': 'NULL', 'opinion': 'magnificent', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: nice view of river and nyc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice view of river and nyc .\n->", + "output": "{\"text\": \"nice view of river and nyc .\", \"labels\": \"[{'aspect': 'view of river and nyc', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i ' m growing ever more disenchanted with the core m3 processing speed .\n->but i ' m growing ever more disenchanted with the core m3 processing speed .\n[{'aspect': 'core m3', 'opinion': 'disenchanted', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: with how slim this thing is i really don ' t see a need for the macbook air line .\n->with how slim this thing is i really don ' t see a need for the macbook air line .\n[{'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i come here enjoy very much with husband .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni come here enjoy very much with husband .\n->", + "output": "{\"text\": \"i come here enjoy very much with husband .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n->But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n[{'aspect': 'late night atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is a capable chromebook !\n->this is a capable chromebook !\n[{'aspect': 'chromebook', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: remind me of home .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nremind me of home .\n->", + "output": "{\"text\": \"remind me of home .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: big learning curve , would hate to see someone older try and figure it out .\n->big learning curve , would hate to see someone older try and figure it out .\n[{'aspect': 'NULL', 'opinion': 'hate', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: My wife and I always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n->My wife and I always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n[{'aspect': 'staff', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'young', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'not always well trained', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great survice\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat survice\n->", + "output": "{\"text\": \"great survice\", \"labels\": \"[{'aspect': 'survice', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n->stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n[{'aspect': 'stylus', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'stylus', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: it was easy to set up .\n->it was easy to set up .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: mmmmmmmmmmmmmmm so delicious\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmmmmmmmmmmmmmmm so delicious\n->", + "output": "{\"text\": \"mmmmmmmmmmmmmmm so delicious\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no gimmicks here - - the food speaks for itself in its freshness and preparation .\n->no gimmicks here - - the food speaks for itself in its freshness and preparation .\n[{'aspect': 'food', 'opinion': 'freshness', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'preparation', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: 8 + lbs , this one is right under 5 so it makes it nice and portable .\n->8 + lbs , this one is right under 5 so it makes it nice and portable .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: a beautifully designed dreamy egyptian restaurant that gets sceney at night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na beautifully designed dreamy egyptian restaurant that gets sceney at night .\n->", + "output": "{\"text\": \"a beautifully designed dreamy egyptian restaurant that gets sceney at night .\", \"labels\": \"[{'aspect': 'egyptian restaurant', 'opinion': 'dreamy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'egyptian restaurant', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were very pleasantly surprised .\n->we were very pleasantly surprised .\n[{'aspect': 'NULL', 'opinion': 'pleasantly surprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the macbook pro is everything i hoped for and more .\n->the macbook pro is everything i hoped for and more .\n[{'aspect': 'macbook pro', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: watch the talented belly dancers as you enjoy delicious baba ganoush that ' s more lemony than smoky .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwatch the talented belly dancers as you enjoy delicious baba ganoush that ' s more lemony than smoky .\n->", + "output": "{\"text\": \"watch the talented belly dancers as you enjoy delicious baba ganoush that ' s more lemony than smoky .\", \"labels\": \"[{'aspect': 'baba ganoush', 'opinion': 'enjoy delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'belly dancers', 'opinion': 'talented', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: My husband and I enjoy Sangria .\n->My husband and I enjoy Sangria .\n[{'aspect': 'Sangria', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: oh , and there ' s hookah .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noh , and there ' s hookah .\n->", + "output": "{\"text\": \"oh , and there ' s hookah .\", \"labels\": \"[{'aspect': 'hookah', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just love my asus chromebook , i take it everywhere .\n->i just love my asus chromebook , i take it everywhere .\n[{'aspect': 'asus chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the display is perfect for me , plenty of brightness and a decent resolution .\n->the display is perfect for me , plenty of brightness and a decent resolution .\n[{'aspect': 'display', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'brightness', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: raymond the bartender rocks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nraymond the bartender rocks !\n->", + "output": "{\"text\": \"raymond the bartender rocks !\", \"labels\": \"[{'aspect': 'raymond', 'opinion': 'rocks', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All of the pizzas are terrific and the price is even better !\n->All of the pizzas are terrific and the price is even better !\n[{'aspect': 'pizzas', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i hate the new keyboard the newer version comes with .\n->i hate the new keyboard the newer version comes with .\n[{'aspect': 'keyboard', 'opinion': 'hate', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: pacifico is a great place to casually hang out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npacifico is a great place to casually hang out .\n->", + "output": "{\"text\": \"pacifico is a great place to casually hang out .\", \"labels\": \"[{'aspect': 'pacifico', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I really recommend the very simple Unda ( Egg ) rolls .\n->I really recommend the very simple Unda ( Egg ) rolls .\n[{'aspect': 'Unda ( Egg ) rolls', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Unda ( Egg ) rolls', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it started to get slow a week ago .\n->it started to get slow a week ago .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the drinks are great , especially when made by raymond .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe drinks are great , especially when made by raymond .\n->", + "output": "{\"text\": \"the drinks are great , especially when made by raymond .\", \"labels\": \"[{'aspect': 'drinks', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'raymond', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cpu and ram were running low .\n->cpu and ram were running low .\n[{'aspect': 'cpu', 'opinion': 'low', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'ram', 'opinion': 'low', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: the dosas are skimpy , unattractive and drip with grease , and personally i ' d drink popcorn topping before i ' d eat another one of these .\n->the dosas are skimpy , unattractive and drip with grease , and personally i ' d drink popcorn topping before i ' d eat another one of these .\n[{'aspect': 'dosas', 'opinion': 'skimpy', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'dosas', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the omlette for brunch is great . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe omlette for brunch is great . . .\n->", + "output": "{\"text\": \"the omlette for brunch is great . . .\", \"labels\": \"[{'aspect': 'omlette for brunch', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love love love this laptop !\n->love love love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: speakers were messed up when turned on and the return did it still have n ' t processed even though it ' s been several weeks .\n->speakers were messed up when turned on and the return did it still have n ' t processed even though it ' s been several weeks .\n[{'aspect': 'speakers', 'opinion': 'messed up', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: quacamole at pacifico is yummy , as are the wings with chimmichuri .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nquacamole at pacifico is yummy , as are the wings with chimmichuri .\n->", + "output": "{\"text\": \"quacamole at pacifico is yummy , as are the wings with chimmichuri .\", \"labels\": \"[{'aspect': 'quacamole', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wings with chimmichuri', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Judging from previous posts this used to be a good place , but not any longer .\n->Judging from previous posts this used to be a good place , but not any longer .\n[{'aspect': 'place', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n->The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\ntext: a weakness is the chicken in the salads .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na weakness is the chicken in the salads .\n->", + "output": "{\"text\": \"a weakness is the chicken in the salads .\", \"labels\": \"[{'aspect': 'chicken in the salads', 'opinion': 'weakness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n->Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n[{'aspect': 'Asian appetizers', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: try their chef ' s specials - - they are to die for .\n->try their chef ' s specials - - they are to die for .\n[{'aspect': \"chef ' s specials\", 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it ' s just average , just shredded , no seasoning on it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s just average , just shredded , no seasoning on it .\n->", + "output": "{\"text\": \"it ' s just average , just shredded , no seasoning on it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no problems with this computer .\n->no problems with this computer .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - pretty loud speakers .\n->- pretty loud speakers .\n[{'aspect': 'speakers', 'opinion': 'loud', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: also , i personally was n ' t a fan of the portobello and asparagus mole .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , i personally was n ' t a fan of the portobello and asparagus mole .\n->", + "output": "{\"text\": \"also , i personally was n ' t a fan of the portobello and asparagus mole .\", \"labels\": \"[{'aspect': 'portobello and asparagus mole', 'opinion': 'fan', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: so i decide to report back to the waitress because it was completely inedible .\n->so i decide to report back to the waitress because it was completely inedible .\n[{'aspect': 'NULL', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: overall , decent food at a good price , with friendly people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , decent food at a good price , with friendly people .\n->", + "output": "{\"text\": \"overall , decent food at a good price , with friendly people .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will be going back and heartily recommend it !\n->i will be going back and heartily recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i ' m glad i got this .\n->i ' m glad i got this .\n[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: best indian restaurant in the city\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest indian restaurant in the city\n->", + "output": "{\"text\": \"best indian restaurant in the city\", \"labels\": \"[{'aspect': 'indian restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had to reset the computer multiple times .\n->i ' ve had to reset the computer multiple times .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: battery life is between 4 to 7 hours depending on what i ' m doing .\n->battery life is between 4 to 7 hours depending on what i ' m doing .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: this small astoria souvlaki spot makes what many consider the best gyros in new york .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis small astoria souvlaki spot makes what many consider the best gyros in new york .\n->", + "output": "{\"text\": \"this small astoria souvlaki spot makes what many consider the best gyros in new york .\", \"labels\": \"[{'aspect': 'gyros', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n->the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n[{'aspect': 'responses', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: Service was also horrible and the ambience is not that great .\n->Service was also horrible and the ambience is not that great .\n[{'aspect': 'Service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'NULL'}]\ntext: what really makes it shine is the food , which is aggressively seasoned with cyrpriot spices , and all made in - house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat really makes it shine is the food , which is aggressively seasoned with cyrpriot spices , and all made in - house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .\n->", + "output": "{\"text\": \"what really makes it shine is the food , which is aggressively seasoned with cyrpriot spices , and all made in - house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'shine', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'gyro meat', 'opinion': 'in - house', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sausages', 'opinion': 'in - house', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'higher quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen on this looks great , the bezels aren ' t noticeable .\n->the screen on this looks great , the bezels aren ' t noticeable .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': \"' t noticeable\", 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: as always we had a great glass of wine while we waited .\n->as always we had a great glass of wine while we waited .\n[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n->", + "output": "{\"text\": \"all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\", \"labels\": \"[{'aspect': 'greek and cypriot dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'gyro', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m glad i got this .\n->i ' m glad i got this .\n[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n->The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n[{'aspect': 'sauce', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck noodles', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: best restaurant in brooklyn\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest restaurant in brooklyn\n->", + "output": "{\"text\": \"best restaurant in brooklyn\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even using it a bit makes my hands / wrist uncomfortable .\n->even using it a bit makes my hands / wrist uncomfortable .\n[{'aspect': 'NULL', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: Baluchi 's has solid food and a nice decor at reasonable prices .\n->Baluchi 's has solid food and a nice decor at reasonable prices .\n[{'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the best !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe best !\n->", + "output": "{\"text\": \"the best !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: doing general internet surfing is a breeze on this laptop .\n->doing general internet surfing is a breeze on this laptop .\n[{'aspect': 'laptop', 'opinion': 'breeze', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is very overpriced and not very tasty .\n->it is very overpriced and not very tasty .\n[{'aspect': 'NULL', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: great food , amazing service , this place is a class act .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food , amazing service , this place is a class act .\n->", + "output": "{\"text\": \"great food , amazing service , this place is a class act .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'class act', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place itself is beautiful the bar scene seems to be happening .\n->The place itself is beautiful the bar scene seems to be happening .\n[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar scene', 'opinion': 'happening', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my only issue is the wifi likes to randomly turn off then back on .\n->my only issue is the wifi likes to randomly turn off then back on .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: the veal was incredible last night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe veal was incredible last night .\n->", + "output": "{\"text\": \"the veal was incredible last night .\", \"labels\": \"[{'aspect': 'veal', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was good not great not worth the wait or another visit\n->Food was good not great not worth the wait or another visit\n[{'aspect': 'Food', 'opinion': 'good not great not worth the wait or another visit', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: this place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .\n->this place is pricey , and yes , the food is worth it ; but the service makes you feel like you should be paying a quater of the price .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'the', 'opinion': 'is', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'the', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: fresh , mind blowing flavors .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfresh , mind blowing flavors .\n->", + "output": "{\"text\": \"fresh , mind blowing flavors .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'mind blowing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The exotic food is beautifully presented and is a delight in delicious combinations .\n->The exotic food is beautifully presented and is a delight in delicious combinations .\n[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i fixed it , however it took intensive research to fix it .\n->i fixed it , however it took intensive research to fix it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: this place is a must visit !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is a must visit !\n->", + "output": "{\"text\": \"this place is a must visit !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'must visit', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ca n ' t go wrong with an asus !\n->ca n ' t go wrong with an asus !\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: but after three charge cycles the screen started vibrating vigorously from side - to - side .\n->but after three charge cycles the screen started vibrating vigorously from side - to - side .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: delicious , creative and fun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelicious , creative and fun .\n->", + "output": "{\"text\": \"delicious , creative and fun .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sashimi is always fresh and the rolls are innovative and delicious .\n->the sashimi is always fresh and the rolls are innovative and delicious .\n[{'aspect': 'sashimi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: A must for all the Dosa lovers .\n->A must for all the Dosa lovers .\n[{'aspect': 'Dosa', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\ntext: got a date ? go here !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngot a date ? go here !\n->", + "output": "{\"text\": \"got a date ? go here !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: Try the crunchy tuna , it is to die for .\n->Try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\ntext: every time i have a special occasion with my boyfriend i have a hard time going anywhere else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nevery time i have a special occasion with my boyfriend i have a hard time going anywhere else .\n->", + "output": "{\"text\": \"every time i have a special occasion with my boyfriend i have a hard time going anywhere else .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish the camera was a little better , but it ' s great otherwise .\n->i wish the camera was a little better , but it ' s great otherwise .\n[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I like the ambience , it 's very dark and original .\n->I like the ambience , it 's very dark and original .\n[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is so romantic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is so romantic .\n->", + "output": "{\"text\": \"it is so romantic .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everyone seemed generally happy with their food , except my brother who had the grilled mahi mahi , seemingly drenched in grapfruit juice !\n->everyone seemed generally happy with their food , except my brother who had the grilled mahi mahi , seemingly drenched in grapfruit juice !\n[{'aspect': 'food', 'opinion': 'happy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled mahi mahi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled mahi mahi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n->wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: its dark , and cozy . . there is always jazz music playing when we go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits dark , and cozy . . there is always jazz music playing when we go .\n->", + "output": "{\"text\": \"its dark , and cozy . . there is always jazz music playing when we go .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cozy . .', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'dark', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we could have made a meal of the yummy dumplings from the dumpling menu .\n->we could have made a meal of the yummy dumplings from the dumpling menu .\n[{'aspect': 'dumplings', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: just one question , now i am running the os on the ssd and the original 1tb harddisk just for storing file , its boot up and start softwares very fast , but operating with file explore is very slow , i already set the file explore not open the quick access but it still slow , rename will effective after seconds , copy file and even right click sometimes need wait for minutes , who know how can i solve it ?\n->just one question , now i am running the os on the ssd and the original 1tb harddisk just for storing file , its boot up and start softwares very fast , but operating with file explore is very slow , i already set the file explore not open the quick access but it still slow , rename will effective after seconds , copy file and even right click sometimes need wait for minutes , who know how can i solve it ?\n[{'aspect': 'boot up', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: most of the booths allow you to sit next to eachother without looking like ' that ' couple .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmost of the booths allow you to sit next to eachother without looking like ' that ' couple .\n->", + "output": "{\"text\": \"most of the booths allow you to sit next to eachother without looking like ' that ' couple .\", \"labels\": \"[{'aspect': 'booths', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n->- i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n[{'aspect': 'chromebooks', 'opinion': 'worried', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: People are always friendly .\n->People are always friendly .\n[{'aspect': 'People', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food is all shared so we get to order together and eat together .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is all shared so we get to order together and eat together .\n->", + "output": "{\"text\": \"the food is all shared so we get to order together and eat together .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'shared', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they were very abrupt with me when i called and actually claimed the food was late because they were out of rice .\n->they were very abrupt with me when i called and actually claimed the food was late because they were out of rice .\n[{'aspect': 'NULL', 'opinion': 'abrupt', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: more williamsburg garbage\n->more williamsburg garbage\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: i ' ve enjoyed 99 % of the dishes we ' ve ordered with the only exceptions being the occasional too - authentic - for - me dish ( i ' m a daring eater but not that daring ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve enjoyed 99 % of the dishes we ' ve ordered with the only exceptions being the occasional too - authentic - for - me dish ( i ' m a daring eater but not that daring ) .\n->", + "output": "{\"text\": \"i ' ve enjoyed 99 % of the dishes we ' ve ordered with the only exceptions being the occasional too - authentic - for - me dish ( i ' m a daring eater but not that daring ) .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dish', 'opinion': 'too - authentic - for - me', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n->this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'computer replacement', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: good creative rolls !\n->good creative rolls !\n[{'aspect': 'rolls', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: once you try it for a special occasion beware . . you ca n ' t stop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonce you try it for a special occasion beware . . you ca n ' t stop !\n->", + "output": "{\"text\": \"once you try it for a special occasion beware . . you ca n ' t stop !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n->The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n[{'aspect': 'eggplant parmesan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'baked ziti with meatsauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: yes , this chromebook comes with the android app store pre - installed .\n->yes , this chromebook comes with the android app store pre - installed .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: you ' ll be there for every anniversary , birthday , valentines day . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou ' ll be there for every anniversary , birthday , valentines day . . .\n->", + "output": "{\"text\": \"you ' ll be there for every anniversary , birthday , valentines day . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am much more productive with this machine .\n->i am much more productive with this machine .\n[{'aspect': 'machine', 'opinion': 'productive', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: 5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n->5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n[{'aspect': 'emmc storage', 'opinion': 'slower', 'polarity': 'negative', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: for a fabulous wedding !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor a fabulous wedding !\n->", + "output": "{\"text\": \"for a fabulous wedding !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n->most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n[{'aspect': 'apps', 'opinion': 'ok', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: i packaged it right back up and sent it back the same day i got it .\n->i packaged it right back up and sent it back the same day i got it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neveryone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n->", + "output": "{\"text\": \"everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'raved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rooms', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'views', 'opinion': 'incomparable', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything was going good until we got our meals .\n->everything was going good until we got our meals .\n[{'aspect': 'meals', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The portions are small but being that the food was so good makes up for that .\n->The portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: service was wonderful ;\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was wonderful ;\n->", + "output": "{\"text\": \"service was wonderful ;\", \"labels\": \"[{'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not enough wines by the glass either .\n->Not enough wines by the glass either .\n[{'aspect': 'wines by the glass', 'opinion': 'Not enough', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the decor is night tho . . . but they really need to clean that vent in the ceiling . . . its quite un - appetizing , and kills your effort to make this place look sleek and modern .\n->the decor is night tho . . . but they really need to clean that vent in the ceiling . . . its quite un - appetizing , and kills your effort to make this place look sleek and modern .\n[{'aspect': 'place', 'opinion': 'sleek', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'modern', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'night', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'vent', 'opinion': 'un - appetizing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: everyone was cheerfully cooperative and helpful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neveryone was cheerfully cooperative and helpful .\n->", + "output": "{\"text\": \"everyone was cheerfully cooperative and helpful .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cheerfully cooperative', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n->first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'graphics', 'opinion': 'failed', 'polarity': 'negative', 'category': 'GRAPHICS#GENERAL'}]\nExample:\ntext: battery works well , i get nearly 4 to 5 hours easily .\n->battery works well , i get nearly 4 to 5 hours easily .\n[{'aspect': 'battery', 'opinion': 'well', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: paul , the maitre d ' , was totally professional and always on top of things .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npaul , the maitre d ' , was totally professional and always on top of things .\n->", + "output": "{\"text\": \"paul , the maitre d ' , was totally professional and always on top of things .\", \"labels\": \"[{'aspect': 'paul', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: track pad is a little spongy , but definitely not a showstopper .\n->track pad is a little spongy , but definitely not a showstopper .\n[{'aspect': 'track pad', 'opinion': 'spongy', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: keyboard feels good .\n->keyboard feels good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}]\ntext: thank you everyone at water ' s edge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthank you everyone at water ' s edge .\n->", + "output": "{\"text\": \"thank you everyone at water ' s edge .\", \"labels\": \"[{'aspect': \"water ' s edge\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The takeout is great too since they give high quality tupperware as well .\n->The takeout is great too since they give high quality tupperware as well .\n[{'aspect': 'takeout', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i don ' t know that i would want to use for work but it ' s perfect for personal use .\n->i don ' t know that i would want to use for work but it ' s perfect for personal use .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: would not go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwould not go back .\n->", + "output": "{\"text\": \"would not go back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the chromebook does not go to sleep or otherwise shut off .\n->and the chromebook does not go to sleep or otherwise shut off .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n->Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: service ok but unfriendly , filthy bathroom .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice ok but unfriendly , filthy bathroom .\n->", + "output": "{\"text\": \"service ok but unfriendly , filthy bathroom .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'bathroom', 'opinion': 'filthy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i did not find the battery to last a full ten hours .\n->i did not find the battery to last a full ten hours .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the bar drinks were eh , ok to say the least .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bar drinks were eh , ok to say the least .\n->", + "output": "{\"text\": \"the bar drinks were eh , ok to say the least .\", \"labels\": \"[{'aspect': 'bar drinks', 'opinion': 'ok', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n->i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n[{'aspect': 'asus support', 'opinion': 'sloth', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the temperatures were good , and the overall responsiveness of the system was fine .\n->the temperatures were good , and the overall responsiveness of the system was fine .\n[{'aspect': 'temperatures', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'responsiveness of the system', 'opinion': 'fine', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the stuff tilapia was horrid . . . tasted like cardboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe stuff tilapia was horrid . . . tasted like cardboard .\n->", + "output": "{\"text\": \"the stuff tilapia was horrid . . . tasted like cardboard .\", \"labels\": \"[{'aspect': 'stuff tilapia', 'opinion': 'horrid', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the setting is casual and romantic .\n->the setting is casual and romantic .\n[{'aspect': 'setting', 'opinion': 'casual', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'setting', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Good spreads , great beverage selections and bagels really tasty .\n->Good spreads , great beverage selections and bagels really tasty .\n[{'aspect': 'spreads', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beverage selections', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we thought the dessert would be better , wrong !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe thought the dessert would be better , wrong !\n->", + "output": "{\"text\": \"we thought the dessert would be better , wrong !\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen would freeze , requiring a hard shutdown .\n->the screen would freeze , requiring a hard shutdown .\n[{'aspect': 'screen', 'opinion': 'freeze', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i thought the gizmo was neat but i was disappointed that i couldn ' t really use it the way i wanted to it that regards .\n->i thought the gizmo was neat but i was disappointed that i couldn ' t really use it the way i wanted to it that regards .\n[{'aspect': 'gizmo', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: $ 170 down the toilet . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n$ 170 down the toilet . . .\n->", + "output": "{\"text\": \"$ 170 down the toilet . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am truly enjoying my laptop after one month .\n->i am truly enjoying my laptop after one month .\n[{'aspect': 'laptop', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n->for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: oh speaking of bathroom , the mens bathroom was disgusting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noh speaking of bathroom , the mens bathroom was disgusting .\n->", + "output": "{\"text\": \"oh speaking of bathroom , the mens bathroom was disgusting .\", \"labels\": \"[{'aspect': 'mens bathroom', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n->the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: Waitstaff are very friendly .\n->Waitstaff are very friendly .\n[{'aspect': 'Waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the floor was wet , the trash can filled with hand towels n all over the floor , no soap , and no hand towels left .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe floor was wet , the trash can filled with hand towels n all over the floor , no soap , and no hand towels left .\n->", + "output": "{\"text\": \"the floor was wet , the trash can filled with hand towels n all over the floor , no soap , and no hand towels left .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff there is very attentive and down to earth .\n->The staff there is very attentive and down to earth .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: A narrow corridor leads to a tiny space where there are three tiny white tiled counters , a great deal of mess ( stacks of bottles , cans ) and a small counter holding 12-14 entrees .\n->A narrow corridor leads to a tiny space where there are three tiny white tiled counters , a great deal of mess ( stacks of bottles , cans ) and a small counter holding 12-14 entrees .\n[{'aspect': 'corridor', 'opinion': 'narrow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'space', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'counters', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: good experience\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood experience\n->", + "output": "{\"text\": \"good experience\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the trackpad is awful as are most acer trackpads .\n->the trackpad is awful as are most acer trackpads .\n[{'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'acer trackpads', 'opinion': 'awful', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\n->Threw my fiance 's surprise 30th birthday dinner here could n't be happier .\n[{'aspect': 'dinner', 'opinion': \"could n't be happier\", 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n->", + "output": "{\"text\": \"the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\", \"labels\": \"[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'staff', 'opinion': 'not seem knowledgeable', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The combination of super-fresh ingredients in the dishes are unusual but really delicious .\n->The combination of super-fresh ingredients in the dishes are unusual but really delicious .\n[{'aspect': 'ingredients', 'opinion': 'super-fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .\n->very immature bartender , didnt know how to make specific drinks , service was so slowwwww , the food was not fresh or warm , waitresses were busy flirting with men at the bar and werent very attentive to all the customers .\n[{'aspect': 'bartender', 'opinion': 'immature', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'slowwwww', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'not fresh or warm', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'waitresses', 'opinion': 'werent very attentive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the bread we received was horrible - rock hard and cold - and the ` ` free ' ' appetizer of olives was disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bread we received was horrible - rock hard and cold - and the ` ` free ' ' appetizer of olives was disappointing .\n->", + "output": "{\"text\": \"the bread we received was horrible - rock hard and cold - and the ` ` free ' ' appetizer of olives was disappointing .\", \"labels\": \"[{'aspect': 'bread', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'bread', 'opinion': 'rock hard', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'bread', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'appetizer of olives', 'opinion': 'disappointing', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pasta penne was pretty extra buttery , creamy which means a big task to diggest.. tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne ... got a little moody afterwards cause was stuffed ... lol\n->The pasta penne was pretty extra buttery , creamy which means a big task to diggest.. tasty at first but i would say that i was full with a slice of pizza and 7 to count , penne ... got a little moody afterwards cause was stuffed ... lol\n[{'aspect': 'pasta penne', 'opinion': 'buttery', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the service is good and the resturant is clean .\n->the service is good and the resturant is clean .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'resturant', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: however , our main course was wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , our main course was wonderful .\n->", + "output": "{\"text\": \"however , our main course was wonderful .\", \"labels\": \"[{'aspect': 'main course', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is great , the computer is fast , and looks great with the aluminum case .\n->the screen is great , the computer is fast , and looks great with the aluminum case .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'aluminum case', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: fans can get loud .\n->fans can get loud .\n[{'aspect': 'fans', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: i had fish and my husband had the filet - both of which exceeded our expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had fish and my husband had the filet - both of which exceeded our expectations .\n->", + "output": "{\"text\": \"i had fish and my husband had the filet - both of which exceeded our expectations .\", \"labels\": \"[{'aspect': 'fish', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'filet', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Restaurant snobs need not bother , this is a small , neighborhood kind of place .\n->Restaurant snobs need not bother , this is a small , neighborhood kind of place .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n->i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n[{'aspect': 'keyboard', 'opinion': 'worried', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\ntext: the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n->", + "output": "{\"text\": \"the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\", \"labels\": \"[{'aspect': 'pear torte', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'unable to provide', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great place to relax and enjoy your dinner\n->great place to relax and enjoy your dinner\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the screen is nice .\n->the screen is nice .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: when we inquired about ports - the waitress listed off several but did not know taste variations or cost .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen we inquired about ports - the waitress listed off several but did not know taste variations or cost .\n->", + "output": "{\"text\": \"when we inquired about ports - the waitress listed off several but did not know taste variations or cost .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portion sizes here are huge , and the sushi is good .\n->The portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: they seemed to do nothing : fixing it was apparently my job .\n->they seemed to do nothing : fixing it was apparently my job .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: all in all , i would return - as it was a beautiful restaurant - but i hope the staff pays more attention to the little details in the future .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all , i would return - as it was a beautiful restaurant - but i hope the staff pays more attention to the little details in the future .\n->", + "output": "{\"text\": \"all in all , i would return - as it was a beautiful restaurant - but i hope the staff pays more attention to the little details in the future .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n->Food was good and the view of the new york city skiline was terrific even on a foggy rainy day like that of when I went .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'view of the new york city skiline', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what ' s not to like , it ' s an amazing machine .\n->what ' s not to like , it ' s an amazing machine .\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshort and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n->", + "output": "{\"text\": \"short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\", \"labels\": \"[{'aspect': 'seating', 'opinion': 'short', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'private', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the buffet had a nice selection .\n->the buffet had a nice selection .\n[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: Solid wine list , knowledgeable staff , friendly owners and an adventurous , ever-changing menu keep us coming back .\n->Solid wine list , knowledgeable staff , friendly owners and an adventurous , ever-changing menu keep us coming back .\n[{'aspect': 'wine list', 'opinion': 'Solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'knowledgeable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owners', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'adventurous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'ever-changing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n->", + "output": "{\"text\": \"the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\", \"labels\": \"[{'aspect': 'boths', 'opinion': 'not as small', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'boths', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n->maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n->the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n[{'aspect': 'NULL', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the service was extremely fast and attentive ( thanks to the service button on your table ) but i barely understood 1 word when the waiter took our order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service was extremely fast and attentive ( thanks to the service button on your table ) but i barely understood 1 word when the waiter took our order .\n->", + "output": "{\"text\": \"the service was extremely fast and attentive ( thanks to the service button on your table ) but i barely understood 1 word when the waiter took our order .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service button', 'opinion': 'thanks to', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mermaid inn is an overall good restaurant with really good seafood .\n->mermaid inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mermaid inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: over all the looks of the place exceeds the actual meals .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nover all the looks of the place exceeds the actual meals .\n->", + "output": "{\"text\": \"over all the looks of the place exceeds the actual meals .\", \"labels\": \"[{'aspect': 'looks', 'opinion': 'exceeds', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'meals', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: less wait time for me !\n->less wait time for me !\n[{'aspect': 'wait time', 'opinion': 'less', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: also , the sandwiches ( nearing $ 7 ) did n ' t come with anything like chips or a side .\n->also , the sandwiches ( nearing $ 7 ) did n ' t come with anything like chips or a side .\n[{'aspect': 'sandwiches', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'sandwiches', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: so what you really end up paying for is the restaurant not the food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso what you really end up paying for is the restaurant not the food .\n->", + "output": "{\"text\": \"so what you really end up paying for is the restaurant not the food .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first occasionally and later to a point where the laptop became unusable .\n->first occasionally and later to a point where the laptop became unusable .\n[{'aspect': 'laptop', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n->i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n[{'aspect': 'look', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'speed', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: will prob . not return but it is a great dinning experience to try at least once .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill prob . not return but it is a great dinning experience to try at least once .\n->", + "output": "{\"text\": \"will prob . not return but it is a great dinning experience to try at least once .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Spreads and toppings are great - though a bit pricey .\n->Spreads and toppings are great - though a bit pricey .\n[{'aspect': 'Spreads', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Spreads', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i waited for a while before writing a review about this product .\n->i waited for a while before writing a review about this product .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: noodle pudding is exactly the type of service and food i enjoy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnoodle pudding is exactly the type of service and food i enjoy .\n->", + "output": "{\"text\": \"noodle pudding is exactly the type of service and food i enjoy .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great little computer .\n->great little computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n->the place is a bistro which means : simple dishes and wine served efficiently in a bustling atmosphere .\n[{'aspect': 'dishes', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'atmosphere', 'opinion': 'bustling', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: servers are all different , greg is my favorite .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservers are all different , greg is my favorite .\n->", + "output": "{\"text\": \"servers are all different , greg is my favorite .\", \"labels\": \"[{'aspect': 'greg', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my understanding is that chromeos is a very light weight so i don ' t know why it lags sometimes with under 10 tabs open and a youtube video playing .\n->my understanding is that chromeos is a very light weight so i don ' t know why it lags sometimes with under 10 tabs open and a youtube video playing .\n[{'aspect': 'chromeos', 'opinion': 'light', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: they have it all - - great price , food , and service .\n->they have it all - - great price , food , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n->", + "output": "{\"text\": \"sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\", \"labels\": \"[{'aspect': 'server', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fantastic place .\n->Fantastic place .\n[{'aspect': 'place', 'opinion': 'Fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: A must for all the Dosa lovers .\n->A must for all the Dosa lovers .\n[{'aspect': 'Dosa', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n->", + "output": "{\"text\": \"this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\", \"labels\": \"[{'aspect': 'runner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a cool bar with great food , and tons of excellent beer .\n->a cool bar with great food , and tons of excellent beer .\n[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: great build materials and quality .\n->great build materials and quality .\n[{'aspect': 'build materials', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: prices are fair across the board for both food and bev .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprices are fair across the board for both food and bev .\n->", + "output": "{\"text\": \"prices are fair across the board for both food and bev .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fair', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bev', 'opinion': 'fair', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service varys from day to day - sometimes they ' re very nice , and sometimes not .\n->the service varys from day to day - sometimes they ' re very nice , and sometimes not .\n[{'aspect': 'service', 'opinion': 'varys', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: after reading a lot of the reviews on here , i was unsure about laptop .\n->after reading a lot of the reviews on here , i was unsure about laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: i go out to eat and like my courses , servers are patient and never rush courses or force another drink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni go out to eat and like my courses , servers are patient and never rush courses or force another drink .\n->", + "output": "{\"text\": \"i go out to eat and like my courses , servers are patient and never rush courses or force another drink .\", \"labels\": \"[{'aspect': 'servers', 'opinion': 'patient', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is my only device with this issue in my home .\n->it is my only device with this issue in my home .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - tablet - mode still needs some work .\n->- tablet - mode still needs some work .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: amazing fresh dogs but best of all endless toppings ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazing fresh dogs but best of all endless toppings ! ! !\n->", + "output": "{\"text\": \"amazing fresh dogs but best of all endless toppings ! ! !\", \"labels\": \"[{'aspect': 'dogs', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dogs', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'toppings', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'toppings', 'opinion': 'endless', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no thanks ! ! !\n->no thanks ! ! !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Most of the servers are very attentive , friendly and quite attractive .\n->Most of the servers are very attentive , friendly and quite attractive .\n[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this place had all the trimmings and i mean all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place had all the trimmings and i mean all .\n->", + "output": "{\"text\": \"this place had all the trimmings and i mean all .\", \"labels\": \"[{'aspect': 'trimmings', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: customer service is difficult .\n->customer service is difficult .\n[{'aspect': 'customer service', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\nExample:\ntext: From the terrible service , to the bland food , not to mention the unaccommodating managers , the overall experience was horrible .\n->From the terrible service , to the bland food , not to mention the unaccommodating managers , the overall experience was horrible .\n[{'aspect': 'service', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'managers', 'opinion': 'unaccommodating', 'polarity': 'negative', 'category': 'NULL'}]\ntext: peppers , onions , relish , chilli , cheeses , you name it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npeppers , onions , relish , chilli , cheeses , you name it .\n->", + "output": "{\"text\": \"peppers , onions , relish , chilli , cheeses , you name it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n->The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n[{'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: looking around , i saw a room full of new yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n->looking around , i saw a room full of new yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n[{'aspect': 'meal', 'opinion': 'real', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'restaurant', 'opinion': 'real', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: amazing fun for hot dog lovers of all ages please do yourself a favor and check this place out ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazing fun for hot dog lovers of all ages please do yourself a favor and check this place out ! ! ! !\n->", + "output": "{\"text\": \"amazing fun for hot dog lovers of all ages please do yourself a favor and check this place out ! ! ! !\", \"labels\": \"[{'aspect': 'hot dog', 'opinion': 'amazing fun', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has way more than i will ever need because all i do is check my email and facebook , but it is crazy fast .\n->it has way more than i will ever need because all i do is check my email and facebook , but it is crazy fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i just received this product about an hour or so ago .\n->i just received this product about an hour or so ago .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: best dining experience in the west village !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest dining experience in the west village !\n->", + "output": "{\"text\": \"best dining experience in the west village !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she loves it .\n->she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: an excellent service\n->an excellent service\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: stepping into casa la femme last night was a true experience unlike any other in new york !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstepping into casa la femme last night was a true experience unlike any other in new york !\n->", + "output": "{\"text\": \"stepping into casa la femme last night was a true experience unlike any other in new york !\", \"labels\": \"[{'aspect': 'casa la femme', 'opinion': 'true', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is always very crowded and popular .\n->This place is always very crowded and popular .\n[{'aspect': 'place', 'opinion': 'crowded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'popular', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I 'm still mad that i had to pay for lousy food .\n->I 'm still mad that i had to pay for lousy food .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: highly impressed from the decor to the food to the hospitality to the great night i had !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly impressed from the decor to the food to the hospitality to the great night i had !\n->", + "output": "{\"text\": \"highly impressed from the decor to the food to the hospitality to the great night i had !\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'hospitality', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->We , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: with chrome remote desktop i am able to play turn - based video games such as hearthstone or civ 5 while miles away from the computer actually running them .\n->with chrome remote desktop i am able to play turn - based video games such as hearthstone or civ 5 while miles away from the computer actually running them .\n[{'aspect': 'chrome remote desktop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: the have a great cocktail with citrus vodka and lemon and lime juice and mint leaves that is to die for !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe have a great cocktail with citrus vodka and lemon and lime juice and mint leaves that is to die for !\n->", + "output": "{\"text\": \"the have a great cocktail with citrus vodka and lemon and lime juice and mint leaves that is to die for !\", \"labels\": \"[{'aspect': 'cocktail with citrus vodka and lemon and lime juice and mint leaves', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: computer wo n ' t turn on have had it less then a year .\n->computer wo n ' t turn on have had it less then a year .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i work with an it company and we ' re testing an all android environment and it ' s working out pretty well so far .\n->i work with an it company and we ' re testing an all android environment and it ' s working out pretty well so far .\n[{'aspect': 'android environment', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: we were drawn into the belly dancing show that captivated the crowd .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were drawn into the belly dancing show that captivated the crowd .\n->", + "output": "{\"text\": \"we were drawn into the belly dancing show that captivated the crowd .\", \"labels\": \"[{'aspect': 'belly dancing show', 'opinion': 'captivated', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our experience did not ever get any better .\n->our experience did not ever get any better .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n->touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n[{'aspect': 'touchscreen', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'neat', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'awkward', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i never write on these sites but this restaurant is def worth commending !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni never write on these sites but this restaurant is def worth commending !\n->", + "output": "{\"text\": \"i never write on these sites but this restaurant is def worth commending !\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - backlit keyboard rocks\n->- backlit keyboard rocks\n[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: volume was not working .\n->volume was not working .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: maggot in the food !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmaggot in the food !\n->", + "output": "{\"text\": \"maggot in the food !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Be careful of portions - they 're HUGE .\n->Be careful of portions - they 're HUGE .\n[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: + nice , large screen\n->+ nice , large screen\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe menu looked great , and the waiter was very nice , but when the food came , it was average .\n->", + "output": "{\"text\": \"the menu looked great , and the waiter was very nice , but when the food came , it was average .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n->The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n[{'aspect': 'decor', 'opinion': 'diner-ish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'sparse', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i loved it and would go again .\n->i loved it and would go again .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: nevertheless , i finished my plate , and that ' s when i found a maggot in mushroom sauce at the bottom .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnevertheless , i finished my plate , and that ' s when i found a maggot in mushroom sauce at the bottom .\n->", + "output": "{\"text\": \"nevertheless , i finished my plate , and that ' s when i found a maggot in mushroom sauce at the bottom .\", \"labels\": \"[{'aspect': 'mushroom sauce', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the perfect spot .\n->the perfect spot .\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The noise level was unbearable , conversation impossible .\n->The noise level was unbearable , conversation impossible .\n[{'aspect': 'noise level', 'opinion': 'unbearable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i showed it to the manager , and he smilingly apologized and brought us two free desserts ( but did not ask us what we wanted and so brought the last two desserts we would have asked for ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni showed it to the manager , and he smilingly apologized and brought us two free desserts ( but did not ask us what we wanted and so brought the last two desserts we would have asked for ) .\n->", + "output": "{\"text\": \"i showed it to the manager , and he smilingly apologized and brought us two free desserts ( but did not ask us what we wanted and so brought the last two desserts we would have asked for ) .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n->the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n[{'aspect': 'build quality', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'back lit keys', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: the body of the chromebook feels solid due to the aluminium body .\n->the body of the chromebook feels solid due to the aluminium body .\n[{'aspect': 'body of the chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the manager finally said he would com # # p the two glasses of wine ( which cost less than the food ) , and made it seem like a big concession .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe manager finally said he would com # # p the two glasses of wine ( which cost less than the food ) , and made it seem like a big concession .\n->", + "output": "{\"text\": \"the manager finally said he would com # # p the two glasses of wine ( which cost less than the food ) , and made it seem like a big concession .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard and os takes some getting used to .\n->the keyboard and os takes some getting used to .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\nExample:\ntext: You can not go wrong with this place .\n->You can not go wrong with this place .\n[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we paid and left because we did n ' t feel like arguing any more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe paid and left because we did n ' t feel like arguing any more .\n->", + "output": "{\"text\": \"we paid and left because we did n ' t feel like arguing any more .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n->and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n[{'aspect': 'backlit keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: great service , great food .\n->great service , great food .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n->", + "output": "{\"text\": \"i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The hot dogs were cold in the middle and the buns were stale .\n->The hot dogs were cold in the middle and the buns were stale .\n[{'aspect': 'hot dogs', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'buns', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: will not be buying asus again\n->will not be buying asus again\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: for a restaurant with such a good reputation and that is usually so packed , there was no reason for such a lack of intelligent customer service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor a restaurant with such a good reputation and that is usually so packed , there was no reason for such a lack of intelligent customer service .\n->", + "output": "{\"text\": \"for a restaurant with such a good reputation and that is usually so packed , there was no reason for such a lack of intelligent customer service .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'good reputation', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'customer service', 'opinion': 'intelligent', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n->i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n[{'aspect': 'NULL', 'opinion': 'heaviness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it was terrible .\n->it was terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: unhygienic\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunhygienic\n->", + "output": "{\"text\": \"unhygienic\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'unhygienic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .\n->the large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .\n[{'aspect': 'bruschettas', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'paninis', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'tramezzinis', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n->With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait-staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': \"does n't care\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'diner', 'opinion': 'glorified', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i do not recommend .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do not recommend .\n->", + "output": "{\"text\": \"i do not recommend .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Also a little more expensive than your average bagel place .\n->Also a little more expensive than your average bagel place .\n[{'aspect': 'bagel', 'opinion': 'expensive', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the 15 ` ` screen is big and nice , this will make my examinations go much faster .\n->the 15 ` ` screen is big and nice , this will make my examinations go much faster .\n[{'aspect': 'NULL', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'faster', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: i got hair in my food 2 times of then !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got hair in my food 2 times of then !\n->", + "output": "{\"text\": \"i got hair in my food 2 times of then !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is ok .\n->the keyboard is ok .\n[{'aspect': 'keyboard', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: Food and service was okay .\n->Food and service was okay .\n[{'aspect': 'Food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: great place , great value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat place , great value .\n->", + "output": "{\"text\": \"great place , great value .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent food for great prices\n->excellent food for great prices\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: I had been a regular due to the consistently good food and ease of getting a table .\n->I had been a regular due to the consistently good food and ease of getting a table .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'ease', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the food is flavorful , plentiful and reasonably priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is flavorful , plentiful and reasonably priced .\n->", + "output": "{\"text\": \"the food is flavorful , plentiful and reasonably priced .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'flavorful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the best !\n->the best !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n->with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n[{'aspect': 'machine', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the atmosphere is relaxed and casual .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe atmosphere is relaxed and casual .\n->", + "output": "{\"text\": \"the atmosphere is relaxed and casual .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mouse area is responsive , and has a nice feel to it .\n->the mouse area is responsive , and has a nice feel to it .\n[{'aspect': 'mouse area', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n->first of all , this place is * not * romantic , as claimed by citysearch ' s editorial review .\n[{'aspect': 'place', 'opinion': '* not * romantic', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: it ' s a great place to order from or sit - in .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a great place to order from or sit - in .\n->", + "output": "{\"text\": \"it ' s a great place to order from or sit - in .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I ordered the smoked salmon and roe appetizer and it was off flavor .\n->I ordered the smoked salmon and roe appetizer and it was off flavor .\n[{'aspect': 'smoked salmon and roe appetizer', 'opinion': 'off flavor', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: awesome\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nawesome\n->", + "output": "{\"text\": \"awesome\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but seemed very poorly made for the money .\n->but seemed very poorly made for the money .\n[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: very happy with my purchase , fast delivery , package well .\n->very happy with my purchase , fast delivery , package well .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'package', 'opinion': 'well', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: inexpensive , unassuming , great time !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ninexpensive , unassuming , great time !\n->", + "output": "{\"text\": \"inexpensive , unassuming , great time !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'pepper', 'opinion': 'much', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: also the case design is sort of rounded at both sides - a minor issue but it makes the device pop open in my purse and detracts from the ` ` feel ` ` of the device by making it feel a lot thicker than it actually is .\n->also the case design is sort of rounded at both sides - a minor issue but it makes the device pop open in my purse and detracts from the ` ` feel ` ` of the device by making it feel a lot thicker than it actually is .\n[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: sushi experience was unbelievable with my fiance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsushi experience was unbelievable with my fiance .\n->", + "output": "{\"text\": \"sushi experience was unbelievable with my fiance .\", \"labels\": \"[{'aspect': 'sushi', 'opinion': 'unbelievable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I like the ambience , it 's very dark and original .\n->I like the ambience , it 's very dark and original .\n[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The sauces used are also not that exciting .\n->The sauces used are also not that exciting .\n[{'aspect': 'sauces', 'opinion': 'not that exciting', 'polarity': 'negative', 'category': 'NULL'}]\ntext: try it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntry it !\n->", + "output": "{\"text\": \"try it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n->BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n[{'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai spiced curry noodles with shrimp', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Not a great place for family or general dining .\n->Not a great place for family or general dining .\n[{'aspect': 'place', 'opinion': 'Not a great', 'polarity': 'negative', 'category': 'NULL'}]\ntext: very pleased\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery pleased\n->", + "output": "{\"text\": \"very pleased\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first msi and if it stays great i will be a returning customer .\n->this is my first msi and if it stays great i will be a returning customer .\n[{'aspect': 'msi', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: definitely recommend this chromebook , it ' s a beautiful machine .\n->definitely recommend this chromebook , it ' s a beautiful machine .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: good creative rolls !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood creative rolls !\n->", + "output": "{\"text\": \"good creative rolls !\", \"labels\": \"[{'aspect': 'rolls', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great laptop if you are willing to put in an ssd and reinstall windows .\n->great laptop if you are willing to put in an ssd and reinstall windows .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I have been to Roth 's twice and both times were very disappointing .\n->I have been to Roth 's twice and both times were very disappointing .\n[{'aspect': \"Roth 's\", 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\ntext: yamato is an excellent place to go if youre not into sashimi , or if you have friends who doesnt like sushi much .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyamato is an excellent place to go if youre not into sashimi , or if you have friends who doesnt like sushi much .\n->", + "output": "{\"text\": \"yamato is an excellent place to go if youre not into sashimi , or if you have friends who doesnt like sushi much .\", \"labels\": \"[{'aspect': 'yamato', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n->the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food , drinks and service are clearly among the best in the city .\n->The food , drinks and service are clearly among the best in the city .\n[{'aspect': 'food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they have great rolls , the triple color and norwegetan rolls , are awesome and filling .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey have great rolls , the triple color and norwegetan rolls , are awesome and filling .\n->", + "output": "{\"text\": \"they have great rolls , the triple color and norwegetan rolls , are awesome and filling .\", \"labels\": \"[{'aspect': 'rolls', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'triple color and norwegetan rolls', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'triple color and norwegetan rolls', 'opinion': 'filling', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what you are paying for is the environment and the name .\n->what you are paying for is the environment and the name .\n[{'aspect': 'environment', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: it is a very good laptop .\n->it is a very good laptop .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: one special roll and one regular roll is enough to fill you up , but save room for dessert !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none special roll and one regular roll is enough to fill you up , but save room for dessert !\n->", + "output": "{\"text\": \"one special roll and one regular roll is enough to fill you up , but save room for dessert !\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'save room', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'special roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'regular roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will update this post if anything changes from what i posted , after i get the correct memory installed .\n->i will update this post if anything changes from what i posted , after i get the correct memory installed .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: The freshest , best variety , and the fastest delivery .\n->The freshest , best variety , and the fastest delivery .\n[{'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they have a delicious banana chocolate dessert , as well as a great green tea tempura .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey have a delicious banana chocolate dessert , as well as a great green tea tempura .\n->", + "output": "{\"text\": \"they have a delicious banana chocolate dessert , as well as a great green tea tempura .\", \"labels\": \"[{'aspect': 'banana chocolate dessert', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'green tea tempura', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the notebook is very well built and it could easily pass as a high - end machine .\n->the notebook is very well built and it could easily pass as a high - end machine .\n[{'aspect': 'notebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'machine', 'opinion': 'high - end', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: super thin - love the color - had to completely remove the operating system though - too many viruses already on the computer when received - had to get my own operating system and re - install everything\n->super thin - love the color - had to completely remove the operating system though - too many viruses already on the computer when received - had to get my own operating system and re - install everything\n[{'aspect': 'operating system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\ntext: the appetizers are also delicious !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe appetizers are also delicious !\n->", + "output": "{\"text\": \"the appetizers are also delicious !\", \"labels\": \"[{'aspect': 'appetizers', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .\n->If you go , try the marinara/arrabiatta sauce , the mozzarella en Carozza is mmmmmmmm ... .. everything is just delicious .\n[{'aspect': 'marinara/arrabiatta sauce', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'marinara/arrabiatta sauce', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mozzarella en Carozza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the plug keeps unplugging .\n->the plug keeps unplugging .\n[{'aspect': 'plug', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: mazing interior .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmazing interior .\n->", + "output": "{\"text\": \"mazing interior .\", \"labels\": \"[{'aspect': 'interior', 'opinion': 'mazing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is a buzzing sound that comes from inside of the keyboard .\n->there is a buzzing sound that comes from inside of the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: so far i absolutely love it .\n->so far i absolutely love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great food !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food !\n->", + "output": "{\"text\": \"great food !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we all felt it was worth it .\n->we all felt it was worth it .\n[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: outside of that , the keyboard is solid , the back lighting was not a selling point to me .\n->outside of that , the keyboard is solid , the back lighting was not a selling point to me .\n[{'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'back lighting', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i ' ve had my fair share of modern japanese and this spot delivers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had my fair share of modern japanese and this spot delivers .\n->", + "output": "{\"text\": \"i ' ve had my fair share of modern japanese and this spot delivers .\", \"labels\": \"[{'aspect': 'modern japanese', 'opinion': 'delivers', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been to rao ' s probably 15 times the past 3 years and it keeps getting better .\n->i have been to rao ' s probably 15 times the past 3 years and it keeps getting better .\n[{'aspect': \"rao ' s\", 'opinion': 'better', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: note : my first one arrived with a crushed speaker screen on one side , though the packaging was unmolested .\n->note : my first one arrived with a crushed speaker screen on one side , though the packaging was unmolested .\n[{'aspect': 'speaker screen', 'opinion': 'crushed', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: the atmosphere was pretty nice but had a bit lacking , which it tries to make up for with a crazy scheme of mirrors .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe atmosphere was pretty nice but had a bit lacking , which it tries to make up for with a crazy scheme of mirrors .\n->", + "output": "{\"text\": \"the atmosphere was pretty nice but had a bit lacking , which it tries to make up for with a crazy scheme of mirrors .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'scheme of mirrors', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will attempt to return it as i am out of the us right now .\n->i will attempt to return it as i am out of the us right now .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i love , love , love this computer .\n->i love , love , love this computer .\n[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\n->", + "output": "{\"text\": \"if you ' re into being lost when you ' re just five feet from your table then hey , that ' s a good thing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n->For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n[{'aspect': 'Paneer Roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but the mac looked and worked great .\n->but the mac looked and worked great .\n[{'aspect': 'mac', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'mac', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndespite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n->", + "output": "{\"text\": \"despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\", \"labels\": \"[{'aspect': 'modern japanese food', 'opinion': 'go - to for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mirrors', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the picture was very bright and clear , the back - lit keyboard was a very nice feature , and it seemed like a good value for the price .\n->the picture was very bright and clear , the back - lit keyboard was a very nice feature , and it seemed like a good value for the price .\n[{'aspect': 'picture', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'picture', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'back - lit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: we love the food , drinks , and atmosphere !\n->we love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: indo chinese food , pretty good . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nindo chinese food , pretty good . . .\n->", + "output": "{\"text\": \"indo chinese food , pretty good . . .\", \"labels\": \"[{'aspect': 'indo chinese food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you do n ' t need a full blown laptop this is a good choice .\n->if you do n ' t need a full blown laptop this is a good choice .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n->it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n[{'aspect': 'screen', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'bizarre', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: not a very fancy place but very good chinese style indian food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot a very fancy place but very good chinese style indian food .\n->", + "output": "{\"text\": \"not a very fancy place but very good chinese style indian food .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'fancy', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'chinese style indian food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n->needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: ingredients are organic which is a real plus for me .\n->ingredients are organic which is a real plus for me .\n[{'aspect': 'ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the chicken lollipop is my favorite , most of the dishes ( i have to agree with a previous reviewer ) are quite oily and very spicy , espeically the chilli chicken .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chicken lollipop is my favorite , most of the dishes ( i have to agree with a previous reviewer ) are quite oily and very spicy , espeically the chilli chicken .\n->", + "output": "{\"text\": \"the chicken lollipop is my favorite , most of the dishes ( i have to agree with a previous reviewer ) are quite oily and very spicy , espeically the chilli chicken .\", \"labels\": \"[{'aspect': 'chicken lollipop', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chilli chicken', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chilli chicken', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you are looking to ditch apple i recommend it and touch screen works great and easy to read movie scripts to take notes .\n->if you are looking to ditch apple i recommend it and touch screen works great and easy to read movie scripts to take notes .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touch screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n->as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: my mom originally introduced me to this place , but even she ( being indian ) feels the food can be somewhat over the top spicy and far too oily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy mom originally introduced me to this place , but even she ( being indian ) feels the food can be somewhat over the top spicy and far too oily .\n->", + "output": "{\"text\": \"my mom originally introduced me to this place , but even she ( being indian ) feels the food can be somewhat over the top spicy and far too oily .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n->at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n[{'aspect': 'model', 'opinion': 'afraid', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'amazon', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: if you want something really different than try jekyll and hyde .\n->if you want something really different than try jekyll and hyde .\n[{'aspect': 'jekyll and hyde', 'opinion': 'different', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: still we keep going back : )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstill we keep going back : )\n->", + "output": "{\"text\": \"still we keep going back : )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - keyboard keys have a shorter touch .\n->- keyboard keys have a shorter touch .\n[{'aspect': 'keyboard keys', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the wine list is also really nice .\n->the wine list is also really nice .\n[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: i was speechless by the horrible food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was speechless by the horrible food .\n->", + "output": "{\"text\": \"i was speechless by the horrible food .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now the hole thing crashed out of nowhere and i ' m going to lose everything i had on it .\n->now the hole thing crashed out of nowhere and i ' m going to lose everything i had on it .\n[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the staff was the friendliest that have seen in new york .\n->the staff was the friendliest that have seen in new york .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i attended a holiday dinner at the restaurant , and the food was majorly disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni attended a holiday dinner at the restaurant , and the food was majorly disappointing .\n->", + "output": "{\"text\": \"i attended a holiday dinner at the restaurant , and the food was majorly disappointing .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to me back - lighting on a keyboard is a make or break so i might return the laptop though ( bought it because i could have sworn i read it was backlit when i purchased it ) .\n->to me back - lighting on a keyboard is a make or break so i might return the laptop though ( bought it because i could have sworn i read it was backlit when i purchased it ) .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: my first chromebook , and so far ( about one month of use ) i like it .\n->my first chromebook , and so far ( about one month of use ) i like it .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i was so stunned , and i left the dinner hungry and majorly disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was so stunned , and i left the dinner hungry and majorly disappointing .\n->", + "output": "{\"text\": \"i was so stunned , and i left the dinner hungry and majorly disappointing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: computer came in good condition and at a good price .\n->computer came in good condition and at a good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#PRICE'}]\nExample:\ntext: monitor looks crisp .\n->monitor looks crisp .\n[{'aspect': 'monitor', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: this place survives on reputation alone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place survives on reputation alone .\n->", + "output": "{\"text\": \"this place survives on reputation alone .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n->I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - pretty loud speakers .\n->- pretty loud speakers .\n[{'aspect': 'speakers', 'opinion': 'loud', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: i will never return .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will never return .\n->", + "output": "{\"text\": \"i will never return .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can use this for school .\n->i can use this for school .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Even though its good seafood , the prices are too high .\n->Even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: best in all of nyc\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest in all of nyc\n->", + "output": "{\"text\": \"best in all of nyc\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like everything about this macpro .\n->i like everything about this macpro .\n[{'aspect': 'macpro', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Although we were looking for regular lettuce and some walnuts the salads we got were great .\n->Although we were looking for regular lettuce and some walnuts the salads we got were great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lettuce', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'walnuts', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n->", + "output": "{\"text\": \"this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had the lobster sandwich and it was FANTASTIC .\n->We had the lobster sandwich and it was FANTASTIC .\n[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: whenever you need a sushi fix , mizu will be there with quality fish and great service .\n->whenever you need a sushi fix , mizu will be there with quality fish and great service .\n[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: for 7 years they have put out the most tasty , most delicious food and kept it that way . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor 7 years they have put out the most tasty , most delicious food and kept it that way . . .\n->", + "output": "{\"text\": \"for 7 years they have put out the most tasty , most delicious food and kept it that way . . .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza was a little soggy .\n->Pizza was a little soggy .\n[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this computer arrived fine but will not charge .\n->this computer arrived fine but will not charge .\n[{'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: never swaying , never a bad meal , never bad service . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnever swaying , never a bad meal , never bad service . . .\n->", + "output": "{\"text\": \"never swaying , never a bad meal , never bad service . . .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'never a bad', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'never bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and it was quick which is very important .\n->and it was quick which is very important .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'important', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: on a hot day it was fabulous to stop in and enjoy lunch .\n->on a hot day it was fabulous to stop in and enjoy lunch .\n[{'aspect': 'NULL', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: you should travel from the bronx to try it . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou should travel from the bronx to try it . . .\n->", + "output": "{\"text\": \"you should travel from the bronx to try it . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is apparent the hard drive has failed yet again .\n->it is apparent the hard drive has failed yet again .\n[{'aspect': 'hard drive', 'opinion': 'failed', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: I loved everythig about it-especially the shows and actors .\n->I loved everythig about it-especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great food , great wine list , great service in a great neighborhood . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food , great wine list , great service in a great neighborhood . . .\n->", + "output": "{\"text\": \"great food , great wine list , great service in a great neighborhood . . .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wine list', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'neighborhood', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great sushi experience .\n->Great sushi experience .\n[{'aspect': 'sushi', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: however , it ' s the service that leaves a bad taste in my mouth .\n->however , it ' s the service that leaves a bad taste in my mouth .\n[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: so delicious ! ! ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso delicious ! ! ! ! ! !\n->", + "output": "{\"text\": \"so delicious ! ! ! ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: easy to use and set up .\n->easy to use and set up .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: patsy ' s pizza = true love\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npatsy ' s pizza = true love\n->", + "output": "{\"text\": \"patsy ' s pizza = true love\", \"labels\": \"[{'aspect': \"patsy ' s pizza\", 'opinion': 'true love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place was nice and calm .\n->The place was nice and calm .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'calm', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is the first macbook i have ever purchased , i wish i had purchased sooner .\n->this is the first macbook i have ever purchased , i wish i had purchased sooner .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: hands down the best pizza on the planet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhands down the best pizza on the planet .\n->", + "output": "{\"text\": \"hands down the best pizza on the planet .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything is either good or excellent in quality .\n->everything is either good or excellent in quality .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: too large for just two people but nothing was left .\n->too large for just two people but nothing was left .\n[{'aspect': 'NULL', 'opinion': 'too large', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: in about 12 minutes , the thing is gone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin about 12 minutes , the thing is gone .\n->", + "output": "{\"text\": \"in about 12 minutes , the thing is gone .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ca n ' t wait to go back .\n->ca n ' t wait to go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i ' m seeing a lot of reviews saying they received the wrong item .\n->i ' m seeing a lot of reviews saying they received the wrong item .\n[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SHIPPING#GENERAL'}]\ntext: the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n->", + "output": "{\"text\": \"the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\", \"labels\": \"[{'aspect': 'hot dogs', 'opinion': 'juicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dogs', 'opinion': 'tender', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n->i would say that all was fine and tasty but the heaviness on my stomach someting that i ca n ' t not mention or undermine .\n[{'aspect': 'NULL', 'opinion': 'heaviness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n->For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n[{'aspect': 'Paneer Roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great toppings definitely a place you need to check out for late night munchies or a mid day boost !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat toppings definitely a place you need to check out for late night munchies or a mid day boost !\n->", + "output": "{\"text\": \"great toppings definitely a place you need to check out for late night munchies or a mid day boost !\", \"labels\": \"[{'aspect': 'toppings', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: samsung rma ' d the device to replace the screen .\n->samsung rma ' d the device to replace the screen .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great food , good size menu , great service and an unpretentious setting .\n->Great food , good size menu , great service and an unpretentious setting .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'unpretentious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great taste\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat taste\n->", + "output": "{\"text\": \"great taste\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n->short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n[{'aspect': 'seating', 'opinion': 'short', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'private', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i will keep my rating at 3 stars , as the issues with the screen quality / shine - and volume / brightness keys being unusable and nonexistent , to be large issues for me .\n->i will keep my rating at 3 stars , as the issues with the screen quality / shine - and volume / brightness keys being unusable and nonexistent , to be large issues for me .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'volume', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'volume', 'opinion': 'nonexistent', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: great , original taste .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat , original taste .\n->", + "output": "{\"text\": \"great , original taste .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'original', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hostess and the waitress were incredibly rude and did everything they could to rush us out .\n->the hostess and the waitress were incredibly rude and did everything they could to rush us out .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitress', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n->I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n[{'aspect': 'scallop roll', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: for me dishes a little oily , but overall dining experience good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor me dishes a little oily , but overall dining experience good .\n->", + "output": "{\"text\": \"for me dishes a little oily , but overall dining experience good .\", \"labels\": \"[{'aspect': 'dishes', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was luke warm .\n->food was luke warm .\n[{'aspect': 'food', 'opinion': 'luke warm', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n->we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n[{'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: helpful service and average price per dish $ 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhelpful service and average price per dish $ 10 .\n->", + "output": "{\"text\": \"helpful service and average price per dish $ 10 .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'dish', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we paid and left because we did n ' t feel like arguing any more .\n->we paid and left because we did n ' t feel like arguing any more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: battery life is superb\n->battery life is superb\n[{'aspect': 'battery life', 'opinion': 'superb', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: great food\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat food\n->", + "output": "{\"text\": \"great food\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n->Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n[{'aspect': 'candle-light', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'closely situated', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n->Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n[{'aspect': 'wine selection', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Gigondas', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'worth', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: this place has great indian chinese food .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place has great indian chinese food .\n->", + "output": "{\"text\": \"this place has great indian chinese food .\", \"labels\": \"[{'aspect': 'indian chinese food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n->Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n[{'aspect': 'fruit of the oil', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'sweetness', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ll end the review simply saying i ' m very happy overall with my purchase !\n->i ' ll end the review simply saying i ' m very happy overall with my purchase !\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the flavors are amazing and the value is phenomenal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe flavors are amazing and the value is phenomenal .\n->", + "output": "{\"text\": \"the flavors are amazing and the value is phenomenal .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so close , but not good enough .\n->so close , but not good enough .\n[{'aspect': 'NULL', 'opinion': 'not good enough', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: ridiculous for something so expensive , some internet research shows numerous people having this issue .\n->ridiculous for something so expensive , some internet research shows numerous people having this issue .\n[{'aspect': 'NULL', 'opinion': 'ridiculous', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: be prepared to wait , because the place is pretty tiny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbe prepared to wait , because the place is pretty tiny .\n->", + "output": "{\"text\": \"be prepared to wait , because the place is pretty tiny .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as it turned out the thunderbolt ports are of no use .\n->as it turned out the thunderbolt ports are of no use .\n[{'aspect': 'thunderbolt ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#USABILITY'}]\nExample:\ntext: note : i bought a refurbished / ` ` damaged ` ` model from amazon and it ' s in absolutely flawless condition .\n->note : i bought a refurbished / ` ` damaged ` ` model from amazon and it ' s in absolutely flawless condition .\n[{'aspect': 'model', 'opinion': 'flawless', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: also , they do not take credit card so come with cash !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , they do not take credit card so come with cash !\n->", + "output": "{\"text\": \"also , they do not take credit card so come with cash !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stop working the macbook pro\n->stop working the macbook pro\n[{'aspect': 'macbook pro', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: even though the place is not beautiful , the food speaks for itself .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven though the place is not beautiful , the food speaks for itself .\n->", + "output": "{\"text\": \"even though the place is not beautiful , the food speaks for itself .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'not beautiful', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'speaks for itself', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is also one of the nicest most comfortable i have ever used .\n->the keyboard is also one of the nicest most comfortable i have ever used .\n[{'aspect': 'keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n->update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n[{'aspect': 'laptop', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: best indian chinese in the city , by far !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest indian chinese in the city , by far !\n->", + "output": "{\"text\": \"best indian chinese in the city , by far !\", \"labels\": \"[{'aspect': 'indian chinese', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza was great .\n->The pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: one year later and the laptop is still in great condition !\n->one year later and the laptop is still in great condition !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: amazing my favorite ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazing my favorite ! ! !\n->", + "output": "{\"text\": \"amazing my favorite ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for some of us , even though they are more expensive , they still offer the better value .\n->for some of us , even though they are more expensive , they still offer the better value .\n[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i will admit that i needed to get used to a combination of keystrokes and screen touches , but the touch screen is both sensitive and accurate .\n->i will admit that i needed to get used to a combination of keystrokes and screen touches , but the touch screen is both sensitive and accurate .\n[{'aspect': 'keystrokes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'screen touches', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'touch screen', 'opinion': 'sensitive', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'touch screen', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: i came across village underground by accident , now i go there all the time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni came across village underground by accident , now i go there all the time .\n->", + "output": "{\"text\": \"i came across village underground by accident , now i go there all the time .\", \"labels\": \"[{'aspect': 'village underground', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: since i already do all of my work in google drive , this is perfect for me .\n->since i already do all of my work in google drive , this is perfect for me .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Food is great and inexpensive .\n->Food is great and inexpensive .\n[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n->", + "output": "{\"text\": \"the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n->apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#OPERATION_PERFORMANCE'}]\nExample:\ntext: great battery life , a matte screen ( non - glossy ) full hd .\n->great battery life , a matte screen ( non - glossy ) full hd .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'matte screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: the dj is awesome , i have been there for my birthday and a bunch of other times with friends and i keep going back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dj is awesome , i have been there for my birthday and a bunch of other times with friends and i keep going back .\n->", + "output": "{\"text\": \"the dj is awesome , i have been there for my birthday and a bunch of other times with friends and i keep going back .\", \"labels\": \"[{'aspect': 'dj', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , it does feel like a sturdy hinge .\n->however , it does feel like a sturdy hinge .\n[{'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i almost hesititate to write a review because the atmosphere was so great and i would hate for it too become to crowded .\n->i almost hesititate to write a review because the atmosphere was so great and i would hate for it too become to crowded .\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n->", + "output": "{\"text\": \"you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is exactly what i needed .\n->this is exactly what i needed .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: gross food \u2013 wow -\n->gross food \u2013 wow -\n[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: which lets face it . . . . at times it ' s a good thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhich lets face it . . . . at times it ' s a good thing .\n->", + "output": "{\"text\": \"which lets face it . . . . at times it ' s a good thing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n->All the appetizers and salads were fabulous , the steak was mouth watering and the pasta was delicious ! ! !\n[{'aspect': 'appetizers', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'steak', 'opinion': 'watering', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this item cost me money .\n->this item cost me money .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: in the end you end up with a fair tab and nothing but a great time ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin the end you end up with a fair tab and nothing but a great time ! ! !\n->", + "output": "{\"text\": \"in the end you end up with a fair tab and nothing but a great time ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fair', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the four seasons restaurant is a great experience .\n->the four seasons restaurant is a great experience .\n[{'aspect': 'the four seasons restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i can eat here every day of the week really lol love this place . . . )\n->i can eat here every day of the week really lol love this place . . . )\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: highly recommended !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly recommended !\n->", + "output": "{\"text\": \"highly recommended !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n->At the end you 're left with a mild broth with noodles that you can slurp out of a cup .\n[{'aspect': 'broth with noodles', 'opinion': 'mild', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and $ 11 for a plate of bland guacamole ?\n->and $ 11 for a plate of bland guacamole ?\n[{'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'guacamole', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: one of the best , if not the best , restaurants in park slope ( and ny in general )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of the best , if not the best , restaurants in park slope ( and ny in general )\n->", + "output": "{\"text\": \"one of the best , if not the best , restaurants in park slope ( and ny in general )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has and does everything it should .\n->it has and does everything it should .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve had the device for 4 days now , and it has a number of big issues .\n->i ' ve had the device for 4 days now , and it has a number of big issues .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: everything on the menu is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything on the menu is great .\n->", + "output": "{\"text\": \"everything on the menu is great .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's a shame that a nice , convenient place like the Pink Pony can be so ruined by lousy service .\n->It 's a shame that a nice , convenient place like the Pink Pony can be so ruined by lousy service .\n[{'aspect': 'place', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: love the style , aluminum shell , 14 inches monitor , and decent resolution .\n->love the style , aluminum shell , 14 inches monitor , and decent resolution .\n[{'aspect': 'style', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'aluminum shell', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '14 inches monitor', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: this establishment is the real deal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis establishment is the real deal .\n->", + "output": "{\"text\": \"this establishment is the real deal .\", \"labels\": \"[{'aspect': 'establishment', 'opinion': 'real deal', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish i could give it away at this point .\n->i wish i could give it away at this point .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it started throwing i / o errors ( log viewer ) and occasionally ` ` system error - 50 ` ` would show .\n->it started throwing i / o errors ( log viewer ) and occasionally ` ` system error - 50 ` ` would show .\n[{'aspect': 'i / o', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: wish ny had more of these kind of places : intimate , superb food , homey , top notch all the way around , certainly worth the wait .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwish ny had more of these kind of places : intimate , superb food , homey , top notch all the way around , certainly worth the wait .\n->", + "output": "{\"text\": \"wish ny had more of these kind of places : intimate , superb food , homey , top notch all the way around , certainly worth the wait .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'homey', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dinner was ok , nothing i would have again .\n->the dinner was ok , nothing i would have again .\n[{'aspect': 'dinner', 'opinion': 'ok', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: indoor was very cozy and cute .\n->indoor was very cozy and cute .\n[{'aspect': 'indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: but $ 500 for a dinner for two that did n ' t include wine ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut $ 500 for a dinner for two that did n ' t include wine ?\n->", + "output": "{\"text\": \"but $ 500 for a dinner for two that did n ' t include wine ?\", \"labels\": \"[{'aspect': 'dinner for two', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good battery life\n->good battery life\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: battery lasts for quite sometime .\n->battery lasts for quite sometime .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: look , the appetizers were really good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlook , the appetizers were really good .\n->", + "output": "{\"text\": \"look , the appetizers were really good .\", \"labels\": \"[{'aspect': 'appetizers', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: im actually wondering if there is an issue with the speakers , it ' s so bad .\n->im actually wondering if there is an issue with the speakers , it ' s so bad .\n[{'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: i really can ' t say enough about this awesome laptop .\n->i really can ' t say enough about this awesome laptop .\n[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the entree was also very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe entree was also very good .\n->", + "output": "{\"text\": \"the entree was also very good .\", \"labels\": \"[{'aspect': 'entree', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromebooks are light weight and start up immediately and are very easy to use .\n->chromebooks are light weight and start up immediately and are very easy to use .\n[{'aspect': 'chromebooks', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebooks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: I highly recommend visiting this restaurant and having dinner and drinks !\n->I highly recommend visiting this restaurant and having dinner and drinks !\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ca n ' t argue about that , but they are clearly over priced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nca n ' t argue about that , but they are clearly over priced .\n->", + "output": "{\"text\": \"ca n ' t argue about that , but they are clearly over priced .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n->downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n[{'aspect': 'downloading android apps', 'opinion': 'easy', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: the 13 ` ` is a good weight .\n->the 13 ` ` is a good weight .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: what you are paying for is the environment and the name .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat you are paying for is the environment and the name .\n->", + "output": "{\"text\": \"what you are paying for is the environment and the name .\", \"labels\": \"[{'aspect': 'environment', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n->The people that work there are always so friendly you forget you are in New York sometimes .\n[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n->Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n[{'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n->", + "output": "{\"text\": \"yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'classy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their tuna tartar appetizer is to die for .\n->Their tuna tartar appetizer is to die for .\n[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the hard drive sounds like a distant lawn mower .\n->the hard drive sounds like a distant lawn mower .\n[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: which of course is not real kobe but wagyu beef .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhich of course is not real kobe but wagyu beef .\n->", + "output": "{\"text\": \"which of course is not real kobe but wagyu beef .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my screen stayed black more than it was on .\n->my screen stayed black more than it was on .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: beautiful ips display .\n->beautiful ips display .\n[{'aspect': 'ips display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: surprised that a place of this caliber would advertise it as kobe .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsurprised that a place of this caliber would advertise it as kobe .\n->", + "output": "{\"text\": \"surprised that a place of this caliber would advertise it as kobe .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent food , although the interior could use some help .\n->excellent food , although the interior could use some help .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'interior', 'opinion': 'help', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: They pray to their Food Gods to make them into a good pizza like VT 's .\n->They pray to their Food Gods to make them into a good pizza like VT 's .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: vanison was good but not amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvanison was good but not amazing .\n->", + "output": "{\"text\": \"vanison was good but not amazing .\", \"labels\": \"[{'aspect': 'vanison', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'vanison', 'opinion': 'not amazing', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: boot up is of course , almost instant .\n->boot up is of course , almost instant .\n[{'aspect': 'boot up', 'opinion': 'instant', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the waitress , seems to be more concerned of looking good than actually waitressing .\n->the waitress , seems to be more concerned of looking good than actually waitressing .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: bison was quite excellent however .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbison was quite excellent however .\n->", + "output": "{\"text\": \"bison was quite excellent however .\", \"labels\": \"[{'aspect': 'bison', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: was surprisingly disappointed .\n->was surprisingly disappointed .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: but after only a couple of days the thing just turned off and won ' t turn back on again .\n->but after only a couple of days the thing just turned off and won ' t turn back on again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: dessert : pure disaster .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndessert : pure disaster .\n->", + "output": "{\"text\": \"dessert : pure disaster .\", \"labels\": \"[{'aspect': 'dessert', 'opinion': 'disaster', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * very solidly built and it transitions nicely from laptop to tablet mode .\n->* very solidly built and it transitions nicely from laptop to tablet mode .\n[{'aspect': 'built', 'opinion': 'solidly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'tablet', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n->She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: just not good at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust not good at all .\n->", + "output": "{\"text\": \"just not good at all .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has and does everything it should .\n->it has and does everything it should .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n->so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: some pineapple covered in a glaze of some kind and some pear tart thing not impressive at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsome pineapple covered in a glaze of some kind and some pear tart thing not impressive at all .\n->", + "output": "{\"text\": \"some pineapple covered in a glaze of some kind and some pear tart thing not impressive at all .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not impressive', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'not impressive', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am sure amazon will exchange it again but it is not worth the time and hassle .\n->i am sure amazon will exchange it again but it is not worth the time and hassle .\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: not a great place for family or general dining .\n->not a great place for family or general dining .\n[{'aspect': 'place', 'opinion': 'not a great', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n->", + "output": "{\"text\": \"i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything works fast and smooth .\n->everything works fast and smooth .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n->Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n[{'aspect': 'food', 'opinion': 'loving', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dining experiences', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n->", + "output": "{\"text\": \"the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\", \"labels\": \"[{'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great speed and storage .\n->great speed and storage .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: First off , the waitress was completely unattentive the 2 times we saw her ( odd in a restaurant with 6 tables ) and got our order wrong .\n->First off , the waitress was completely unattentive the 2 times we saw her ( odd in a restaurant with 6 tables ) and got our order wrong .\n[{'aspect': 'waitress', 'opinion': 'unattentive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: all in all , the food was great ( except for the dessserts ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all , the food was great ( except for the dessserts ) .\n->", + "output": "{\"text\": \"all in all , the food was great ( except for the dessserts ) .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessserts', 'opinion': 'except', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n->now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: we thought the dessert would be better , wrong !\n->we thought the dessert would be better , wrong !\n[{'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: if you are going for the food , it will not be worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are going for the food , it will not be worth it .\n->", + "output": "{\"text\": \"if you are going for the food , it will not be worth it .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not imagine you not rushing out to eat there .\n->i can not imagine you not rushing out to eat there .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: will not be buying asus again\n->will not be buying asus again\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: you would think they would make up for it with service , sadly , no .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou would think they would make up for it with service , sadly , no .\n->", + "output": "{\"text\": \"you would think they would make up for it with service , sadly , no .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'sadly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the prices were cheap compared to the quality of service and food .\n->the prices were cheap compared to the quality of service and food .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: an awesome product , well built - well worth your time and money .\n->an awesome product , well built - well worth your time and money .\n[{'aspect': 'product', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'well built', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'product', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: service was just ok , it is not what you ' d expect for $ 500 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice was just ok , it is not what you ' d expect for $ 500 .\n->", + "output": "{\"text\": \"service was just ok , it is not what you ' d expect for $ 500 .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: rao ' s has the best service and atmosphere in nyc .\n->rao ' s has the best service and atmosphere in nyc .\n[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n->I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n[{'aspect': 'scallops', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mahi mahi ( on saffron risotto', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: terrible waste of money . . scammers\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nterrible waste of money . . scammers\n->", + "output": "{\"text\": \"terrible waste of money . . scammers\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'scammers', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: less than 90 days and the screen stopped working .\n->less than 90 days and the screen stopped working .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: the price was great during prime days .\n->the price was great during prime days .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: i agree that dining at casa la femme is like no other dining experience !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni agree that dining at casa la femme is like no other dining experience !\n->", + "output": "{\"text\": \"i agree that dining at casa la femme is like no other dining experience !\", \"labels\": \"[{'aspect': 'casa la femme', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little place has a cute interior decor and affordable city prices .\n->this little place has a cute interior decor and affordable city prices .\n[{'aspect': 'interior decor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'little', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n->yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n[{'aspect': 'place', 'opinion': 'classy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: i am actually offended to have spent so much money on such a bad experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am actually offended to have spent so much money on such a bad experience .\n->", + "output": "{\"text\": \"i am actually offended to have spent so much money on such a bad experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best dish is nori-wrapped tuna .\n->Best dish is nori-wrapped tuna .\n[{'aspect': 'nori-wrapped tuna', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The place was nice and calm .\n->The place was nice and calm .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'calm', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i literally just got back home after visiting casa la femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni literally just got back home after visiting casa la femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .\n->", + "output": "{\"text\": \"i literally just got back home after visiting casa la femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .\", \"labels\": \"[{'aspect': 'casa la femme', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'casa la femme', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speakers are surprisingly decent .\n->the speakers are surprisingly decent .\n[{'aspect': 'speakers', 'opinion': 'decent', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n->During the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: our visit their to say the least , was an unpleasant and costly experience !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour visit their to say the least , was an unpleasant and costly experience !\n->", + "output": "{\"text\": \"our visit their to say the least , was an unpleasant and costly experience !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'costly', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after seven months , the usb - c ports stopped charging .\n->after seven months , the usb - c ports stopped charging .\n[{'aspect': 'usb - c ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the cream cheeses are out of this world and i love that coffee ! !\n->the cream cheeses are out of this world and i love that coffee ! !\n[{'aspect': 'cream cheeses', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'coffee', 'opinion': 'love', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: and even more so unpleasant because it was so costly for such an unpleasant experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand even more so unpleasant because it was so costly for such an unpleasant experience .\n->", + "output": "{\"text\": \"and even more so unpleasant because it was so costly for such an unpleasant experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'costly', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We wo n't go to this place again for a good meal .\n->We wo n't go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n->they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: we did arrive late for our reservation so i can not complain too much about the wait for a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe did arrive late for our reservation so i can not complain too much about the wait for a table .\n->", + "output": "{\"text\": \"we did arrive late for our reservation so i can not complain too much about the wait for a table .\", \"labels\": \"[{'aspect': 'wait', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The kitchen however , is almost always slow .\n->The kitchen however , is almost always slow .\n[{'aspect': 'kitchen', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: they have authentic indian at amazing prices .\n->they have authentic indian at amazing prices .\n[{'aspect': 'indian', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: although we were told 10 - 15 minutes and it was more like 45 minutes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough we were told 10 - 15 minutes and it was more like 45 minutes .\n->", + "output": "{\"text\": \"although we were told 10 - 15 minutes and it was more like 45 minutes .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: extremely disappointed as this was a gift to my husband .\n->extremely disappointed as this was a gift to my husband .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the speakers overall are not very good .\n->the speakers overall are not very good .\n[{'aspect': 'speakers', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: we were ushered to the bar to wait momentarily and upon arrival were so excited .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were ushered to the bar to wait momentarily and upon arrival were so excited .\n->", + "output": "{\"text\": \"we were ushered to the bar to wait momentarily and upon arrival were so excited .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excited', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the owner and staff are all japanese as well and that adds to the entire ambiance .\n->the owner and staff are all japanese as well and that adds to the entire ambiance .\n[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'ambiance', 'opinion': 'adds', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Knowledge of the chef and the waitress are below average .\n->Knowledge of the chef and the waitress are below average .\n[{'aspect': 'chef', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the place is beautiful !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place is beautiful !\n->", + "output": "{\"text\": \"the place is beautiful !\", \"labels\": \"[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: still learning but it ' s a good computer and a great deal\n->still learning but it ' s a good computer and a great deal\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: pro ' s : this chromebook is very light .\n->pro ' s : this chromebook is very light .\n[{'aspect': 'chromebook', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the hostess was very pleasant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hostess was very pleasant .\n->", + "output": "{\"text\": \"the hostess was very pleasant .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would definitely recommend SEA if you like thai cuisine !\n->I would definitely recommend SEA if you like thai cuisine !\n[{'aspect': 'thai cuisine', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: everything from the power cord to the computer looks brand new .\n->everything from the power cord to the computer looks brand new .\n[{'aspect': 'power cord', 'opinion': 'new', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: our experience did not ever get any better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour experience did not ever get any better .\n->", + "output": "{\"text\": \"our experience did not ever get any better .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent lapto , just as they show it .\n->excellent lapto , just as they show it .\n[{'aspect': 'lapto', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The wine list is excellent .\n->The wine list is excellent .\n[{'aspect': 'wine list', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: for each course we waited over 1 / 2 hour to 45 minutes and were never offered a drink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor each course we waited over 1 / 2 hour to 45 minutes and were never offered a drink .\n->", + "output": "{\"text\": \"for each course we waited over 1 / 2 hour to 45 minutes and were never offered a drink .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also bought a wireless mouse , which paired perfectly .\n->i also bought a wireless mouse , which paired perfectly .\n[{'aspect': 'wireless mouse', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: this was the second vivobook in a row !\n->this was the second vivobook in a row !\n[{'aspect': 'vivobook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n->", + "output": "{\"text\": \"we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been using a chromebook now for three years and am totally satisfied .\n->i have been using a chromebook now for three years and am totally satisfied .\n[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n->a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n[{'aspect': 'gentleman', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: after the 4th time i asked again and the waiter than said after our dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter the 4th time i asked again and the waiter than said after our dinner .\n->", + "output": "{\"text\": \"after the 4th time i asked again and the waiter than said after our dinner .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great bar , most gorgeous bartenders you 've ever seen ( specifically the blond lady ) .\n->Great bar , most gorgeous bartenders you 've ever seen ( specifically the blond lady ) .\n[{'aspect': 'bar', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bartenders', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n->this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n[{'aspect': 'performance', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'happy', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: we asked for beverages and never received them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe asked for beverages and never received them .\n->", + "output": "{\"text\": \"we asked for beverages and never received them .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The waitress suggested glasses of wine that went very well with the food .\n->The waitress suggested glasses of wine that went very well with the food .\n[{'aspect': 'glasses of wine', 'opinion': 'went very well with the food', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Big Wong gets big Ups for a fine establishment .\n->Big Wong gets big Ups for a fine establishment .\n[{'aspect': 'establishment', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we asked for sides which the waiter than admitted that he forgot to put in that part of our order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe asked for sides which the waiter than admitted that he forgot to put in that part of our order .\n->", + "output": "{\"text\": \"we asked for sides which the waiter than admitted that he forgot to put in that part of our order .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'forgot', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n->The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining hall', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: super yummy pizza !\n->super yummy pizza !\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: my chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .\n->", + "output": "{\"text\": \"my chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .\", \"labels\": \"[{'aspect': 'chicken', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their exotic salad is basic ly a delicious little green salad with a peanut sauce that is perfect before their sweet basil fried tofu .\n->Their exotic salad is basic ly a delicious little green salad with a peanut sauce that is perfect before their sweet basil fried tofu .\n[{'aspect': 'exotic salad', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'green salad', 'opinion': 'delicious little', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'peanut sauce', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it worked beautifully and smoothly .\n->it worked beautifully and smoothly .\n[{'aspect': 'NULL', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i would not expect this for a $ 55 dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would not expect this for a $ 55 dinner .\n->", + "output": "{\"text\": \"i would not expect this for a $ 55 dinner .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dinner', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stick with the chicken , beef , and lamb dishes .\n->stick with the chicken , beef , and lamb dishes .\n[{'aspect': 'chicken', 'opinion': 'stick', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'beef', 'opinion': 'stick', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb dishes', 'opinion': 'stick', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the pizza was great .\n->the pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: by the time we finished our dinner we still had not received one beverage nor hooka !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nby the time we finished our dinner we still had not received one beverage nor hooka !\n->", + "output": "{\"text\": \"by the time we finished our dinner we still had not received one beverage nor hooka !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: trackpad keeps breaking .\n->trackpad keeps breaking .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n->To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\ntext: what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n->", + "output": "{\"text\": \"what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I loved this place ! !\n->I loved this place ! !\n[{'aspect': 'place', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: camera is sd but not a problem .\n->camera is sd but not a problem .\n[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\ntext: the only beverage we did receive was water in dirty glasses !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only beverage we did receive was water in dirty glasses !\n->", + "output": "{\"text\": \"the only beverage we did receive was water in dirty glasses !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'dirty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: customer service told me that i ' d have to buy a new one , item is still under warranty .\n->customer service told me that i ' d have to buy a new one , item is still under warranty .\n[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the food is reliable and the price is moderate .\n->the food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\ntext: to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n->", + "output": "{\"text\": \"to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good laptop , but not great .\n->good laptop , but not great .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food is tasty and portion sizes are appropriate .\n->The food is tasty and portion sizes are appropriate .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'NULL'}]\ntext: by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nby the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n->", + "output": "{\"text\": \"by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve been there three times and have always had wonderful experiences .\n->i ' ve been there three times and have always had wonderful experiences .\n[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The entree was bland and small , dessert was not inspired .\n->The entree was bland and small , dessert was not inspired .\n[{'aspect': 'entree', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'entree', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'not inspired', 'polarity': 'negative', 'category': 'NULL'}]\ntext: if it seemed possible to do so while there i would have fought my bill since my dinner portion of my meal was inedible !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif it seemed possible to do so while there i would have fought my bill since my dinner portion of my meal was inedible !\n->", + "output": "{\"text\": \"if it seemed possible to do so while there i would have fought my bill since my dinner portion of my meal was inedible !\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The filet mignon dish was superb !\n->The filet mignon dish was superb !\n[{'aspect': 'filet mignon dish', 'opinion': 'superb', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: then it just rebooted without prompt .\n->then it just rebooted without prompt .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n->", + "output": "{\"text\": \"i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ordered this computer to use in college and also for gaming .\n->ordered this computer to use in college and also for gaming .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n->The mussaman curry that I ordered was as thin as water and aside from the poorly fried tofu that I ordered in it , they graciously provided me with ONE piece of poorly cooked potato .\n[{'aspect': 'mussaman curry', 'opinion': 'thin', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'fried tofu', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato', 'opinion': 'poorly cooked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: my husbands birthday and my sons was not as it was intended . . . and we drove two hours to spend too much money to be treated terribly !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy husbands birthday and my sons was not as it was intended . . . and we drove two hours to spend too much money to be treated terribly !\n->", + "output": "{\"text\": \"my husbands birthday and my sons was not as it was intended . . . and we drove two hours to spend too much money to be treated terribly !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is ok but could be better .\n->The service is ok but could be better .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'could be better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it is heavy , but that is to be expected with a laptop like this one .\n->it is heavy , but that is to be expected with a laptop like this one .\n[{'aspect': 'laptop', 'opinion': 'heavy', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i wish i could be refunded !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wish i could be refunded !\n->", + "output": "{\"text\": \"i wish i could be refunded !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speed : it ' s very fast .\n->speed : it ' s very fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the battery life is a huge selling point in my opinion : even after multiple shut - downs / start - ups throughout the day , i get about 10 hours total run - time , and charging only takes about an hour .\n->the battery life is a huge selling point in my opinion : even after multiple shut - downs / start - ups throughout the day , i get about 10 hours total run - time , and charging only takes about an hour .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: do n ' t go with a larger group than 4 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo n ' t go with a larger group than 4 !\n->", + "output": "{\"text\": \"do n ' t go with a larger group than 4 !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i have had this computer for almost a year now and i love it .\n->i have had this computer for almost a year now and i love it .\n[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: at first we were a little taken aback , as this seemed to present a problem , although the restaurant looked fairly empty , but they hastily put the table together for us .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat first we were a little taken aback , as this seemed to present a problem , although the restaurant looked fairly empty , but they hastily put the table together for us .\n->", + "output": "{\"text\": \"at first we were a little taken aback , as this seemed to present a problem , although the restaurant looked fairly empty , but they hastily put the table together for us .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Cafe Noir', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - long battery life\n->- long battery life\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: the menu is fairly simple without much descriptions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe menu is fairly simple without much descriptions .\n->", + "output": "{\"text\": \"the menu is fairly simple without much descriptions .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'simple', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far great machine .\n->so far great machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - fans run more often on the latest version .\n->- fans run more often on the latest version .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}]\ntext: there was no tap beer that evening , which was a disappointment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere was no tap beer that evening , which was a disappointment .\n->", + "output": "{\"text\": \"there was no tap beer that evening , which was a disappointment .\", \"labels\": \"[{'aspect': 'beer', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n->it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'insane', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: perfect laptop for everyday use .\n->perfect laptop for everyday use .\n[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: not much of a selection of bottled beer either , we went with brahma .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot much of a selection of bottled beer either , we went with brahma .\n->", + "output": "{\"text\": \"not much of a selection of bottled beer either , we went with brahma .\", \"labels\": \"[{'aspect': 'selection of bottled beer', 'opinion': 'not much', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n->we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: have frequented ' ino for several years and the food remains excellent .\n->have frequented ' ino for several years and the food remains excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .\n->", + "output": "{\"text\": \"the appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .\", \"labels\": \"[{'aspect': 'fried oysters and clams', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fried oysters and clams', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it simply refuses to boot .\n->it simply refuses to boot .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the lunch buffet is expensive but is deff worth it .\n->the lunch buffet is expensive but is deff worth it .\n[{'aspect': 'lunch buffet', 'opinion': 'expensive', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'lunch buffet', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n->", + "output": "{\"text\": \"the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\", \"labels\": \"[{'aspect': 'lobster knuckles', 'opinion': 'ok', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'lobster knuckles', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent atmosphere , delicious dishes good and friendly service .\n->excellent atmosphere , delicious dishes good and friendly service .\n[{'aspect': 'atmosphere', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: May , the owner always has a smile on her and will warmly greet you .\n->May , the owner always has a smile on her and will warmly greet you .\n[{'aspect': 'owner', 'opinion': 'warmly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i believe there were 2 shrimp in the ` ` salt encrusted shrimp ' ' appetizer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni believe there were 2 shrimp in the ` ` salt encrusted shrimp ' ' appetizer .\n->", + "output": "{\"text\": \"i believe there were 2 shrimp in the ` ` salt encrusted shrimp ' ' appetizer .\", \"labels\": \"[{'aspect': \"` ` salt encrusted shrimp ' ' appetizer\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t know why google print is so touchy .\n->i don ' t know why google print is so touchy .\n[{'aspect': 'google print', 'opinion': 'touchy', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: touchpad is nice and responsive .\n->touchpad is nice and responsive .\n[{'aspect': 'touchpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: she replied ` ` well it would be more convenient for us if you ordered now , since you are a larger party , and it might get crowded . ' '\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe replied ` ` well it would be more convenient for us if you ordered now , since you are a larger party , and it might get crowded . ' '\n->", + "output": "{\"text\": \"she replied ` ` well it would be more convenient for us if you ordered now , since you are a larger party , and it might get crowded . ' '\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you must try the shrimp appetizers .\n->you must try the shrimp appetizers .\n[{'aspect': 'shrimp appetizers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: service is not exactly five star , but thats not really a big deal .\n->service is not exactly five star , but thats not really a big deal .\n[{'aspect': 'service', 'opinion': 'not exactly five star', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: the food arrived in about 15 minutes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food arrived in about 15 minutes .\n->", + "output": "{\"text\": \"the food arrived in about 15 minutes .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very happy with this purchase .\n->i am very happy with this purchase .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fries', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i had the thai style fried sea bass . . . which was very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had the thai style fried sea bass . . . which was very good .\n->", + "output": "{\"text\": \"i had the thai style fried sea bass . . . which was very good .\", \"labels\": \"[{'aspect': 'thai style fried sea bass', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n->i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n[{'aspect': 'zenkichi', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'unhurried', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: this is among the best .\n->this is among the best .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: everyone seemed generally happy with their food , except my brother who had the grilled mahi mahi , seemingly drenched in grapfruit juice !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neveryone seemed generally happy with their food , except my brother who had the grilled mahi mahi , seemingly drenched in grapfruit juice !\n->", + "output": "{\"text\": \"everyone seemed generally happy with their food , except my brother who had the grilled mahi mahi , seemingly drenched in grapfruit juice !\", \"labels\": \"[{'aspect': 'food', 'opinion': 'happy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled mahi mahi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled mahi mahi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recommend it , definitely\n->i recommend it , definitely\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: about half of my home automation apps also do not work .\n->about half of my home automation apps also do not work .\n[{'aspect': 'home automation apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i heard the lobster roll was excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni heard the lobster roll was excellent .\n->", + "output": "{\"text\": \"i heard the lobster roll was excellent .\", \"labels\": \"[{'aspect': 'lobster roll', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: truly the mark of an attentive waiter .\n->truly the mark of an attentive waiter .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n->slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\ntext: they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n->", + "output": "{\"text\": \"they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: maitre - d - ' ' eat and get out ' '\n->maitre - d - ' ' eat and get out ' '\n[{'aspect': 'maitre - d', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\ntext: all in all the food was good - a little on the expensive side , but fresh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all the food was good - a little on the expensive side , but fresh .\n->", + "output": "{\"text\": \"all in all the food was good - a little on the expensive side , but fresh .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen display isn ' t bright at all !\n->the screen display isn ' t bright at all !\n[{'aspect': 'screen display', 'opinion': \"' t bright at all\", 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food is reliable and the price is moderate .\n->the food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\ntext: service not the friendliest to our ` ` large party ' ' !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nservice not the friendliest to our ` ` large party ' ' !\n->", + "output": "{\"text\": \"service not the friendliest to our ` ` large party ' ' !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'not the friendliest', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: runs good , poor battery life .\n->runs good , poor battery life .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'poor', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n->we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'tired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: probably would not go back here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprobably would not go back here .\n->", + "output": "{\"text\": \"probably would not go back here .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazing laptop !\n->amazing laptop !\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i don ' t know that i would want to use for work but it ' s perfect for personal use .\n->i don ' t know that i would want to use for work but it ' s perfect for personal use .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great indian food\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat indian food\n->", + "output": "{\"text\": \"great indian food\", \"labels\": \"[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service here was great , food was fantastic .\n->Service here was great , food was fantastic .\n[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: one of my favorite places in manhattan .\n->one of my favorite places in manhattan .\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: food was amazing - i love indian food and eat it quite regularly , but i can say this is one of the best i ' ve had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfood was amazing - i love indian food and eat it quite regularly , but i can say this is one of the best i ' ve had .\n->", + "output": "{\"text\": \"food was amazing - i love indian food and eat it quite regularly , but i can say this is one of the best i ' ve had .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Similar to other Indian restaurants , they use the dinner special to attract customers at the door .\n->Similar to other Indian restaurants , they use the dinner special to attract customers at the door .\n[{'aspect': 'dinner special', 'opinion': 'attract', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: camera is not that good , so video call on mobile phone is better .\n->camera is not that good , so video call on mobile phone is better .\n[{'aspect': 'camera', 'opinion': 'not that good', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: very ` ` normal indian food ' ' , but done really well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery ` ` normal indian food ' ' , but done really well .\n->", + "output": "{\"text\": \"very ` ` normal indian food ' ' , but done really well .\", \"labels\": \"[{'aspect': 'indian food', 'opinion': 'normal', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'indian food', 'opinion': 'well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after updating the software i noticed that the was a lot of backlight bleeding from the display .\n->after updating the software i noticed that the was a lot of backlight bleeding from the display .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: it also has a great backlit keyboard .\n->it also has a great backlit keyboard .\n[{'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: also , waiters try to push more food on you , like suggest things as if they are complimentary when they actually cost $ .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , waiters try to push more food on you , like suggest things as if they are complimentary when they actually cost $ .\n->", + "output": "{\"text\": \"also , waiters try to push more food on you , like suggest things as if they are complimentary when they actually cost $ .\", \"labels\": \"[{'aspect': 'waiters', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: * very solidly built and it transitions nicely from laptop to tablet mode .\n->* very solidly built and it transitions nicely from laptop to tablet mode .\n[{'aspect': 'built', 'opinion': 'solidly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'tablet', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n->", + "output": "{\"text\": \"but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'indian food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is among the best .\n->this is among the best .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'place', 'opinion': 'exceeded my expectations', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: one of the best\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of the best\n->", + "output": "{\"text\": \"one of the best\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far it ' s running smooth with no issues as if it was new .\n->so far it ' s running smooth with no issues as if it was new .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: skip dessert .\n->skip dessert .\n[{'aspect': 'dessert', 'opinion': 'skip', 'polarity': 'negative', 'category': 'NULL'}]\ntext: bukhara grill , the tagline says it all . . ` ` indian spice rave ' '\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbukhara grill , the tagline says it all . . ` ` indian spice rave ' '\n->", + "output": "{\"text\": \"bukhara grill , the tagline says it all . . ` ` indian spice rave ' '\", \"labels\": \"[{'aspect': 'bukhara grill', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ca n ' t remember the last time i had such gross food in new york .\n->i ca n ' t remember the last time i had such gross food in new york .\n[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: first of all , the battery life on it is insane .\n->first of all , the battery life on it is insane .\n[{'aspect': 'battery life', 'opinion': 'insane', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the lunch buffet is expensive but is deff worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe lunch buffet is expensive but is deff worth it .\n->", + "output": "{\"text\": \"the lunch buffet is expensive but is deff worth it .\", \"labels\": \"[{'aspect': 'lunch buffet', 'opinion': 'expensive', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'lunch buffet', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n->first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n[{'aspect': 'keyboard', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: lives up to the hype\n->lives up to the hype\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: we have gone for dinner only a few times but the same great quality and service is given .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe have gone for dinner only a few times but the same great quality and service is given .\n->", + "output": "{\"text\": \"we have gone for dinner only a few times but the same great quality and service is given .\", \"labels\": \"[{'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The steak is good , the fish is good and the sushi was surprisingly great .\n->The steak is good , the fish is good and the sushi was surprisingly great .\n[{'aspect': 'steak', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we were drawn into the belly dancing show that captivated the crowd .\n->we were drawn into the belly dancing show that captivated the crowd .\n[{'aspect': 'belly dancing show', 'opinion': 'captivated', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: bukhara is on my top 5 indian places in nyc\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbukhara is on my top 5 indian places in nyc\n->", + "output": "{\"text\": \"bukhara is on my top 5 indian places in nyc\", \"labels\": \"[{'aspect': 'bukhara', 'opinion': 'top', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: movies look good and the sound is great .\n->movies look good and the sound is great .\n[{'aspect': 'sound', 'opinion': 'great', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: such a disappointment . . .\n->such a disappointment . . .\n[{'aspect': 'NULL', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: wretched and retching\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwretched and retching\n->", + "output": "{\"text\": \"wretched and retching\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'wretched', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'retching', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the second is also a known trouble spot with android , and that is the microsd card .\n->the second is also a known trouble spot with android , and that is the microsd card .\n[{'aspect': 'microsd card', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}]\ntext: i have never been so disgusted by both food an service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have never been so disgusted by both food an service .\n->", + "output": "{\"text\": \"i have never been so disgusted by both food an service .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the entrees were served with miso soup and rice .\n->the entrees were served with miso soup and rice .\n[{'aspect': 'entrees', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: love love love this laptop !\n->love love love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: for starters they delivered us someone else ' s order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor starters they delivered us someone else ' s order .\n->", + "output": "{\"text\": \"for starters they delivered us someone else ' s order .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is so cool and the service is prompt and curtious .\n->The place is so cool and the service is prompt and curtious .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i had to ask her three times before she finally came back with the dish ive requested .\n->i had to ask her three times before she finally came back with the dish ive requested .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n->", + "output": "{\"text\": \"however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\", \"labels\": \"[{'aspect': 'kimchee', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'slimy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'korean fair', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have been to Roth 's twice and both times were very disappointing .\n->I have been to Roth 's twice and both times were very disappointing .\n[{'aspect': \"Roth 's\", 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the portions are large and the servers always surprise us with a different starter .\n->the portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n->", + "output": "{\"text\": \"my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n->despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n[{'aspect': 'modern japanese food', 'opinion': 'go - to for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mirrors', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the laptop runs smoothly and renders larger games quickly .\n->the laptop runs smoothly and renders larger games quickly .\n[{'aspect': 'laptop', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: none was made so i hung up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnone was made so i hung up .\n->", + "output": "{\"text\": \"none was made so i hung up .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you will find yourself returning quite often .\n->you will find yourself returning quite often .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The service was attentive and her suggestions of menu items was right on the mark .\n->The service was attentive and her suggestions of menu items was right on the mark .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu items', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}]\ntext: less than three minutes passed before i found myself doubled over the toilet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nless than three minutes passed before i found myself doubled over the toilet .\n->", + "output": "{\"text\": \"less than three minutes passed before i found myself doubled over the toilet .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n->all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n[{'aspect': 'desktop / application options', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: this unit is extremely well built .\n->this unit is extremely well built .\n[{'aspect': 'unit', 'opinion': 'well built', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n->", + "output": "{\"text\": \"my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in practice , the device is heavier than is comfortable for this .\n->in practice , the device is heavier than is comfortable for this .\n[{'aspect': 'device', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'device', 'opinion': 'than is comfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: with magsafe 2 , even the gentlest pull makes the plug to disconnect , which is very annoying for me .\n->with magsafe 2 , even the gentlest pull makes the plug to disconnect , which is very annoying for me .\n[{'aspect': 'plug', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n->", + "output": "{\"text\": \"it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'repulsive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mousepad is functional but really doesnt get in the way .\n->the mousepad is functional but really doesnt get in the way .\n[{'aspect': 'mousepad', 'opinion': 'functional', 'polarity': 'neutral', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: but asus chromebooks have a fatsl flaw .\n->but asus chromebooks have a fatsl flaw .\n[{'aspect': 'asus chromebooks', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: gorgeous place ideal for a romantic dinner\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngorgeous place ideal for a romantic dinner\n->", + "output": "{\"text\": \"gorgeous place ideal for a romantic dinner\", \"labels\": \"[{'aspect': 'place', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'ideal', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'ideal', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very unsatisfied with warranty service .\n->very unsatisfied with warranty service .\n[{'aspect': 'warranty service', 'opinion': 'unsatisfied', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\nExample:\ntext: great sushi experience .\n->great sushi experience .\n[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i book a gorgeous white organza tent which included a four course prix fix menu which we enjoyed a lot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni book a gorgeous white organza tent which included a four course prix fix menu which we enjoyed a lot .\n->", + "output": "{\"text\": \"i book a gorgeous white organza tent which included a four course prix fix menu which we enjoyed a lot .\", \"labels\": \"[{'aspect': 'four course prix fix menu', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'white organza tent', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the display is clear .\n->the display is clear .\n[{'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Cute place , nice wait staff but would never go there again .\n->Cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i completely recommend casa la femme for any special occasion and to really impress your date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni completely recommend casa la femme for any special occasion and to really impress your date .\n->", + "output": "{\"text\": \"i completely recommend casa la femme for any special occasion and to really impress your date .\", \"labels\": \"[{'aspect': 'casa la femme', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: mmmmm . . . it was delicious .\n->mmmmm . . . it was delicious .\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: more williamsburg garbage\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmore williamsburg garbage\n->", + "output": "{\"text\": \"more williamsburg garbage\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good creative rolls !\n->good creative rolls !\n[{'aspect': 'rolls', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: we ate at this thai place following the reviews but very unhappy with the foods .\n->we ate at this thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the dishes came out around 5 minutes apart .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe dishes came out around 5 minutes apart .\n->", + "output": "{\"text\": \"the dishes came out around 5 minutes apart .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n->i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n[{'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'pita', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hummus', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled octopus', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the retina screen does an excellent job of not tiring my eyes after a long day of computer work .\n->the retina screen does an excellent job of not tiring my eyes after a long day of computer work .\n[{'aspect': 'retina screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the bibimbap was average , but the stone bowl was n ' t even close to sizzling .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bibimbap was average , but the stone bowl was n ' t even close to sizzling .\n->", + "output": "{\"text\": \"the bibimbap was average , but the stone bowl was n ' t even close to sizzling .\", \"labels\": \"[{'aspect': 'bibimbap', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'stone bowl', 'opinion': \"n ' t even close to sizzling\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - boot time , sleep time and wake time are crazy fast .\n->- boot time , sleep time and wake time are crazy fast .\n[{'aspect': 'boot time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'boot time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\nExample:\ntext: the chrome rdp app also works well , connecting to my windows machines with good performance , and i can access files on my nas using network file shares after using the chromebook for the last couple of weeks , i ' d confidently recommend one for everything but gaming and special purpose applications .\n->the chrome rdp app also works well , connecting to my windows machines with good performance , and i can access files on my nas using network file shares after using the chromebook for the last couple of weeks , i ' d confidently recommend one for everything but gaming and special purpose applications .\n[{'aspect': 'chrome rdp app', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chrome rdp app', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: too bad i had paid an extra $ 2 for the stone bowl .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntoo bad i had paid an extra $ 2 for the stone bowl .\n->", + "output": "{\"text\": \"too bad i had paid an extra $ 2 for the stone bowl .\", \"labels\": \"[{'aspect': 'stone bowl', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: plenty of memory and storage .\n->plenty of memory and storage .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: - backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n->- backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n[{'aspect': 'backlit keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit keyboard', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the nakgi - bokum was horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe nakgi - bokum was horrible .\n->", + "output": "{\"text\": \"the nakgi - bokum was horrible .\", \"labels\": \"[{'aspect': 'nakgi - bokum', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n->and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n[{'aspect': 'system', 'opinion': 'not worry', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\nExample:\ntext: everything is either good or excellent in quality .\n->everything is either good or excellent in quality .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: easily the worst stir - fried squid i ' ve ever tasted .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neasily the worst stir - fried squid i ' ve ever tasted .\n->", + "output": "{\"text\": \"easily the worst stir - fried squid i ' ve ever tasted .\", \"labels\": \"[{'aspect': 'stir - fried squid', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n->Looking around , I saw a room full of New Yorkers enjoying a real meal in a real restaurant , not a clubhouse of the fabulous trying to be seen .\n[{'aspect': 'meal', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'real', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !\n->it \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'cute', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the sauce tasted more like chinese fast food than decent korean .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sauce tasted more like chinese fast food than decent korean .\n->", + "output": "{\"text\": \"the sauce tasted more like chinese fast food than decent korean .\", \"labels\": \"[{'aspect': 'sauce', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would recommend this machine to anyone who wants an inexpensive web - content device .\n->i would recommend this machine to anyone who wants an inexpensive web - content device .\n[{'aspect': 'machine', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i also like the display .\n->i also like the display .\n[{'aspect': 'display', 'opinion': 'like', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: the side dishes were passable , and i did get a refill upon request .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe side dishes were passable , and i did get a refill upon request .\n->", + "output": "{\"text\": \"the side dishes were passable , and i did get a refill upon request .\", \"labels\": \"[{'aspect': 'side dishes', 'opinion': 'passable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is also attentive and friendly .\n->The staff is also attentive and friendly .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i should have known better : msi has boot issues , no way around it .\n->i should have known better : msi has boot issues , no way around it .\n[{'aspect': 'msi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MOTHERBOARD#QUALITY'}]\ntext: the real problem i had with this place was the complete lack of service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe real problem i had with this place was the complete lack of service .\n->", + "output": "{\"text\": \"the real problem i had with this place was the complete lack of service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'lack of', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Excellent dumplings served amid clean , chic decor .\n->Excellent dumplings served amid clean , chic decor .\n[{'aspect': 'dumplings', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'clean', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'chic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Of course the reason its so packed is because the food is so delicious !\n->Of course the reason its so packed is because the food is so delicious !\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: she just nodded and walked off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe just nodded and walked off .\n->", + "output": "{\"text\": \"she just nodded and walked off .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great service , great food .\n->great service , great food .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food was bland oily .\n->The food was bland oily .\n[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'NULL'}]\ntext: as to my comment about the food , no apology or acknowledgment was made .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas to my comment about the food , no apology or acknowledgment was made .\n->", + "output": "{\"text\": \"as to my comment about the food , no apology or acknowledgment was made .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: package arrived faster than the estimated arrival .\n->package arrived faster than the estimated arrival .\n[{'aspect': 'package', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\nExample:\ntext: i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n->i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n[{'aspect': 'voltage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: my wife had barely touched that mess of a dish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy wife had barely touched that mess of a dish .\n->", + "output": "{\"text\": \"my wife had barely touched that mess of a dish .\", \"labels\": \"[{'aspect': 'dish', 'opinion': 'mess', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do n ' t judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\n->do n ' t judge this place prima facie , you have to try it to believe it , a home away from home for the literate heart .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The restaurant has a Family feel , not least with regard to the portions which are enormous ; the veal alone could have single-handedly solved third world famine .\n->The restaurant has a Family feel , not least with regard to the portions which are enormous ; the veal alone could have single-handedly solved third world famine .\n[{'aspect': 'restaurant', 'opinion': 'Family feel', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we were charged full price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe were charged full price .\n->", + "output": "{\"text\": \"we were charged full price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'full', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best italian food i ever had ( and being italian , that means alot ) .\n->best italian food i ever had ( and being italian , that means alot ) .\n[{'aspect': 'italian food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n->this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n[{'aspect': 'chromebook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: lives up to the hype\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlives up to the hype\n->", + "output": "{\"text\": \"lives up to the hype\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re daring , try the balsamic vinegar over icecream , it ' s wonderful !\n->if you ' re daring , try the balsamic vinegar over icecream , it ' s wonderful !\n[{'aspect': 'balsamic vinegar over icecream', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'balsamic vinegar over icecream', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: there is really no excuse why it can ' t have one .\n->there is really no excuse why it can ' t have one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: we ' ve tried before but it always packed and does n ' t take reservations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ' ve tried before but it always packed and does n ' t take reservations .\n->", + "output": "{\"text\": \"we ' ve tried before but it always packed and does n ' t take reservations .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n->As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: for instance , plates were just dumped on the table , i was handed the wine list upside down , etc . . . .\n->for instance , plates were just dumped on the table , i was handed the wine list upside down , etc . . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it was well worth the wait .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was well worth the wait .\n->", + "output": "{\"text\": \"it was well worth the wait .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good price , good quality and good service in korea .\n->good price , good quality and good service in korea .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the force touch trackpad works great .\n->the force touch trackpad works great .\n[{'aspect': 'force touch trackpad', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\ntext: the wife had the risotto which was amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wife had the risotto which was amazing .\n->", + "output": "{\"text\": \"the wife had the risotto which was amazing .\", \"labels\": \"[{'aspect': 'risotto', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the kitchen however , is almost always slow .\n->the kitchen however , is almost always slow .\n[{'aspect': 'kitchen', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n->the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: it does n ' t look appetizing as it ' s covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit does n ' t look appetizing as it ' s covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\n->", + "output": "{\"text\": \"it does n ' t look appetizing as it ' s covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen resolution super ( retina ) .\n->screen resolution super ( retina ) .\n[{'aspect': 'screen resolution', 'opinion': 'super', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: the m3 is great , never slow or laggy .\n->the m3 is great , never slow or laggy .\n[{'aspect': 'm3', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'm3', 'opinion': 'never slow or laggy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the farro salad and the mashed yukon potatoes were also extremely tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe farro salad and the mashed yukon potatoes were also extremely tasty .\n->", + "output": "{\"text\": \"the farro salad and the mashed yukon potatoes were also extremely tasty .\", \"labels\": \"[{'aspect': 'farro salad', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mashed yukon potatoes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Anyway , the food is good , the price is right and they have a decent wine list .\n->Anyway , the food is good , the price is right and they have a decent wine list .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was very impressed with its sleek design and the speed of the processor itself .\n->i was very impressed with its sleek design and the speed of the processor itself .\n[{'aspect': 'design', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'processor', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: i love margherita pizza \u2013 i looove east village pizza\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love margherita pizza \u2013 i looove east village pizza\n->", + "output": "{\"text\": \"i love margherita pizza \u2013 i looove east village pizza\", \"labels\": \"[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n->i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n[{'aspect': 'chromebook', 'opinion': 'enthusiast', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Service was very good - prompt , attentive and non-intrusive .\n->Service was very good - prompt , attentive and non-intrusive .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love this place , every time we are in the city this is one of the places we always go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove this place , every time we are in the city this is one of the places we always go .\n->", + "output": "{\"text\": \"love this place , every time we are in the city this is one of the places we always go .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city .\n->The food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a quintessential slice of nyc pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na quintessential slice of nyc pizza .\n->", + "output": "{\"text\": \"a quintessential slice of nyc pizza .\", \"labels\": \"[{'aspect': 'slice of nyc pizza', 'opinion': 'quintessential', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n->The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Cafe Noir', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: however it can happen at random , for example when signing into gmail .\n->however it can happen at random , for example when signing into gmail .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n->", + "output": "{\"text\": \"the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\", \"labels\": \"[{'aspect': 'crust', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'light', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it also has much better performance , including an easily upgraded m .\n->it also has much better performance , including an easily upgraded m .\n[{'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: we are very happy with the lenovo laptop .\n->we are very happy with the lenovo laptop .\n[{'aspect': 'lenovo laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: personally i like the margherita pizza better , but they are all good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npersonally i like the margherita pizza better , but they are all good .\n->", + "output": "{\"text\": \"personally i like the margherita pizza better , but they are all good .\", \"labels\": \"[{'aspect': 'margherita pizza', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - long battery life\n->- long battery life\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n->the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n[{'aspect': 'meat', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauces', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchi', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'salad', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'meal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: possibly the most romantic restaurant in the city\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npossibly the most romantic restaurant in the city\n->", + "output": "{\"text\": \"possibly the most romantic restaurant in the city\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is average .\n->service is average .\n[{'aspect': 'service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: despite the minor case marks , i can heartily recommend this chromebook based on features , design , and operation .\n->despite the minor case marks , i can heartily recommend this chromebook based on features , design , and operation .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n->", + "output": "{\"text\": \"this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\", \"labels\": \"[{'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n->for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: the food was exceptional .\n->the food was exceptional .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate decor will make you feel like you \u2019 re no longer in the city .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate decor will make you feel like you \u2019 re no longer in the city .\n->", + "output": "{\"text\": \"not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate decor will make you feel like you \u2019 re no longer in the city .\", \"labels\": \"[{'aspect': 'spot', 'opinion': 'hidden', 'polarity': 'neutral', 'category': 'LOCATION#GENERAL'}, {'aspect': 'unmarked wooden doors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n->received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The service was a bit slow , but they were very friendly .\n->The service was a bit slow , but they were very friendly .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'negative', 'category': 'NULL'}]\ntext: if you \u2019 re planning to come here , make sure that your date is someone whom you really like since you \u2019 ll be ushered to private booths where there will be no people or food watching ( choose the ones on the ground level that have glass ceilings so you may see the stars in the sky ! ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you \u2019 re planning to come here , make sure that your date is someone whom you really like since you \u2019 ll be ushered to private booths where there will be no people or food watching ( choose the ones on the ground level that have glass ceilings so you may see the stars in the sky ! ) .\n->", + "output": "{\"text\": \"if you \u2019 re planning to come here , make sure that your date is someone whom you really like since you \u2019 ll be ushered to private booths where there will be no people or food watching ( choose the ones on the ground level that have glass ceilings so you may see the stars in the sky ! ) .\", \"labels\": \"[{'aspect': 'private booths', 'opinion': 'ushered', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'glass ceilings', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The well mannered , pleasant staff that Tony has in his employ .\n->The well mannered , pleasant staff that Tony has in his employ .\n[{'aspect': 'staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the second the screen did not rotate .\n->the second the screen did not rotate .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: it \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !\n->", + "output": "{\"text\": \"it \u2019 s just you and your date and an occasional cute \u2018 excuse me \u2019 before the waiter opens the little curtain to your booth !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'cute', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sadly after barely a year old it crashes all the time , the touch screen rarely works , and the track pad stops working until a reboot on occasion .\n->sadly after barely a year old it crashes all the time , the touch screen rarely works , and the track pad stops working until a reboot on occasion .\n[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n->this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n->", + "output": "{\"text\": \"my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\", \"labels\": \"[{'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is expensive but well worth the money .\n->it is expensive but well worth the money .\n[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: touch - screen features are responsive .\n->touch - screen features are responsive .\n[{'aspect': 'touch - screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: we started off with a delightful sashimi amuse bouche .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe started off with a delightful sashimi amuse bouche .\n->", + "output": "{\"text\": \"we started off with a delightful sashimi amuse bouche .\", \"labels\": \"[{'aspect': 'sashimi amuse bouche', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 've been to several places for Dim Sum and this has got to be the WORST .\n->I 've been to several places for Dim Sum and this has got to be the WORST .\n[{'aspect': 'Dim Sum', 'opinion': 'WORST .', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Some of the workers ignore me and talk to the female customers , other times , they 've skipped my order .\n->Some of the workers ignore me and talk to the female customers , other times , they 've skipped my order .\n[{'aspect': 'workers', 'opinion': 'ignore', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'order', 'opinion': 'skipped', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i picked the grilled black cod as my entree , which i absolutely devoured while someone commented that the grilled salmon dish was better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni picked the grilled black cod as my entree , which i absolutely devoured while someone commented that the grilled salmon dish was better .\n->", + "output": "{\"text\": \"i picked the grilled black cod as my entree , which i absolutely devoured while someone commented that the grilled salmon dish was better .\", \"labels\": \"[{'aspect': 'grilled black cod', 'opinion': 'devoured', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled salmon dish', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !\n->Not sure where the previous reviewer , lonk , dined , but Saul is in a great neighborhood and has great food !\n[{'aspect': 'neighborhood', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what more can you ask for ?\n->what more can you ask for ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the entrees were served with miso soup and rice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe entrees were served with miso soup and rice .\n->", + "output": "{\"text\": \"the entrees were served with miso soup and rice .\", \"labels\": \"[{'aspect': 'entrees', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Indoor was very cozy and cute .\n->Indoor was very cozy and cute .\n[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n->As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: for desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .\n->", + "output": "{\"text\": \"for desserts , we tried the frozen black sesame mousse ( interesting but not extraordinary ) and matcha ( powdered green tea ) and blueberry cheesecake , which was phenomenal .\", \"labels\": \"[{'aspect': 'frozen black sesame mousse', 'opinion': 'interesting', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'frozen black sesame mousse', 'opinion': 'extraordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'matcha ( powdered green tea ) and blueberry cheesecake', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen turn black and won ' t turn on within a month rarely use .\n->screen turn black and won ' t turn on within a month rarely use .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: Ive been here a bunch of times now and the service is always outstanding .\n->Ive been here a bunch of times now and the service is always outstanding .\n[{'aspect': 'service', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n->", + "output": "{\"text\": \"i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\", \"labels\": \"[{'aspect': 'zenkichi', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'unhurried', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\n->But that was n't the icing on the cake : a tiramisu that resembled nothing I have ever had .\n[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is a piece of garbage .\n->this is a piece of garbage .\n[{'aspect': 'NULL', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: terrible would be a compliment !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nterrible would be a compliment !\n->", + "output": "{\"text\": \"terrible would be a compliment !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to me it exemplifies soho , cute , artsy , interesting .\n->to me it exemplifies soho , cute , artsy , interesting .\n[{'aspect': 'NULL', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'artsy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: mousepad is a little wonky if you ' re not deliberate with your fingers , recommend using a mouse .\n->mousepad is a little wonky if you ' re not deliberate with your fingers , recommend using a mouse .\n[{'aspect': 'mousepad', 'opinion': 'wonky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: the service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .\n->", + "output": "{\"text\": \"the service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the cpu runs super fast and doesn ' t start utilizing its full potential until you start doing things like installation of applications , but the memory usage sits around 40 - 50 % the most of the time .\n->the cpu runs super fast and doesn ' t start utilizing its full potential until you start doing things like installation of applications , but the memory usage sits around 40 - 50 % the most of the time .\n[{'aspect': 'cpu', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: To sum it up : Service varies from good to mediorce , depending on which waiter you get ; generally it is just average Ok .\n->To sum it up : Service varies from good to mediorce , depending on which waiter you get ; generally it is just average Ok .\n[{'aspect': 'Service', 'opinion': 'varies', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: we paid a fixed pricce but got nothing ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe paid a fixed pricce but got nothing ! !\n->", + "output": "{\"text\": \"we paid a fixed pricce but got nothing ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was so glad that the problem would finally be fixed .\n->i was so glad that the problem would finally be fixed .\n[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}]\nExample:\ntext: i made the mistake of thinking it was new .\n->i made the mistake of thinking it was new .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n->", + "output": "{\"text\": \"we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'tired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is plenty of room for positives , it looks sharp , runs great and is the perfect size for traveling and working on the go .\n->there is plenty of room for positives , it looks sharp , runs great and is the perfect size for traveling and working on the go .\n[{'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'traveling', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: on that scale , it ' s a world - beater .\n->on that scale , it ' s a world - beater .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: they are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !\n->", + "output": "{\"text\": \"they are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'drinks', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}, {'aspect': 'pita bread', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stay far away from this laptop !\n->stay far away from this laptop !\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: stay away\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstay away\n->", + "output": "{\"text\": \"stay away\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n->The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n[{'aspect': 'food', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portioins', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: one of the things that drew me to the c302 was the convertible form - factor .\n->one of the things that drew me to the c302 was the convertible form - factor .\n[{'aspect': 'c302', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: great shabu shabu\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat shabu shabu\n->", + "output": "{\"text\": \"great shabu shabu\", \"labels\": \"[{'aspect': 'shabu shabu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely glad i purchased my mac .\n->definitely glad i purchased my mac .\n[{'aspect': 'mac', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: This is one great place to eat pizza more out but not a good place for take-out pizza .\n->This is one great place to eat pizza more out but not a good place for take-out pizza .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'take-out pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love it .\n->", + "output": "{\"text\": \"i love it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tech support is useless .\n->tech support is useless .\n[{'aspect': 'tech support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: not having to switch to / from desktop version of websites is great .\n->not having to switch to / from desktop version of websites is great .\n[{'aspect': 'websites', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: but for the shabu shabu , you wo n ' t find much better in ny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut for the shabu shabu , you wo n ' t find much better in ny .\n->", + "output": "{\"text\": \"but for the shabu shabu , you wo n ' t find much better in ny .\", \"labels\": \"[{'aspect': 'shabu shabu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a dreadful little piece of machinery .\n->this is a dreadful little piece of machinery .\n[{'aspect': 'machinery', 'opinion': 'dreadful', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i will never buy another asus after this experience .\n->i will never buy another asus after this experience .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n->", + "output": "{\"text\": \"the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\", \"labels\": \"[{'aspect': 'meat', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauces', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchi', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'salad', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'meal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + play store compatibility is available now .\n->+ play store compatibility is available now .\n[{'aspect': 'play store', 'opinion': 'available', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: also upon turning it on , i got a blue screen .\n->also upon turning it on , i got a blue screen .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: dokebi gives williamsburg the right one - two punch of classic korean food and fusion twists like pork belly tacos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndokebi gives williamsburg the right one - two punch of classic korean food and fusion twists like pork belly tacos .\n->", + "output": "{\"text\": \"dokebi gives williamsburg the right one - two punch of classic korean food and fusion twists like pork belly tacos .\", \"labels\": \"[{'aspect': 'korean food', 'opinion': 'classic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fusion twists', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork belly tacos', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is decent .\n->the food is decent .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n->we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the hot dogs are good , yes , but the reason to get over here is the fantastic pork croquette sandwich , perfect on its supermarket squishy bun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hot dogs are good , yes , but the reason to get over here is the fantastic pork croquette sandwich , perfect on its supermarket squishy bun .\n->", + "output": "{\"text\": \"the hot dogs are good , yes , but the reason to get over here is the fantastic pork croquette sandwich , perfect on its supermarket squishy bun .\", \"labels\": \"[{'aspect': 'hot dogs', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork croquette sandwich', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bun', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n->on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power', 'opinion': 'failed', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n->Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: restaurant with a view\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nrestaurant with a view\n->", + "output": "{\"text\": \"restaurant with a view\", \"labels\": \"[{'aspect': 'view', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LOCATION#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was lousy - too sweet or too salty and the portions tiny .\n->The food was lousy - too sweet or too salty and the portions tiny .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: finally a reliable chinese restaurant !\n->finally a reliable chinese restaurant !\n[{'aspect': 'chinese restaurant', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the food tasted very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food tasted very good .\n->", + "output": "{\"text\": \"the food tasted very good .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was the friendliest that have seen in New York .\n->The staff was the friendliest that have seen in New York .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the pixels are currently stuck .\n->the pixels are currently stuck .\n[{'aspect': 'pixels', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the main entree was also very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe main entree was also very good .\n->", + "output": "{\"text\": \"the main entree was also very good .\", \"labels\": \"[{'aspect': 'main entree', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: granddaughter so loves it said it ' s the best christmas present ever\n->granddaughter so loves it said it ' s the best christmas present ever\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the waiter was attentive .\n->the waiter was attentive .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: price is high but the food is good , so i would come back again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprice is high but the food is good , so i would come back again .\n->", + "output": "{\"text\": \"price is high but the food is good , so i would come back again .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'high', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when we sat , we got great and fast service .\n->when we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it has been great for everything i ' ve done .\n->it has been great for everything i ' ve done .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this place does n ' t make any sense\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place does n ' t make any sense\n->", + "output": "{\"text\": \"this place does n ' t make any sense\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , this is where the build quality really shines .\n->again , this is where the build quality really shines .\n[{'aspect': 'build quality', 'opinion': 'shines', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: great chromebook .\n->great chromebook .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: just want to warn you all - do n ' t waste your time and money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust want to warn you all - do n ' t waste your time and money .\n->", + "output": "{\"text\": \"just want to warn you all - do n ' t waste your time and money .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: awsome pizza especially the margheritta slice .\n->awsome pizza especially the margheritta slice .\n[{'aspect': 'pizza', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margheritta slice', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it is very light weight .\n->it is very light weight .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: this place has totally weird decor , stairs going up with mirrored walls - i am surprised how no one yet broke their head or fall off the stairs - mirrored walls make you dizzy and delusional . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place has totally weird decor , stairs going up with mirrored walls - i am surprised how no one yet broke their head or fall off the stairs - mirrored walls make you dizzy and delusional . . .\n->", + "output": "{\"text\": \"this place has totally weird decor , stairs going up with mirrored walls - i am surprised how no one yet broke their head or fall off the stairs - mirrored walls make you dizzy and delusional . . .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'weird', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'mirrored walls', 'opinion': 'dizzy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'mirrored walls', 'opinion': 'delusional', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Save your money and do n't waste your calories , go to Margharita 's on Washington Street instead , they have amazing food and the BEST service .\n->Save your money and do n't waste your calories , go to Margharita 's on Washington Street instead , they have amazing food and the BEST service .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Leon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .\n->Leon is an East Village gem : casual but hip , with well prepared basic French bistro fare , good specials , a warm and lively atmosphere .\n[{'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'lively', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'French bistro fare', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this place is not inviting and the food is totally weird .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place is not inviting and the food is totally weird .\n->", + "output": "{\"text\": \"this place is not inviting and the food is totally weird .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'not inviting', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n->all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n[{'aspect': 'web browsing', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great pizza for lunch place .\n->Great pizza for lunch place .\n[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the concept of japanese tapas is newly created and clearly does n ' t work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe concept of japanese tapas is newly created and clearly does n ' t work .\n->", + "output": "{\"text\": \"the concept of japanese tapas is newly created and clearly does n ' t work .\", \"labels\": \"[{'aspect': 'japanese tapas', 'opinion': \"does n ' t work\", 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n->i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: it ' s a good enough laptop .\n->it ' s a good enough laptop .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: good food\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood food\n->", + "output": "{\"text\": \"good food\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this laptop .\n->i love this laptop .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the actual laptop is very much darker and blue .\n->the actual laptop is very much darker and blue .\n[{'aspect': 'actual laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the food was great and tasty , but the sitting space was too small , i do n ' t like being cramp in a corner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food was great and tasty , but the sitting space was too small , i do n ' t like being cramp in a corner .\n->", + "output": "{\"text\": \"the food was great and tasty , but the sitting space was too small , i do n ' t like being cramp in a corner .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sitting space', 'opinion': 'too small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: quacamole at pacifico is yummy , as are the wings with chimmichuri .\n->quacamole at pacifico is yummy , as are the wings with chimmichuri .\n[{'aspect': 'quacamole', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wings with chimmichuri', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: every additional feature would be unnecessary for my personal usage .\n->every additional feature would be unnecessary for my personal usage .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: over all it was a very nice romantic place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nover all it was a very nice romantic place .\n->", + "output": "{\"text\": \"over all it was a very nice romantic place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however it can happen at random , for example when signing into gmail .\n->however it can happen at random , for example when signing into gmail .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: There was no ambiance .\n->There was no ambiance .\n[{'aspect': 'ambiance', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}]\ntext: loved it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nloved it\n->", + "output": "{\"text\": \"loved it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: No food snobs allowed , this place is for people who appreciate good food .\n->No food snobs allowed , this place is for people who appreciate good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n->you can get a table without a reservation if you get there early i they do n ' t make you by bottles .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: a coworker and i tried pacifico after work a few fridays and loved it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na coworker and i tried pacifico after work a few fridays and loved it .\n->", + "output": "{\"text\": \"a coworker and i tried pacifico after work a few fridays and loved it .\", \"labels\": \"[{'aspect': 'pacifico', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try their chef 's specials -- they are to die for .\n->Try their chef 's specials -- they are to die for .\n[{'aspect': \"chef 's specials\", 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"chef 's specials\", 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n->We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n[{'aspect': 'quality', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'care', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the atmosphere was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe atmosphere was great .\n->", + "output": "{\"text\": \"the atmosphere was great .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n->As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the machine is near - unusable out of the box .\n->the machine is near - unusable out of the box .\n[{'aspect': 'machine', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the food we ordered was excellent , although i would n ' t say the margaritas were anything to write home about .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food we ordered was excellent , although i would n ' t say the margaritas were anything to write home about .\n->", + "output": "{\"text\": \"the food we ordered was excellent , although i would n ' t say the margaritas were anything to write home about .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margaritas', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n->everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n[{'aspect': 'atmosphere', 'opinion': 'raved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rooms', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'views', 'opinion': 'incomparable', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i have never been disappointed in the red eye .\n->i have never been disappointed in the red eye .\n[{'aspect': 'red eye', 'opinion': 'never been disappointed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: our waitress was n ' t mean , but not especially warm or attentive either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour waitress was n ' t mean , but not especially warm or attentive either .\n->", + "output": "{\"text\": \"our waitress was n ' t mean , but not especially warm or attentive either .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': \"n ' t mean\", 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitress', 'opinion': 'not especially warm or attentive', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: please be aware that it ' s cash or amex only !\n->please be aware that it ' s cash or amex only !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n->the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n[{'aspect': 'soy sauce', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'vinegar-soaked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i must say i am surprised by the bad reviews of the restaurant earlier in the year , though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni must say i am surprised by the bad reviews of the restaurant earlier in the year , though .\n->", + "output": "{\"text\": \"i must say i am surprised by the bad reviews of the restaurant earlier in the year , though .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'bad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: terrible would be a compliment !\n->terrible would be a compliment !\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: We waited at the bar and had martinis that were just right .\n->We waited at the bar and had martinis that were just right .\n[{'aspect': 'martinis', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}]\ntext: regardless , we ' ll be back and ca n ' t wait to visit in the summer to take advantage of the patio .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nregardless , we ' ll be back and ca n ' t wait to visit in the summer to take advantage of the patio .\n->", + "output": "{\"text\": \"regardless , we ' ll be back and ca n ' t wait to visit in the summer to take advantage of the patio .\", \"labels\": \"[{'aspect': 'patio', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff was the friendliest that have seen in new york .\n->the staff was the friendliest that have seen in new york .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: wifi radio loses signal too frequently .\n->wifi radio loses signal too frequently .\n[{'aspect': 'wifi radio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: dumbfoundingly poor\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndumbfoundingly poor\n->", + "output": "{\"text\": \"dumbfoundingly poor\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'poor', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The quantity is also very good , you will come out satisfied .\n->The quantity is also very good , you will come out satisfied .\n[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: performance wise , this is an excellent machine - it has a beautiful touchscreen , plenty of storage , converts to a tablet , and seamlessly connects to the google play store to run any of the millions of android apps available .\n->performance wise , this is an excellent machine - it has a beautiful touchscreen , plenty of storage , converts to a tablet , and seamlessly connects to the google play store to run any of the millions of android apps available .\n[{'aspect': 'touchscreen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\ntext: this was , from start to finish , a mind - bogglingly uncomfortable experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was , from start to finish , a mind - bogglingly uncomfortable experience .\n->", + "output": "{\"text\": \"this was , from start to finish , a mind - bogglingly uncomfortable experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its fast , light weight , quiet , and looks easy to add additional ram and hdd ' s .\n->its fast , light weight , quiet , and looks easy to add additional ram and hdd ' s .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the keyboard is easy to use , and there is no external noise to contend with .\n->the keyboard is easy to use , and there is no external noise to contend with .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n->", + "output": "{\"text\": \"lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'rudeness', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer literally blue screened on the second day because system 32 was corrupt .\n->the computer literally blue screened on the second day because system 32 was corrupt .\n[{'aspect': 'system 32', 'opinion': 'corrupt', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: super easy to use .\n->super easy to use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n->", + "output": "{\"text\": \"the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\", \"labels\": \"[{'aspect': 'servers', 'opinion': 'perfected', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service ok but unfriendly , filthy bathroom .\n->service ok but unfriendly , filthy bathroom .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'bathroom', 'opinion': 'filthy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n->Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: polite acknowledgement is out ;\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npolite acknowledgement is out ;\n->", + "output": "{\"text\": \"polite acknowledgement is out ;\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love how quick this thing is .\n->i love how quick this thing is .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: i absolutely love this chromebook .\n->i absolutely love this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: and how many times can you pick up the same perfectly aligned set of napkins , inspect them vapidly and plonk them down in exactly the same place instead of venturing a glance at people who are there to help you make the rent ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand how many times can you pick up the same perfectly aligned set of napkins , inspect them vapidly and plonk them down in exactly the same place instead of venturing a glance at people who are there to help you make the rent ?\n->", + "output": "{\"text\": \"and how many times can you pick up the same perfectly aligned set of napkins , inspect them vapidly and plonk them down in exactly the same place instead of venturing a glance at people who are there to help you make the rent ?\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food always tastes fresh and served promptly .\n->The food always tastes fresh and served promptly .\n[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the flip and touchscreen aspects work fine , no problems .\n->the flip and touchscreen aspects work fine , no problems .\n[{'aspect': 'flip', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n->", + "output": "{\"text\": \"a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\", \"labels\": \"[{'aspect': 'server', 'opinion': 'enhanced', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is the best for the price in my opinion .\n->this laptop is the best for the price in my opinion .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the computer ' s hardware is decent , but the materials are poor .\n->the computer ' s hardware is decent , but the materials are poor .\n[{'aspect': \"computer ' s hardware\", 'opinion': 'decent', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'materials', 'opinion': 'poor', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: overall the food quality was pretty good , though i hear the salmon is much better when it has n ' t sat cooling in front of the guest .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall the food quality was pretty good , though i hear the salmon is much better when it has n ' t sat cooling in front of the guest .\n->", + "output": "{\"text\": \"overall the food quality was pretty good , though i hear the salmon is much better when it has n ' t sat cooling in front of the guest .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the thing is fantastic .\n->the thing is fantastic .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: prices too high for this cramped and unappealing resturant .\n->prices too high for this cramped and unappealing resturant .\n[{'aspect': 'resturant', 'opinion': 'high', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'resturant', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'resturant', 'opinion': 'unappealing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: the place has a nice fit - out , some attractive furnishings and , from what i could tell , a reasonable wine list ( i was given the food menu when i asked for the carte des vins )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe place has a nice fit - out , some attractive furnishings and , from what i could tell , a reasonable wine list ( i was given the food menu when i asked for the carte des vins )\n->", + "output": "{\"text\": \"the place has a nice fit - out , some attractive furnishings and , from what i could tell , a reasonable wine list ( i was given the food menu when i asked for the carte des vins )\", \"labels\": \"[{'aspect': 'fit - out', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'furnishings', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'wine list', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the second the screen did not rotate .\n->the second the screen did not rotate .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n->and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n[{'aspect': 'google environment', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: at $ 120 for two people , however , this in no way represents value , unless you ' re looking to pay by the hour for passive - aggressive torture .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat $ 120 for two people , however , this in no way represents value , unless you ' re looking to pay by the hour for passive - aggressive torture .\n->", + "output": "{\"text\": \"at $ 120 for two people , however , this in no way represents value , unless you ' re looking to pay by the hour for passive - aggressive torture .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'passive - aggressive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: give it a try and enjoy .\n->give it a try and enjoy .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: - the touchscreen is very responsive , fast , and so far everything has scaled just fine during use\n->- the touchscreen is very responsive , fast , and so far everything has scaled just fine during use\n[{'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: on that scale , it ' s a world - beater .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non that scale , it ' s a world - beater .\n->", + "output": "{\"text\": \"on that scale , it ' s a world - beater .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n->the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n[{'aspect': 'battery life', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'longevity', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: best indian restaurant in the city\n->best indian restaurant in the city\n[{'aspect': 'indian restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: how is this palce still open ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhow is this palce still open ?\n->", + "output": "{\"text\": \"how is this palce still open ?\", \"labels\": \"[{'aspect': 'palce', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at $ 120 for two people , however , this in no way represents value , unless you ' re looking to pay by the hour for passive - aggressive torture .\n->at $ 120 for two people , however , this in no way represents value , unless you ' re looking to pay by the hour for passive - aggressive torture .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'passive - aggressive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: sometimes had to do several times , but thought it might be an idiosyncrady of this model .\n->sometimes had to do several times , but thought it might be an idiosyncrady of this model .\n[{'aspect': 'model', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: i honestly do n ' t even know where to begin .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni honestly do n ' t even know where to begin .\n->", + "output": "{\"text\": \"i honestly do n ' t even know where to begin .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The best pad thai i 've ever had .\n->The best pad thai i 've ever had .\n[{'aspect': 'pad thai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The Thali was small , thoroughly unremarkable , and $ 14.95 .\n->The Thali was small , thoroughly unremarkable , and $ 14.95 .\n[{'aspect': 'Thali', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Thali', 'opinion': 'unremarkable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: we arrived and were seated immediately , which made us both happy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe arrived and were seated immediately , which made us both happy .\n->", + "output": "{\"text\": \"we arrived and were seated immediately , which made us both happy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: note : i haven ' t had any issues with the touchscreen at all .\n->note : i haven ' t had any issues with the touchscreen at all .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: we were very disappointed .\n->we were very disappointed .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: everything was going good until we got our meals .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything was going good until we got our meals .\n->", + "output": "{\"text\": \"everything was going good until we got our meals .\", \"labels\": \"[{'aspect': 'meals', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: drinks way over priced .\n->drinks way over priced .\n[{'aspect': 'drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: called customer service twice only to learn that i need to return old charger ( i pay $ 15 to return ) .\n->called customer service twice only to learn that i need to return old charger ( i pay $ 15 to return ) .\n[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: i took one look at the chicken and i was appalled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni took one look at the chicken and i was appalled .\n->", + "output": "{\"text\": \"i took one look at the chicken and i was appalled .\", \"labels\": \"[{'aspect': 'chicken', 'opinion': 'appalled', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Should you happen to be impressed by the cuisine definitely try it .\n->Should you happen to be impressed by the cuisine definitely try it .\n[{'aspect': 'cuisine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n->i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n[{'aspect': 'computer', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'computer', 'opinion': 'defective', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n->", + "output": "{\"text\": \"it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\", \"labels\": \"[{'aspect': 'spinach', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In the evening , this place attracted a well dressed , with it , NY crowd .\n->In the evening , this place attracted a well dressed , with it , NY crowd .\n[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: seriously , this place kicks ass .\n->seriously , this place kicks ass .\n[{'aspect': 'place', 'opinion': 'kicks ass', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i took one bite from the $ 24 salmon , and i have never , in the 17 years i have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in flatbush farms .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni took one bite from the $ 24 salmon , and i have never , in the 17 years i have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in flatbush farms .\n->", + "output": "{\"text\": \"i took one bite from the $ 24 salmon , and i have never , in the 17 years i have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in flatbush farms .\", \"labels\": \"[{'aspect': 'salmon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'salmon', 'opinion': 'fishy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n->we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'delight', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: love these chromebooks !\n->love these chromebooks !\n[{'aspect': 'chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n->", + "output": "{\"text\": \"at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great little laptop .\n->great little laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the home screen is blank with a customizable photo that you can add but that ' s it .\n->the home screen is blank with a customizable photo that you can add but that ' s it .\n[{'aspect': 'home screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: so , i switch with my boyfriend again to see if maybe i could stomach the meat and spinach again , but the spinach was so undercooked that i just could not bite through it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso , i switch with my boyfriend again to see if maybe i could stomach the meat and spinach again , but the spinach was so undercooked that i just could not bite through it .\n->", + "output": "{\"text\": \"so , i switch with my boyfriend again to see if maybe i could stomach the meat and spinach again , but the spinach was so undercooked that i just could not bite through it .\", \"labels\": \"[{'aspect': 'spinach', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For authentic Thai food , look no further than Toons .\n->For authentic Thai food , look no further than Toons .\n[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: android aspect is no longer ` ` beta , ` ` and gotten / still getting better .\n->android aspect is no longer ` ` beta , ` ` and gotten / still getting better .\n[{'aspect': 'android', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n->", + "output": "{\"text\": \"i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - display glass not glued well on one side .\n->- display glass not glued well on one side .\n[{'aspect': 'display glass', 'opinion': 'not glued well', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so i decide to report back to the waitress because it was completely inedible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso i decide to report back to the waitress because it was completely inedible .\n->", + "output": "{\"text\": \"so i decide to report back to the waitress because it was completely inedible .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The cafe itself was really nice with comfortable outdoor chairs and tables , but the service could have been better .\n->The cafe itself was really nice with comfortable outdoor chairs and tables , but the service could have been better .\n[{'aspect': 'cafe', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor chairs', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i am still disastified even if this was a replacement\n->i am still disastified even if this was a replacement\n[{'aspect': 'replacement', 'opinion': 'disastified', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: guess what , i waited for twenty minutes before she came over and when she finally did , she says , ` ` oh well , i wish you would have said something earlier ' ' no apology , nothing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nguess what , i waited for twenty minutes before she came over and when she finally did , she says , ` ` oh well , i wish you would have said something earlier ' ' no apology , nothing .\n->", + "output": "{\"text\": \"guess what , i waited for twenty minutes before she came over and when she finally did , she says , ` ` oh well , i wish you would have said something earlier ' ' no apology , nothing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is great .\n->the build quality is great .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: told us to sit anywhere , and when we sat he said the table was reserved .\n->told us to sit anywhere , and when we sat he said the table was reserved .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: after that she simply took our plates , walked away , came back another twenty minutes later with the bill and the chicken on it ! ! ! ! ! ! ! ! ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter that she simply took our plates , walked away , came back another twenty minutes later with the bill and the chicken on it ! ! ! ! ! ! ! ! ! ! ! !\n->", + "output": "{\"text\": \"after that she simply took our plates , walked away , came back another twenty minutes later with the bill and the chicken on it ! ! ! ! ! ! ! ! ! ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I liked the food at this quasi-thai restaurant .\n->I liked the food at this quasi-thai restaurant .\n[{'aspect': 'food', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i absolutely love everything about this chromebook .\n->i absolutely love everything about this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: no desert menu , no apology , nothing ! ! ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno desert menu , no apology , nothing ! ! ! ! ! !\n->", + "output": "{\"text\": \"no desert menu , no apology , nothing ! ! ! ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n->The wait staff is pleasant , fun , and for the most part gorgeous ( in the wonderful aesthetic beautification way , not in that she's-way-cuter-than-me-that-b @ # $ * way ) .\n[{'aspect': 'wait staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n->this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n[{'aspect': 'restaurant', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: this is where it really really gets bad : the manager said , there is absolutely nothing we can do , it ' s a matter of taste that she did n ' t like it , and i can not comp it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is where it really really gets bad : the manager said , there is absolutely nothing we can do , it ' s a matter of taste that she did n ' t like it , and i can not comp it .\n->", + "output": "{\"text\": \"this is where it really really gets bad : the manager said , there is absolutely nothing we can do , it ' s a matter of taste that she did n ' t like it , and i can not comp it .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was prompt and courteous .\n->Service was prompt and courteous .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great item by apple as usual .\n->great item by apple as usual .\n[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: again , no apology , no is there anything else i can get you , no can i get you a drink to make up for it , nothing ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nagain , no apology , no is there anything else i can get you , no can i get you a drink to make up for it , nothing ! ! ! !\n->", + "output": "{\"text\": \"again , no apology , no is there anything else i can get you , no can i get you a drink to make up for it , nothing ! ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Sure , the setting is nice .\n->Sure , the setting is nice .\n[{'aspect': 'setting', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n->the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: the level of rudeness was preposterous .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe level of rudeness was preposterous .\n->", + "output": "{\"text\": \"the level of rudeness was preposterous .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'preposterous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n->a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n[{'aspect': 'gentleman', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: my husbands was perfect , my was well done and dry .\n->my husbands was perfect , my was well done and dry .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'well done', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the manager came to the table and said we can do what we want , so we paid for what we did enjoy , the drinks and appetizers , and walked out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe manager came to the table and said we can do what we want , so we paid for what we did enjoy , the drinks and appetizers , and walked out .\n->", + "output": "{\"text\": \"the manager came to the table and said we can do what we want , so we paid for what we did enjoy , the drinks and appetizers , and walked out .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'drinks', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'appetizers', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life short\n->battery life short\n[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: best . sushi . ever .\n->best . sushi . ever .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i have never ever had such an unpleasant experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have never ever had such an unpleasant experience .\n->", + "output": "{\"text\": \"i have never ever had such an unpleasant experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: note that this laptop is only 45 daysish old .\n->note that this laptop is only 45 daysish old .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n->samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n[{'aspect': 'stylus', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'oem stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: this staff should be fired .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis staff should be fired .\n->", + "output": "{\"text\": \"this staff should be fired .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'fired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n->The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spaghetti with Scallops and Shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: cirspy crust margherita pizza\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncirspy crust margherita pizza\n->", + "output": "{\"text\": \"cirspy crust margherita pizza\", \"labels\": \"[{'aspect': 'margherita pizza', 'opinion': 'cirspy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'crust', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's charmingly small and that leads to an atmoshere that is extremely cozy and romantic , even .\n->It 's charmingly small and that leads to an atmoshere that is extremely cozy and romantic , even .\n[{'aspect': 'atmoshere', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshere', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is my first chromebook purchase and i have to say that i ' m enjoying the speed and simplicity of it .\n->this is my first chromebook purchase and i have to say that i ' m enjoying the speed and simplicity of it .\n[{'aspect': 'chromebook', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it was really good pizza .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was really good pizza .\n->", + "output": "{\"text\": \"it was really good pizza .\", \"labels\": \"[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with ssd lightning fast start up\n->with ssd lightning fast start up\n[{'aspect': 'ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n->i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the crust was imazingly cooked well and pizza was fully loaded : ) : ) : )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe crust was imazingly cooked well and pizza was fully loaded : ) : ) : )\n->", + "output": "{\"text\": \"the crust was imazingly cooked well and pizza was fully loaded : ) : ) : )\", \"labels\": \"[{'aspect': 'crust', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'fully loaded', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this restaurant is vegetarian ; there are no meat dishes whatsoever .\n->this restaurant is vegetarian ; there are no meat dishes whatsoever .\n[{'aspect': 'meat dishes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: Restaurant snobs need not bother , this is a small , neighborhood kind of place .\n->Restaurant snobs need not bother , this is a small , neighborhood kind of place .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you know what i mean all the positives things happening there made mw write this review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou know what i mean all the positives things happening there made mw write this review .\n->", + "output": "{\"text\": \"you know what i mean all the positives things happening there made mw write this review .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'positives', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n->nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n[{'aspect': 'mac os x', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: the screen is plenty big and the visual very nice .\n->the screen is plenty big and the visual very nice .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n->", + "output": "{\"text\": \"i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'pita', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hummus', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled octopus', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I like the ambience , it 's very dark and original .\n->I like the ambience , it 's very dark and original .\n[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the price is reasonable although the service is poor .\n->the price is reasonable although the service is poor .\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: if i could give 0 stars i would do so for this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif i could give 0 stars i would do so for this place .\n->", + "output": "{\"text\": \"if i could give 0 stars i would do so for this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Oh , do n't even let me start with how expensive the bills were !\n->Oh , do n't even let me start with how expensive the bills were !\n[{'aspect': 'bills', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: their designs are made for phones and on this huge screen , they are palpitated .\n->their designs are made for phones and on this huge screen , they are palpitated .\n[{'aspect': 'designs', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n->", + "output": "{\"text\": \"the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is very attentive and we can almost always get a table .\n->The staff is very attentive and we can almost always get a table .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i do n ' t think 16 gb is enough .\n->i do n ' t think 16 gb is enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\ntext: well . . . they can run but they ca n ' t hide .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwell . . . they can run but they ca n ' t hide .\n->", + "output": "{\"text\": \"well . . . they can run but they ca n ' t hide .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n->despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n[{'aspect': 'modern japanese food', 'opinion': 'go - to for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mirrors', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i bought this laptop for software development .\n->i bought this laptop for software development .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: this place . . . god where do i begin .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis place . . . god where do i begin .\n->", + "output": "{\"text\": \"this place . . . god where do i begin .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - beautiful , bright ips screen with full 1080p resolution\n->- beautiful , bright ips screen with full 1080p resolution\n[{'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .\n->We went here for lunch a couple of weeks ago on a Saturday , and I was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the decor however seems to be the distraction so you wo n ' t notice that you just payed 300 bucks for some cold eggplant that took 2 frickin hours to come ! ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe decor however seems to be the distraction so you wo n ' t notice that you just payed 300 bucks for some cold eggplant that took 2 frickin hours to come ! ! ! !\n->", + "output": "{\"text\": \"the decor however seems to be the distraction so you wo n ' t notice that you just payed 300 bucks for some cold eggplant that took 2 frickin hours to come ! ! ! !\", \"labels\": \"[{'aspect': 'decor', 'opinion': 'distraction', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'eggplant', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'eggplant', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Sauce was watery and the food did n't have much flavor .\n->Sauce was watery and the food did n't have much flavor .\n[{'aspect': 'Sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The worst excuse for Japanese food I 've ever encountered .\n->The worst excuse for Japanese food I 've ever encountered .\n[{'aspect': 'Japanese food', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: great hot dogs !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat hot dogs !\n->", + "output": "{\"text\": \"great hot dogs !\", \"labels\": \"[{'aspect': 'hot dogs', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will never buy an msi product again , and will tell every person i know to stay far away .\n->will never buy an msi product again , and will tell every person i know to stay far away .\n[{'aspect': 'msi product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: Moules were excellent , lobster ravioli was VERY salty !\n->Moules were excellent , lobster ravioli was VERY salty !\n[{'aspect': 'Moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hot dogs are top notch , and they ' re slamwich is amazing !\n->", + "output": "{\"text\": \"the hot dogs are top notch , and they ' re slamwich is amazing !\", \"labels\": \"[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are the best bagels I 've had .\n->They are the best bagels I 've had .\n[{'aspect': 'bagels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is very helpful and it is very fast .\n->it is very helpful and it is very fast .\n[{'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: going to bark is always worth the train ride , and will make your tongue and belly very happy !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoing to bark is always worth the train ride , and will make your tongue and belly very happy !\n->", + "output": "{\"text\": \"going to bark is always worth the train ride , and will make your tongue and belly very happy !\", \"labels\": \"[{'aspect': 'bark', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n->so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: so disappointing to receive the laptop and it wouldn ' t even power up .\n->so disappointing to receive the laptop and it wouldn ' t even power up .\n[{'aspect': 'laptop', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: only complaint is the pricing - - i believe it would be more reasonable to pay a dollar less on each item listed on the menu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly complaint is the pricing - - i believe it would be more reasonable to pay a dollar less on each item listed on the menu .\n->", + "output": "{\"text\": \"only complaint is the pricing - - i believe it would be more reasonable to pay a dollar less on each item listed on the menu .\", \"labels\": \"[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price it is an amazing starting point and hard to beat especially with such an amazing brand such as asus .\n->for the price it is an amazing starting point and hard to beat especially with such an amazing brand such as asus .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: it ' s very crisp and responsive .\n->it ' s very crisp and responsive .\n[{'aspect': 'NULL', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: fabulous food - if the front of house staff do n ' t put you off \u2013\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfabulous food - if the front of house staff do n ' t put you off \u2013\n->", + "output": "{\"text\": \"fabulous food - if the front of house staff do n ' t put you off \u2013\", \"labels\": \"[{'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'front of house staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n->food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork belly', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: speed : it ' s very fast .\n->speed : it ' s very fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n->", + "output": "{\"text\": \"it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will not recommend it .\n->will not recommend it .\n[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: loved it\n->loved it\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: each time we ' ve been , the front of house staff ( not the waiters - they ' re fantastic - but the people who greet and seat you ) has been so hideous to us that were it not for the exceptional fish dishes i would never return .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neach time we ' ve been , the front of house staff ( not the waiters - they ' re fantastic - but the people who greet and seat you ) has been so hideous to us that were it not for the exceptional fish dishes i would never return .\n->", + "output": "{\"text\": \"each time we ' ve been , the front of house staff ( not the waiters - they ' re fantastic - but the people who greet and seat you ) has been so hideous to us that were it not for the exceptional fish dishes i would never return .\", \"labels\": \"[{'aspect': 'waiters', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'front of house staff', 'opinion': 'hideous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'fish dishes', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: surprisingly nothing could be further from the truth .\n->surprisingly nothing could be further from the truth .\n[{'aspect': 'NULL', 'opinion': 'surprisingly', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: as bfc does n ' t take reservations you almost always have to wait by the bar - and be abused by the front of house staff until you are seated , which can be over an hour later !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas bfc does n ' t take reservations you almost always have to wait by the bar - and be abused by the front of house staff until you are seated , which can be over an hour later !\n->", + "output": "{\"text\": \"as bfc does n ' t take reservations you almost always have to wait by the bar - and be abused by the front of house staff until you are seated , which can be over an hour later !\", \"labels\": \"[{'aspect': 'bfc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'front of house staff', 'opinion': 'abused', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'abused', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'secret back room', 'opinion': 'Check out', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good display\n->good display\n[{'aspect': 'display', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: the frizzy retro girl ( with winged / dame edna glasses ) will yell at you if you try to order a drink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe frizzy retro girl ( with winged / dame edna glasses ) will yell at you if you try to order a drink .\n->", + "output": "{\"text\": \"the frizzy retro girl ( with winged / dame edna glasses ) will yell at you if you try to order a drink .\", \"labels\": \"[{'aspect': 'girl', 'opinion': 'frizzy', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazon , as well as the google suite of docs , sheets , slides , and photos work great .\n->amazon , as well as the google suite of docs , sheets , slides , and photos work great .\n[{'aspect': 'amazon', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google suite', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the concept of japanese tapas is newly created and clearly does n ' t work .\n->the concept of japanese tapas is newly created and clearly does n ' t work .\n[{'aspect': 'japanese tapas', 'opinion': \"does n ' t work\", 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: i was almost amused by the fact that she was turning away customers at 9pm on a friday night because she ` ` had a bbq to go to ' ' that night - wtf ? ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was almost amused by the fact that she was turning away customers at 9pm on a friday night because she ` ` had a bbq to go to ' ' that night - wtf ? ?\n->", + "output": "{\"text\": \"i was almost amused by the fact that she was turning away customers at 9pm on a friday night because she ` ` had a bbq to go to ' ' that night - wtf ? ?\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was absolutely horrible !\n->The food was absolutely horrible !\n[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the back garden sitting area is very pleasant , where you can see their personal herb garden .\n->the back garden sitting area is very pleasant , where you can see their personal herb garden .\n[{'aspect': 'back garden sitting area', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i ' d be horrified if my staff were turning away customers so early and so rudely !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' d be horrified if my staff were turning away customers so early and so rudely !\n->", + "output": "{\"text\": \"i ' d be horrified if my staff were turning away customers so early and so rudely !\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'horrified', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it feels wonderful to finally say that about an acer display .\n->it feels wonderful to finally say that about an acer display .\n[{'aspect': 'acer display', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Terrific menu full of unique rolls and special dishes .\n->Terrific menu full of unique rolls and special dishes .\n[{'aspect': 'menu', 'opinion': 'Terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\ntext: there ' s another girl who i ca n ' t describe , she is about 5 ' 6 ' ' with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you ' re * really * talking about .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere ' s another girl who i ca n ' t describe , she is about 5 ' 6 ' ' with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you ' re * really * talking about .\n->", + "output": "{\"text\": \"there ' s another girl who i ca n ' t describe , she is about 5 ' 6 ' ' with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you ' re * really * talking about .\", \"labels\": \"[{'aspect': 'girl', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The appetizers are just OK and the main courses were decidedly subpar .\n->The appetizers are just OK and the main courses were decidedly subpar .\n[{'aspect': 'appetizers', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'main courses', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The service is a little scatty at times but all is forgiven when the food arrives .\n->The service is a little scatty at times but all is forgiven when the food arrives .\n[{'aspect': 'service', 'opinion': 'scatty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'forgiven', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n->", + "output": "{\"text\": \"i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nasty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n->The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n[{'aspect': 'in-house lady DJ', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: When he finally did , he was unable to make a gin and tonic -- could n't find tonic .\n->When he finally did , he was unable to make a gin and tonic -- could n't find tonic .\n[{'aspect': 'gin and tonic', 'opinion': 'unable', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: again , i ' d be super upset if that were my employee .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nagain , i ' d be super upset if that were my employee .\n->", + "output": "{\"text\": \"again , i ' d be super upset if that were my employee .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fabulous decor - makes you feel like you ' re in a trendy manhattan restaurant , very very good food , cheaply - priced , generally friendly staff , and if you ' re a manhattanite , or spend most of your time in manhattan , rice avenue will make you feel at home . . . . . very soho / village / upper west side minus the expensive prices and pretentious clientele . . . . . all on roosevelt avenue !\n->fabulous decor - makes you feel like you ' re in a trendy manhattan restaurant , very very good food , cheaply - priced , generally friendly staff , and if you ' re a manhattanite , or spend most of your time in manhattan , rice avenue will make you feel at home . . . . . very soho / village / upper west side minus the expensive prices and pretentious clientele . . . . . all on roosevelt avenue !\n[{'aspect': 'decor', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheaply - priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n->the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n[{'aspect': '4gb / 32gb version', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': '4gb / 32gb version', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage space', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: considering you will spend at least $ 60 a head , i expect better service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nconsidering you will spend at least $ 60 a head , i expect better service .\n->", + "output": "{\"text\": \"considering you will spend at least $ 60 a head , i expect better service .\", \"labels\": \"[{'aspect': 'service', 'opinion': 'expect better', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s pretty light , too , so it ' s easy to travel with .\n->it ' s pretty light , too , so it ' s easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: A touch more jalapeno heat for contrast and it would have been very good indeed .\n->A touch more jalapeno heat for contrast and it would have been very good indeed .\n[{'aspect': 'jalapeno', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\n->", + "output": "{\"text\": \"so , if you ' re walking by and thinking about dining , you might want to see who ' s going to be seating you first . . .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mizu is home to creative and unique rolls not to found anywhere else .\n->mizu is home to creative and unique rolls not to found anywhere else .\n[{'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: I love Indian food and consider myself to be quite an expert on it .\n->I love Indian food and consider myself to be quite an expert on it .\n[{'aspect': 'Indian food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: maitre - d - ' ' eat and get out ' '\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmaitre - d - ' ' eat and get out ' '\n->", + "output": "{\"text\": \"maitre - d - ' ' eat and get out ' '\", \"labels\": \"[{'aspect': 'maitre - d', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop does n ' t work .\n->the laptop does n ' t work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n->The menu looked good , except for offering the Chilean Sea Bass , but the server does not offer up the specials that were written on the board outside .\n[{'aspect': 'menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Chilean Sea Bass', 'opinion': 'except', 'polarity': 'negative', 'category': 'NULL'}]\ntext: while finishing our meals which included a high - end bottle of wine , our son ' s fiance joined us for a glass of wine and dessert .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile finishing our meals which included a high - end bottle of wine , our son ' s fiance joined us for a glass of wine and dessert .\n->", + "output": "{\"text\": \"while finishing our meals which included a high - end bottle of wine , our son ' s fiance joined us for a glass of wine and dessert .\", \"labels\": \"[{'aspect': 'bottle of wine', 'opinion': 'high - end', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it came exactly as advertised .\n->it came exactly as advertised .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n->the menu looked great , and the waiter was very nice , but when the food came , it was average .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: this guy refused to seat her and she left , followed shortly by the four of us , but not before i told him that in my 40 years of world travel , including paris , that i had never seen such a display of bad behavior by a frontman in a restaurant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis guy refused to seat her and she left , followed shortly by the four of us , but not before i told him that in my 40 years of world travel , including paris , that i had never seen such a display of bad behavior by a frontman in a restaurant .\n->", + "output": "{\"text\": \"this guy refused to seat her and she left , followed shortly by the four of us , but not before i told him that in my 40 years of world travel , including paris , that i had never seen such a display of bad behavior by a frontman in a restaurant .\", \"labels\": \"[{'aspect': 'frontman', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Mizu is home to creative and unique rolls not to found anywhere else .\n->Mizu is home to creative and unique rolls not to found anywhere else .\n[{'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: picked this up as something light and easy to carry around for working on personal coding projects while riding the bus .\n->picked this up as something light and easy to carry around for working on personal coding projects while riding the bus .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: his response was smug , arrogant , and condescending , totally consistent with his deportment on display all evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhis response was smug , arrogant , and condescending , totally consistent with his deportment on display all evening .\n->", + "output": "{\"text\": \"his response was smug , arrogant , and condescending , totally consistent with his deportment on display all evening .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'smug', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'condescending', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is alright - some stuff is good - some is not ( like the steak dish which tends to be dry ) .\n->The food is alright - some stuff is good - some is not ( like the steak dish which tends to be dry ) .\n[{'aspect': 'steak dish', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is a great notebook .\n->this is a great notebook .\n[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: if you go here , do it on his off - night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you go here , do it on his off - night .\n->", + "output": "{\"text\": \"if you go here , do it on his off - night .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , this is where the build quality really shines .\n->again , this is where the build quality really shines .\n[{'aspect': 'build quality', 'opinion': 'shines', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: very cozy and warm inside . . . . .\n->very cozy and warm inside . . . . .\n[{'aspect': 'NULL', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: best meal in a long time !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest meal in a long time !\n->", + "output": "{\"text\": \"best meal in a long time !\", \"labels\": \"[{'aspect': 'meal', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I always get the Shabu-Shabu dinner and the beef is always fresh .\n->I always get the Shabu-Shabu dinner and the beef is always fresh .\n[{'aspect': 'beef', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m saving up for my next visit .\n->i ' m saving up for my next visit .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: mussles and calamari were superb saturday evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmussles and calamari were superb saturday evening .\n->", + "output": "{\"text\": \"mussles and calamari were superb saturday evening .\", \"labels\": \"[{'aspect': 'mussles', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'calamari', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is nice to type on .\n->the keyboard is nice to type on .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: the waiter was attentive .\n->the waiter was attentive .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: my father had the flank steak which was very good , and my mother had the swordfish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy father had the flank steak which was very good , and my mother had the swordfish .\n->", + "output": "{\"text\": \"my father had the flank steak which was very good , and my mother had the swordfish .\", \"labels\": \"[{'aspect': 'flank steak', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n->sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n[{'aspect': 'keyboard', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'android app support', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the other is excellent !\n->the other is excellent !\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the four seasons restaurant is a great experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe four seasons restaurant is a great experience .\n->", + "output": "{\"text\": \"the four seasons restaurant is a great experience .\", \"labels\": \"[{'aspect': 'the four seasons restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n->i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: excellent lapto , just as they show it .\n->excellent lapto , just as they show it .\n[{'aspect': 'lapto', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the food is great and the environment is even better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is great and the environment is even better .\n->", + "output": "{\"text\": \"the food is great and the environment is even better .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'environment', 'opinion': 'better', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our family never expected such incredible entertainment in a restaurant .\n->Our family never expected such incredible entertainment in a restaurant .\n[{'aspect': 'entertainment', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food arrived 20 minutes after I called , cold and soggy .\n->The food arrived 20 minutes after I called , cold and soggy .\n[{'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: everyone must come here at least once .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neveryone must come here at least once .\n->", + "output": "{\"text\": \"everyone must come here at least once .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: everyone was cheerfully cooperative and helpful .\n->everyone was cheerfully cooperative and helpful .\n[{'aspect': 'NULL', 'opinion': 'cheerfully cooperative', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: highly recommend it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly recommend it !\n->", + "output": "{\"text\": \"highly recommend it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n->Everything is always cooked to perfection , the service is excellent , the decor cool and understated .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'understated', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: up until this point , asus chromebooks have been my favorite .\n->up until this point , asus chromebooks have been my favorite .\n[{'aspect': 'asus chromebooks', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: taking hot dogs to the next level\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntaking hot dogs to the next level\n->", + "output": "{\"text\": \"taking hot dogs to the next level\", \"labels\": \"[{'aspect': 'hot dogs', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After really enjoying ourselves at the bar we sat down at a table and had dinner .\n->After really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'table', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'enjoying', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: absolutely terrible quality control to not even get past the initial boot .\n->absolutely terrible quality control to not even get past the initial boot .\n[{'aspect': 'quality control', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: at first glance this place seems a bit pricey for a hot dog joint , but at bark you do n ' t just get your average hot dog .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat first glance this place seems a bit pricey for a hot dog joint , but at bark you do n ' t just get your average hot dog .\n->", + "output": "{\"text\": \"at first glance this place seems a bit pricey for a hot dog joint , but at bark you do n ' t just get your average hot dog .\", \"labels\": \"[{'aspect': 'hot dog', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bark', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food , good size menu , great service and an unpretensious setting .\n->Great food , good size menu , great service and an unpretensious setting .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'good size', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'unpretensious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n->Yes you have to wait to be seated and because its small there is no waiting area and the seat at the bar was all taken .\n[{'aspect': 'waiting area', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seat', 'opinion': 'all taken', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: here the hot dog is elevated to the level of a real entree with numerous variations available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhere the hot dog is elevated to the level of a real entree with numerous variations available .\n->", + "output": "{\"text\": \"here the hot dog is elevated to the level of a real entree with numerous variations available .\", \"labels\": \"[{'aspect': 'hot dog', 'opinion': 'elevated', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dog', 'opinion': 'numerous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n->yes , the place is classy and beautiful , but they most certainly target the uber whealthy not the common joe that wants to go all out every once in a while .\n[{'aspect': 'place', 'opinion': 'classy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: quiet keyboard .\n->quiet keyboard .\n[{'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: great atmosphere\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat atmosphere\n->", + "output": "{\"text\": \"great atmosphere\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n->calling the place hampton chutney co . does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'room', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'clerks', 'opinion': 'unhelpful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: we were ushered to the bar to wait momentarily and upon arrival were so excited .\n->we were ushered to the bar to wait momentarily and upon arrival were so excited .\n[{'aspect': 'NULL', 'opinion': 'excited', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i highly recommend the fish tacos , everything else was ok .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend the fish tacos , everything else was ok .\n->", + "output": "{\"text\": \"i highly recommend the fish tacos , everything else was ok .\", \"labels\": \"[{'aspect': 'fish tacos', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: my main complaint is the battery life , i see many positive reviews about the battery life .\n->my main complaint is the battery life , i see many positive reviews about the battery life .\n[{'aspect': 'battery life', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .\n->", + "output": "{\"text\": \"cool atmosphere , the fire place in the back really ads to it but needs a bit more heat throughout on a cold night .\", \"labels\": \"[{'aspect': 'atmosphere', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'fire place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would however spend $ 300 on this device .\n->i would however spend $ 300 on this device .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n->Raga stands out with an interesting fusion of French and Indian cooking .\n[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: poor service and management\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npoor service and management\n->", + "output": "{\"text\": \"poor service and management\", \"labels\": \"[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n->i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n[{'aspect': 'apple support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: usb - c connectors on both sides can both charge , making the power cord location an option .\n->usb - c connectors on both sides can both charge , making the power cord location an option .\n[{'aspect': 'usb - c connectors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: had an awful experience at casa la femme on a saturday dinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhad an awful experience at casa la femme on a saturday dinner .\n->", + "output": "{\"text\": \"had an awful experience at casa la femme on a saturday dinner .\", \"labels\": \"[{'aspect': 'casa la femme', 'opinion': 'awful', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n[{'aspect': 'appetizer selection', 'opinion': 'complaints', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n->We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n[{'aspect': 'scenery', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner reservations', 'opinion': 'early', 'polarity': 'positive', 'category': 'NULL'}]\ntext: appetizers took nearly an hour .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nappetizers took nearly an hour .\n->", + "output": "{\"text\": \"appetizers took nearly an hour .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what you are paying for is the environment and the name .\n->what you are paying for is the environment and the name .\n[{'aspect': 'environment', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: could have been a great computer if not for the terrible keyboard construction .\n->could have been a great computer if not for the terrible keyboard construction .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard construction', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: never got an explanation as to what was going on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnever got an explanation as to what was going on .\n->", + "output": "{\"text\": \"never got an explanation as to what was going on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the drag and drop works poorly which is very annoying .\n->the drag and drop works poorly which is very annoying .\n[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: our waitress was n ' t mean , but not especially warm or attentive either .\n->our waitress was n ' t mean , but not especially warm or attentive either .\n[{'aspect': 'waitress', 'opinion': \"n ' t mean\", 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitress', 'opinion': 'not especially warm or attentive', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: the manager was rude and handled the situation extremely poorly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe manager was rude and handled the situation extremely poorly .\n->", + "output": "{\"text\": \"the manager was rude and handled the situation extremely poorly .\", \"labels\": \"[{'aspect': 'manager', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'manager', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mousepad was not very responsive .\n->the mousepad was not very responsive .\n[{'aspect': 'mousepad', 'opinion': 'not very responsive', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: keep up the good work .\n->keep up the good work .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: on the way out , we heard of other guests complaining about similar issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non the way out , we heard of other guests complaining about similar issues .\n->", + "output": "{\"text\": \"on the way out , we heard of other guests complaining about similar issues .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'complaining', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will painfully learn the new pads .\n->i will painfully learn the new pads .\n[{'aspect': 'pads', 'opinion': 'painfully', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: the only draw back with this pc is the battery life which lasts about 3 hrs before needing to be charged .\n->the only draw back with this pc is the battery life which lasts about 3 hrs before needing to be charged .\n[{'aspect': 'battery life', 'opinion': 'draw back', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: can \u2019 t believe how an expensive nyc restaurant can be so disrespectful to its clients .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncan \u2019 t believe how an expensive nyc restaurant can be so disrespectful to its clients .\n->", + "output": "{\"text\": \"can \u2019 t believe how an expensive nyc restaurant can be so disrespectful to its clients .\", \"labels\": \"[{'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'disrespectful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n->we ordered the chicken casserole , but what we got were a few small pieces of chicken , all dark meat and on the bone .\n[{'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chicken casserole', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i don ' t know how to use a lot of computers but this is simple and easy to use .\n->i don ' t know how to use a lot of computers but this is simple and easy to use .\n[{'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: what a hassle !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat a hassle !\n->", + "output": "{\"text\": \"what a hassle !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'hassle', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bruschetta and panini 's are so yummy !\n->The bruschetta and panini 's are so yummy !\n[{'aspect': 'bruschetta', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'panini', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this issue happens more frequently when using netflix ( app or through chrome browser ) .\n->this issue happens more frequently when using netflix ( app or through chrome browser ) .\n[{'aspect': 'happens', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: the food is very good , but not outstanding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe food is very good , but not outstanding .\n->", + "output": "{\"text\": \"the food is very good , but not outstanding .\", \"labels\": \"[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'not outstanding', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: picks up wireless signals weakly !\n->picks up wireless signals weakly !\n[{'aspect': 'wireless signals', 'opinion': 'weakly', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: Their tuna tartar appetizer is to die for .\n->Their tuna tartar appetizer is to die for .\n[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\ntext: there is no way it justifies the accolades it receives , the attitude of the staff or the wait for a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is no way it justifies the accolades it receives , the attitude of the staff or the wait for a table .\n->", + "output": "{\"text\": \"there is no way it justifies the accolades it receives , the attitude of the staff or the wait for a table .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * keyboard * - the keyboard is alright .\n->* keyboard * - the keyboard is alright .\n[{'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: that ' s how confident i am in the asus after 10 days .\n->that ' s how confident i am in the asus after 10 days .\n[{'aspect': 'asus', 'opinion': 'confident', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n->", + "output": "{\"text\": \"on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this worked ok for about a year and then just totally died .\n->this worked ok for about a year and then just totally died .\n[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'died', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n->- backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n[{'aspect': 'backlit keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit keyboard', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: mistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine . . . but not the grunt that we received from the al di la staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine . . . but not the grunt that we received from the al di la staff .\n->", + "output": "{\"text\": \"mistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine . . . but not the grunt that we received from the al di la staff .\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very happy with this purchase .\n->i am very happy with this purchase .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i pray it stays open forever .\n->i pray it stays open forever .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: expensive\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexpensive\n->", + "output": "{\"text\": \"expensive\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was easy to set up .\n->it was easy to set up .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the veal was incredible last night .\n->the veal was incredible last night .\n[{'aspect': 'veal', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the pasta was well cooked , did n ' t have enough sauce though or flavor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pasta was well cooked , did n ' t have enough sauce though or flavor .\n->", + "output": "{\"text\": \"the pasta was well cooked , did n ' t have enough sauce though or flavor .\", \"labels\": \"[{'aspect': 'pasta', 'opinion': 'well cooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thank you everyone at water ' s edge .\n->thank you everyone at water ' s edge .\n[{'aspect': \"water ' s edge\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: try the lobster teriyaki and the rose special roll .\n->try the lobster teriyaki and the rose special roll .\n[{'aspect': 'lobster teriyaki', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rose special roll', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: so rude ! ! !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso rude ! ! !\n->", + "output": "{\"text\": \"so rude ! ! !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for $ 389 on cyber monday 2017 .\n->i bought this for $ 389 on cyber monday 2017 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Someone else recommended the dessert - we also left that .\n->Someone else recommended the dessert - we also left that .\n[{'aspect': 'dessert', 'opinion': 'recommended', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i was one of the people that went for this horrible experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was one of the people that went for this horrible experience .\n->", + "output": "{\"text\": \"i was one of the people that went for this horrible experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good food : my favorite is the seafood spaghetti .\n->good food : my favorite is the seafood spaghetti .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood spaghetti', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: overall , this thing can do very basic stuff , sometimes .\n->overall , this thing can do very basic stuff , sometimes .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: the hostess was rude and i got a distinct feeling that they did not want to serve us .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hostess was rude and i got a distinct feeling that they did not want to serve us .\n->", + "output": "{\"text\": \"the hostess was rude and i got a distinct feeling that they did not want to serve us .\", \"labels\": \"[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keys feel nice and responsive however the mouse pad is a little over responsive .\n->the keys feel nice and responsive however the mouse pad is a little over responsive .\n[{'aspect': 'keys', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keys', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'mouse pad', 'opinion': 'over responsive', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is tasty and portion sizes are appropriate .\n->The food is tasty and portion sizes are appropriate .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the only thing that my friend left out is that when we sat down at the bar the bartender disappeared .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing that my friend left out is that when we sat down at the bar the bartender disappeared .\n->", + "output": "{\"text\": \"the only thing that my friend left out is that when we sat down at the bar the bartender disappeared .\", \"labels\": \"[{'aspect': 'bartender', 'opinion': 'disappeared', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu is limited but almost all of the dishes are excellent .\n->The menu is limited but almost all of the dishes are excellent .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you can watch 1080p video on it and it looks great .\n->you can watch 1080p video on it and it looks great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: i asked for a menu and the same waitress looked at my like i was insane .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni asked for a menu and the same waitress looked at my like i was insane .\n->", + "output": "{\"text\": \"i asked for a menu and the same waitress looked at my like i was insane .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: What is even better , is that the prices are very affordable as well , and the food is really good .\n->What is even better , is that the prices are very affordable as well , and the food is really good .\n[{'aspect': 'prices', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i don ' t know how to use a lot of computers but this is simple and easy to use .\n->i don ' t know how to use a lot of computers but this is simple and easy to use .\n[{'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: i was shocked that my friends wanted to stay after the waitress said , ` ` can i help you ' ' and ` ` how many are in your party . ' '\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was shocked that my friends wanted to stay after the waitress said , ` ` can i help you ' ' and ` ` how many are in your party . ' '\n->", + "output": "{\"text\": \"i was shocked that my friends wanted to stay after the waitress said , ` ` can i help you ' ' and ` ` how many are in your party . ' '\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , i wonder if my unit is defective because most reviews talk about this laptop having a 6 + hour battery life .\n->again , i wonder if my unit is defective because most reviews talk about this laptop having a 6 + hour battery life .\n[{'aspect': 'unit', 'opinion': 'defective', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n->lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n[{'aspect': 'touch pad', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: the three of us standing in front of her should have been an indication of how many of us there were .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe three of us standing in front of her should have been an indication of how many of us there were .\n->", + "output": "{\"text\": \"the three of us standing in front of her should have been an indication of how many of us there were .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - it does slow down noticeably if you ' re doing too much at once .\n->- it does slow down noticeably if you ' re doing too much at once .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: there is really no excuse why it can ' t have one .\n->there is really no excuse why it can ' t have one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: we did n ' t look like the other patrons in there so unfortunately i think that may have been part of the problem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe did n ' t look like the other patrons in there so unfortunately i think that may have been part of the problem .\n->", + "output": "{\"text\": \"we did n ' t look like the other patrons in there so unfortunately i think that may have been part of the problem .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: usb - c connectors on both sides can both charge , making the power cord location an option .\n->usb - c connectors on both sides can both charge , making the power cord location an option .\n[{'aspect': 'usb - c connectors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\nExample:\ntext: the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n->the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n[{'aspect': 'charging port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: bad staff\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbad staff\n->", + "output": "{\"text\": \"bad staff\", \"labels\": \"[{'aspect': 'staff', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: drawbacks : service is slow and they do n ' t toast !\n->drawbacks : service is slow and they do n ' t toast !\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i go out to eat and like my courses , servers are patient and never rush courses or force another drink .\n->i go out to eat and like my courses , servers are patient and never rush courses or force another drink .\n[{'aspect': 'servers', 'opinion': 'patient', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i generally like this place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni generally like this place .\n->", + "output": "{\"text\": \"i generally like this place .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the coolest thing is the touch screen on something this size .\n->the coolest thing is the touch screen on something this size .\n[{'aspect': 'touch screen', 'opinion': 'coolest', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: it took hours to restore to factory default settings , and it crashed once again days later .\n->it took hours to restore to factory default settings , and it crashed once again days later .\n[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the design of the space is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe design of the space is good .\n->", + "output": "{\"text\": \"the design of the space is good .\", \"labels\": \"[{'aspect': 'space', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n->to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n[{'aspect': 'samsung', 'opinion': 'doubtful', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\nExample:\ntext: i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n->i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: but the service is horrid !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the service is horrid !\n->", + "output": "{\"text\": \"but the service is horrid !\", \"labels\": \"[{'aspect': 'service', 'opinion': 'horrid', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n->i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n[{'aspect': 'machine', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i ' m glad i was introduced to this place and this is a rare gem in ny .\n->i ' m glad i was introduced to this place and this is a rare gem in ny .\n[{'aspect': 'place', 'opinion': 'glad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i was there for brunch recently , and we were tag teamed by a waitress and a waiter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was there for brunch recently , and we were tag teamed by a waitress and a waiter .\n->", + "output": "{\"text\": \"i was there for brunch recently , and we were tag teamed by a waitress and a waiter .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'teamed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'teamed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: touch - screen features are responsive .\n->touch - screen features are responsive .\n[{'aspect': 'touch - screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: all the staff is absolutely professional ! !\n->all the staff is absolutely professional ! !\n[{'aspect': 'staff', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .\n->", + "output": "{\"text\": \"the waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .\", \"labels\": \"[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Le Pere Pinard has a $ 15 pre-theater menu that is outstanding .\n->Le Pere Pinard has a $ 15 pre-theater menu that is outstanding .\n[{'aspect': 'pre-theater menu', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: camera is sd but not a problem .\n->camera is sd but not a problem .\n[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\ntext: i ca n ' t believe that it was , but please put the bag down before delivering food !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ca n ' t believe that it was , but please put the bag down before delivering food !\n->", + "output": "{\"text\": \"i ca n ' t believe that it was , but please put the bag down before delivering food !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: since you have access to android apps on the google play store , you don ' t have to rely solely on the chrome browser to access apps .\n->since you have access to android apps on the google play store , you don ' t have to rely solely on the chrome browser to access apps .\n[{'aspect': 'android apps', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: they have it all - - great price , food , and service .\n->they have it all - - great price , food , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: the waitress came to check in on us every few minutes , and began to clear the plates while half of us were still eating ( a big pet peeve of mine that happens almost everywhere , so i try to ignore it ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe waitress came to check in on us every few minutes , and began to clear the plates while half of us were still eating ( a big pet peeve of mine that happens almost everywhere , so i try to ignore it ) .\n->", + "output": "{\"text\": \"the waitress came to check in on us every few minutes , and began to clear the plates while half of us were still eating ( a big pet peeve of mine that happens almost everywhere , so i try to ignore it ) .\", \"labels\": \"[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the base model went up in price $ 400 , which excludes any performance benefits .\n->the base model went up in price $ 400 , which excludes any performance benefits .\n[{'aspect': 'base model', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: first it took us a long time to find the place .\n->first it took us a long time to find the place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n->", + "output": "{\"text\": \"i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchscreen and the hotkeys along the top take some getting used to but i was quickly in the google play store , downloading all the apps i have on my phone and linking them to my accounts .\n->the touchscreen and the hotkeys along the top take some getting used to but i was quickly in the google play store , downloading all the apps i have on my phone and linking them to my accounts .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'hotkeys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: it ' s so easy to travel with .\n->it ' s so easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: she then put the check down without asking if we were done , and came to check on the bill every two minutes , even though we were one of three occupied tables .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe then put the check down without asking if we were done , and came to check on the bill every two minutes , even though we were one of three occupied tables .\n->", + "output": "{\"text\": \"she then put the check down without asking if we were done , and came to check on the bill every two minutes , even though we were one of three occupied tables .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is a lot of fun .\n->the place is a lot of fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: so far , this chromebook is fantastic .\n->so far , this chromebook is fantastic .\n[{'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i wish i could like this place more , and i wish someone would retrain the staff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wish i could like this place more , and i wish someone would retrain the staff .\n->", + "output": "{\"text\": \"i wish i could like this place more , and i wish someone would retrain the staff .\", \"labels\": \"[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a brand i look for that i feel i can trust .\n->this is a brand i look for that i feel i can trust .\n[{'aspect': 'brand', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: other than that , i like it , and this is my first chromebook .\n->other than that , i like it , and this is my first chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: acer wants $ 170 to just look at it then add the repair cost on top of that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer wants $ 170 to just look at it then add the repair cost on top of that .\n->", + "output": "{\"text\": \"acer wants $ 170 to just look at it then add the repair cost on top of that .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service is friendly , prices are good - delivery time was a little slow , but for the way this pizza tastes , I 'm willing to overlook it .\n->Service is friendly , prices are good - delivery time was a little slow , but for the way this pizza tastes , I 'm willing to overlook it .\n[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery time', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: as of this writing , the computer ' s dedicated video card is completely non - functional , the computer routinely switches off in the middle of executing a process , and i can ' t even use the hdmi out port .\n->as of this writing , the computer ' s dedicated video card is completely non - functional , the computer routinely switches off in the middle of executing a process , and i can ' t even use the hdmi out port .\n[{'aspect': \"computer ' s dedicated video card\", 'opinion': 'non - functional', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hdmi out port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: update : i repaired it myself for $ 12 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupdate : i repaired it myself for $ 12 .\n->", + "output": "{\"text\": \"update : i repaired it myself for $ 12 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Normally that would be improper , however they were all delicious and my host did not complain .\n->Normally that would be improper , however they were all delicious and my host did not complain .\n[{'aspect': 'host', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The Yellowtail was particularly good as well .\n->The Yellowtail was particularly good as well .\n[{'aspect': 'Yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i had nothing to lose since it was a paper weight otherwise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had nothing to lose since it was a paper weight otherwise .\n->", + "output": "{\"text\": \"i had nothing to lose since it was a paper weight otherwise .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend it .\n->i highly recommend it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n->my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n[{'aspect': 'place', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n->", + "output": "{\"text\": \"the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is heavy , but that is to be expected with a laptop like this one .\n->it is heavy , but that is to be expected with a laptop like this one .\n[{'aspect': 'laptop', 'opinion': 'heavy', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->the brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: first one that they shipped was obviously defective , super slow and speakers were garbled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst one that they shipped was obviously defective , super slow and speakers were garbled .\n->", + "output": "{\"text\": \"first one that they shipped was obviously defective , super slow and speakers were garbled .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'defective', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'speakers', 'opinion': 'garbled', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop performs in every way .\n->this laptop performs in every way .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: but that is highly forgivable .\n->but that is highly forgivable .\n[{'aspect': 'NULL', 'opinion': 'forgivable', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: the replacement i got was much better , but still too slow for my expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe replacement i got was much better , but still too slow for my expectations .\n->", + "output": "{\"text\": \"the replacement i got was much better , but still too slow for my expectations .\", \"labels\": \"[{'aspect': 'replacement', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t know that i would want to use for work but it ' s perfect for personal use .\n->i don ' t know that i would want to use for work but it ' s perfect for personal use .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: boot up is of course , almost instant .\n->boot up is of course , almost instant .\n[{'aspect': 'boot up', 'opinion': 'instant', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: i wound up returning it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wound up returning it .\n->", + "output": "{\"text\": \"i wound up returning it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked properly for less than a week , and the touch screen stopped functioning again .\n->it worked properly for less than a week , and the touch screen stopped functioning again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: it is heavy , but that is to be expected with a laptop like this one .\n->it is heavy , but that is to be expected with a laptop like this one .\n[{'aspect': 'laptop', 'opinion': 'heavy', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: this works fine for that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis works fine for that .\n->", + "output": "{\"text\": \"this works fine for that .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like the ease of connecting to the internet wi - fi .\n->i like the ease of connecting to the internet wi - fi .\n[{'aspect': 'wi - fi', 'opinion': 'ease', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: i have had this computer for almost a year now and i love it .\n->i have had this computer for almost a year now and i love it .\n[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: october 12 , 2017 - - started having trouble maintaining connection to wifi ( spectrum service ) , but usually after several loops re - entering password , connection would be re - established .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noctober 12 , 2017 - - started having trouble maintaining connection to wifi ( spectrum service ) , but usually after several loops re - entering password , connection would be re - established .\n->", + "output": "{\"text\": \"october 12 , 2017 - - started having trouble maintaining connection to wifi ( spectrum service ) , but usually after several loops re - entering password , connection would be re - established .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'trouble', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were then shooed inside .\n->we were then shooed inside .\n[{'aspect': 'NULL', 'opinion': 'shooed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n->this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n[{'aspect': 'performance', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'happy', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: sometimes had to do several times , but thought it might be an idiosyncrady of this model .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes had to do several times , but thought it might be an idiosyncrady of this model .\n->", + "output": "{\"text\": \"sometimes had to do several times , but thought it might be an idiosyncrady of this model .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great laptop if you are willing to put in an ssd and reinstall windows .\n->great laptop if you are willing to put in an ssd and reinstall windows .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n->During the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n->", + "output": "{\"text\": \"now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'bummed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i believe there were 2 shrimp in the ` ` salt encrusted shrimp ' ' appetizer .\n->i believe there were 2 shrimp in the ` ` salt encrusted shrimp ' ' appetizer .\n[{'aspect': \"` ` salt encrusted shrimp ' ' appetizer\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the entertainment was great they have shows that go on through out the dinner .\n->the entertainment was great they have shows that go on through out the dinner .\n[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i just got this thing today & was really excited about it but this has been frustrating & disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just got this thing today & was really excited about it but this has been frustrating & disappointing .\n->", + "output": "{\"text\": \"i just got this thing today & was really excited about it but this has been frustrating & disappointing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well , i have to say , i ' m fairly impressed with my new chrome flipbook !\n->well , i have to say , i ' m fairly impressed with my new chrome flipbook !\n[{'aspect': 'chrome flipbook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n->The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n[{'aspect': 'cuisine', 'opinion': 'different', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i ' ll wait a while until this device gets 4 - 5 stars .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ll wait a while until this device gets 4 - 5 stars .\n->", + "output": "{\"text\": \"i ' ll wait a while until this device gets 4 - 5 stars .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a great place to meet up for some food and drinks . . .\n->a great place to meet up for some food and drinks . . .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'NULL'}]\ntext: there are certain very basic tasks that this computer can do .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere are certain very basic tasks that this computer can do .\n->", + "output": "{\"text\": \"there are certain very basic tasks that this computer can do .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: paul , the maitre d ' , was totally professional and always on top of things .\n->paul , the maitre d ' , was totally professional and always on top of things .\n[{'aspect': 'paul', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n->i thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: a lot of websites don ' t work properly with the chromebook , even though you ' re using the same browser .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na lot of websites don ' t work properly with the chromebook , even though you ' re using the same browser .\n->", + "output": "{\"text\": \"a lot of websites don ' t work properly with the chromebook , even though you ' re using the same browser .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the track pad is one of the best i have seen for a non - apple touch pad .\n->the track pad is one of the best i have seen for a non - apple touch pad .\n[{'aspect': 'track pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: greatest thing i ' ve bought myself in a long time .\n->greatest thing i ' ve bought myself in a long time .\n[{'aspect': 'NULL', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: overall , this thing can do very basic stuff , sometimes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , this thing can do very basic stuff , sometimes .\n->", + "output": "{\"text\": \"overall , this thing can do very basic stuff , sometimes .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n->The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is the worst computer i have ever owned .\n->this is the worst computer i have ever owned .\n[{'aspect': 'computer', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: pro : light , reasonable price , fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npro : light , reasonable price , fast .\n->", + "output": "{\"text\": \"pro : light , reasonable price , fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n->the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screen', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: Cute place , nice wait staff but would never go there again .\n->Cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'negative', 'category': 'NULL'}]\ntext: keyboard key fragile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard key fragile .\n->", + "output": "{\"text\": \"keyboard key fragile .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'fragile', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is also very comfortable to type on .\n->the keyboard is also very comfortable to type on .\n[{'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: very happy with it .\n->very happy with it .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: after 6 months my eyboard key is not function very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter 6 months my eyboard key is not function very well .\n->", + "output": "{\"text\": \"after 6 months my eyboard key is not function very well .\", \"labels\": \"[{'aspect': 'eyboard key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n->the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n[{'aspect': 'keyboard', 'opinion': 'large', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: they did n't disappoint , service from the second i arrived at the door was extremely pleasant and attentive with almost one server per table .\n->they did n't disappoint , service from the second i arrived at the door was extremely pleasant and attentive with almost one server per table .\n[{'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i only use the chormebook for surving the internet , checking my email , and watch hulu or netflix .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni only use the chormebook for surving the internet , checking my email , and watch hulu or netflix .\n->", + "output": "{\"text\": \"i only use the chormebook for surving the internet , checking my email , and watch hulu or netflix .\", \"labels\": \"[{'aspect': 'chormebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n->There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n[{'aspect': 'table', 'opinion': 'long wait', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'insde table', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Please if your thinking about it go , and stay the wait you wo n't be disappointed .\n->Please if your thinking about it go , and stay the wait you wo n't be disappointed .\n[{'aspect': 'wait', 'opinion': \"wo n't be disappointed\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: this is something to reconsidered if one to buy the chromebook for doing homework and a lot of typing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is something to reconsidered if one to buy the chromebook for doing homework and a lot of typing .\n->", + "output": "{\"text\": \"this is something to reconsidered if one to buy the chromebook for doing homework and a lot of typing .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Over the years , it has always provided a pleasurable dining experience with quality food and wine .\n->Over the years , it has always provided a pleasurable dining experience with quality food and wine .\n[{'aspect': 'food', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining', 'opinion': 'pleasurable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it looks way better in person than in the pictures .\n->it looks way better in person than in the pictures .\n[{'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: up date per may 13 / 2018 about two months ago , the charger wont work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nup date per may 13 / 2018 about two months ago , the charger wont work .\n->", + "output": "{\"text\": \"up date per may 13 / 2018 about two months ago , the charger wont work .\", \"labels\": \"[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re on the fence i recommend this asus .\n->if you ' re on the fence i recommend this asus .\n[{'aspect': 'asus', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' ve been using it for almost three weeks now and it has not let me down .\n->i ' ve been using it for almost three weeks now and it has not let me down .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: so latest update the charger only last about one year and the half .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso latest update the charger only last about one year and the half .\n->", + "output": "{\"text\": \"so latest update the charger only last about one year and the half .\", \"labels\": \"[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: luckily we saved room for the bbq salmon , sea bass and crispy duck .\n->luckily we saved room for the bbq salmon , sea bass and crispy duck .\n[{'aspect': 'bbq salmon', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sea bass', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crispy duck', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: and the price is excellent for what you get .\n->and the price is excellent for what you get .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: overall , it is not horrible , but i wouldn ' t purchase this model again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , it is not horrible , but i wouldn ' t purchase this model again .\n->", + "output": "{\"text\": \"overall , it is not horrible , but i wouldn ' t purchase this model again .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'not horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i must give it yon out of yon stars !\n->i must give it yon out of yon stars !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: you ca n ' t go wrong with this place .\n->you ca n ' t go wrong with this place .\n[{'aspect': 'place', 'opinion': \"ca n ' t go wrong\", 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: - long battery life\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- long battery life\n->", + "output": "{\"text\": \"- long battery life\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t have need for the backlit keyboard .\n->i don ' t have need for the backlit keyboard .\n[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i ' m seriously considering returning it !\n->i ' m seriously considering returning it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: - charges quickly\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- charges quickly\n->", + "output": "{\"text\": \"- charges quickly\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything about this restaurant was special .\n->everything about this restaurant was special .\n[{'aspect': 'restaurant', 'opinion': 'special', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n->but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n[{'aspect': 'windows 10', 'opinion': 'worst', 'polarity': 'negative', 'category': 'OS#GENERAL'}, {'aspect': 'windows 10', 'opinion': 'awful', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\ntext: - screen feels smaller than other of the same size .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- screen feels smaller than other of the same size .\n->", + "output": "{\"text\": \"- screen feels smaller than other of the same size .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n->it was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .\n[{'aspect': 'spinach', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: it does great and has lots of cool stuff\n->it does great and has lots of cool stuff\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it gets two stars because it is a nice light weight and the folding is nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit gets two stars because it is a nice light weight and the folding is nice .\n->", + "output": "{\"text\": \"it gets two stars because it is a nice light weight and the folding is nice .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'folding', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only wish the power button was somewhere else , its too easy to hit accidentally .\n->only wish the power button was somewhere else , its too easy to hit accidentally .\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\nExample:\ntext: Largest and freshest pieces of sushi , and delicious !\n->Largest and freshest pieces of sushi , and delicious !\n[{'aspect': 'pieces of sushi', 'opinion': 'Largest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: two replacements had the same problem all in 4 weeks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntwo replacements had the same problem all in 4 weeks .\n->", + "output": "{\"text\": \"two replacements had the same problem all in 4 weeks .\", \"labels\": \"[{'aspect': 'replacements', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chennai garden is my favorite indian restaurant in the city .\n->chennai garden is my favorite indian restaurant in the city .\n[{'aspect': 'chennai garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i already returned the first laptop because i had to press extremely hard to get the left click to work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni already returned the first laptop because i had to press extremely hard to get the left click to work .\n->", + "output": "{\"text\": \"i already returned the first laptop because i had to press extremely hard to get the left click to work .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was very surprised at how fast it came .\n->i was very surprised at how fast it came .\n[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\nExample:\ntext: as everyone else says ; the keyboard is not backlit .\n->as everyone else says ; the keyboard is not backlit .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i ' ve had the device for 4 days now , and it has a number of big issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had the device for 4 days now , and it has a number of big issues .\n->", + "output": "{\"text\": \"i ' ve had the device for 4 days now , and it has a number of big issues .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wish ny had more of these kind of places : intimate , superb food , homey , top notch all the way around , certainly worth the wait .\n->wish ny had more of these kind of places : intimate , superb food , homey , top notch all the way around , certainly worth the wait .\n[{'aspect': 'food', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'homey', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: however there are many issues with this computer on start up that really bothered me and made my experience with a mac not that great\n->however there are many issues with this computer on start up that really bothered me and made my experience with a mac not that great\n[{'aspect': 'computer', 'opinion': 'bothered', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'mac', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: next , is that the track pad is insanely wobbly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnext , is that the track pad is insanely wobbly .\n->", + "output": "{\"text\": \"next , is that the track pad is insanely wobbly .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'wobbly', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: highly recommend it !\n->highly recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The waitresses are nice -- also you can just get counter service sit .\n->The waitresses are nice -- also you can just get counter service sit .\n[{'aspect': 'waitresses', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: one of the worst offenders is the battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of the worst offenders is the battery life .\n->", + "output": "{\"text\": \"one of the worst offenders is the battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'worst', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: NO more reservations , expensive tips and annoying stuff .\n->NO more reservations , expensive tips and annoying stuff .\n[{'aspect': 'reservations', 'opinion': 'NO more', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tips', 'opinion': 'expensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'stuff', 'opinion': 'annoying', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this computer seemed very exciting but after having troubles with 3 of them i give up .\n->this computer seemed very exciting but after having troubles with 3 of them i give up .\n[{'aspect': 'computer', 'opinion': 'exciting', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n->", + "output": "{\"text\": \"it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after about 60 days use the power adapter / charger stopped working .\n->after about 60 days use the power adapter / charger stopped working .\n[{'aspect': 'power adapter / charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i bought this chromebook on the prime day deal , but even without $ 60 off it would still have been worth the money .\n->i bought this chromebook on the prime day deal , but even without $ 60 off it would still have been worth the money .\n[{'aspect': 'chromebook', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: there are issues beyond these , but it ' s all software related so i will let it be .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere are issues beyond these , but it ' s all software related so i will let it be .\n->", + "output": "{\"text\": \"there are issues beyond these , but it ' s all software related so i will let it be .\", \"labels\": \"[{'aspect': 'software', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n->if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: moreover i ' m quite upset because it seems amazon will not pay me back the shipping fees , which for me amount to about 100 $ as i live in france .\n->moreover i ' m quite upset because it seems amazon will not pay me back the shipping fees , which for me amount to about 100 $ as i live in france .\n[{'aspect': 'amazon', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}]\ntext: within two days of receiving this item a line appeared on my screen ( while i was using it , previously fine ) and below it the image / screen flickered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwithin two days of receiving this item a line appeared on my screen ( while i was using it , previously fine ) and below it the image / screen flickered .\n->", + "output": "{\"text\": \"within two days of receiving this item a line appeared on my screen ( while i was using it , previously fine ) and below it the image / screen flickered .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: perfect ` ` computer ` ` for my young child .\n->perfect ` ` computer ` ` for my young child .\n[{'aspect': 'computer', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: the flip and touchscreen aspects work fine , no problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe flip and touchscreen aspects work fine , no problems .\n->", + "output": "{\"text\": \"the flip and touchscreen aspects work fine , no problems .\", \"labels\": \"[{'aspect': 'flip', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n->the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n[{'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\nExample:\ntext: this makes it uncomfortable holding it in tablet mode .\n->this makes it uncomfortable holding it in tablet mode .\n[{'aspect': 'tablet mode', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: it can not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit can not .\n->", + "output": "{\"text\": \"it can not .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - love the trackpad .\n->- love the trackpad .\n[{'aspect': 'trackpad', 'opinion': 'trackpad', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: hot / dead pixels on screen after 4 months use .\n->hot / dead pixels on screen after 4 months use .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: screen looks good , it has a good battery life , keypad has some nice feedback , the works .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen looks good , it has a good battery life , keypad has some nice feedback , the works .\n->", + "output": "{\"text\": \"screen looks good , it has a good battery life , keypad has some nice feedback , the works .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The steak is good , the fish is good and the sushi was surprisingly great .\n->The steak is good , the fish is good and the sushi was surprisingly great .\n[{'aspect': 'steak', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n->the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n[{'aspect': 'plastic', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keys', 'opinion': \"' t stick\", 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mousepad', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: but you can ' t use an acer chromebook r 11 this way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut you can ' t use an acer chromebook r 11 this way .\n->", + "output": "{\"text\": \"but you can ' t use an acer chromebook r 11 this way .\", \"labels\": \"[{'aspect': 'acer chromebook r 11', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The tuna and wasabe potatoes are excellent .\n->The tuna and wasabe potatoes are excellent .\n[{'aspect': 'tuna', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wasabe potatoes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the os doesn ' t leave menu bar at the top for copying in programs for studies .\n->the os doesn ' t leave menu bar at the top for copying in programs for studies .\n[{'aspect': 'os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\ntext: and the chromebook does not go to sleep or otherwise shut off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand the chromebook does not go to sleep or otherwise shut off .\n->", + "output": "{\"text\": \"and the chromebook does not go to sleep or otherwise shut off .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n->the restaurant is a bit noisy but that is something that can be overlooked once you sit down and enjoy a great meal\n[{'aspect': 'meal', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'meal', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'restaurant', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: We have been to this place many times , and always have great food , wine , and service .\n->We have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: loved it for an hour than it went black and we got a chrome os missing or damage message .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nloved it for an hour than it went black and we got a chrome os missing or damage message .\n->", + "output": "{\"text\": \"loved it for an hour than it went black and we got a chrome os missing or damage message .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n->They are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !\n[{'aspect': 'place', 'opinion': 'hostile', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n->there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n[{'aspect': 'screen resolution', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'not working well', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: terrible product and worse customer service - - do not buy\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nterrible product and worse customer service - - do not buy\n->", + "output": "{\"text\": \"terrible product and worse customer service - - do not buy\", \"labels\": \"[{'aspect': 'product', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'customer service', 'opinion': 'worse', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unfortunately , the downfall for me are the speakers .\n->unfortunately , the downfall for me are the speakers .\n[{'aspect': 'speakers', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: - ips full hd screen .\n->- ips full hd screen .\n[{'aspect': 'hd screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\ntext: we ordered this chromebook for my son to use for school .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ordered this chromebook for my son to use for school .\n->", + "output": "{\"text\": \"we ordered this chromebook for my son to use for school .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s all about google but my kids really like it .\n->it ' s all about google but my kids really like it .\n[{'aspect': 'google', 'opinion': 'like', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: have had mine for about 3 - 4 wks and have had no trouble .\n->have had mine for about 3 - 4 wks and have had no trouble .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: we received it in august , and it worked fine for just 5 months and then the touch screen stopped working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe received it in august , and it worked fine for just 5 months and then the touch screen stopped working .\n->", + "output": "{\"text\": \"we received it in august , and it worked fine for just 5 months and then the touch screen stopped working .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: too bad the food was n ' t of the same heritage .\n->too bad the food was n ' t of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n->I must say it 's a little pricey for the food because it was not as spectacular as the view .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it worked properly for less than a week , and the touch screen stopped functioning again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit worked properly for less than a week , and the touch screen stopped functioning again .\n->", + "output": "{\"text\": \"it worked properly for less than a week , and the touch screen stopped functioning again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n->With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait-staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We were worried we would have trouble getting in , but somehow managed to have a short wait .\n->We were worried we would have trouble getting in , but somehow managed to have a short wait .\n[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we contacted both acer and amazon , and they both informed us that it has to be sent back for repairs again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe contacted both acer and amazon , and they both informed us that it has to be sent back for repairs again .\n->", + "output": "{\"text\": \"we contacted both acer and amazon , and they both informed us that it has to be sent back for repairs again .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n->i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n[{'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'pita', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hummus', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled octopus', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: good display\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood display\n->", + "output": "{\"text\": \"good display\", \"labels\": \"[{'aspect': 'display', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There is no excuse for such lousy service !\n->There is no excuse for such lousy service !\n[{'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: overall , this thing can do very basic stuff , sometimes .\n->overall , this thing can do very basic stuff , sometimes .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: good battery life\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood battery life\n->", + "output": "{\"text\": \"good battery life\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this acer is a web surfer that ' s easy to travel with .\n->this acer is a web surfer that ' s easy to travel with .\n[{'aspect': 'acer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Pizza here is consistently good .\n->Pizza here is consistently good .\n[{'aspect': 'Pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: no fans and stays reasonably cool unless you are playing a game .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno fans and stays reasonably cool unless you are playing a game .\n->", + "output": "{\"text\": \"no fans and stays reasonably cool unless you are playing a game .\", \"labels\": \"[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}, {'aspect': 'NULL', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'FANS&COOLING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this acer chromebook 14 is by far the best chromebook 300 dollars can buy .\n->this acer chromebook 14 is by far the best chromebook 300 dollars can buy .\n[{'aspect': 'acer chromebook 14', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: - speakers can ` ` chatter ` ` after playing youtube videos for a long period of time .\n->- speakers can ` ` chatter ` ` after playing youtube videos for a long period of time .\n[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: build quality seems ok , keyboard is not flimsy or too firm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuild quality seems ok , keyboard is not flimsy or too firm .\n->", + "output": "{\"text\": \"build quality seems ok , keyboard is not flimsy or too firm .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'ok', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'not flimsy or too firm', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m no expert on screens but i personally think the panel looks very nice .\n->i ' m no expert on screens but i personally think the panel looks very nice .\n[{'aspect': 'panel', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Excellent spot for holiday get togethers with co-workers or friends that you have n't seen in a while .\n->Excellent spot for holiday get togethers with co-workers or friends that you have n't seen in a while .\n[{'aspect': 'spot', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my understanding is that chromeos is a very light weight so i don ' t know why it lags sometimes with under 10 tabs open and a youtube video playing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy understanding is that chromeos is a very light weight so i don ' t know why it lags sometimes with under 10 tabs open and a youtube video playing .\n->", + "output": "{\"text\": \"my understanding is that chromeos is a very light weight so i don ' t know why it lags sometimes with under 10 tabs open and a youtube video playing .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'light', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the device is very light and easily transportable .\n->the device is very light and easily transportable .\n[{'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'transportable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n->i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: i needed a small chromebook for light web use while traveling and this tablet does ok but considering the price i would have expected a better processor and keyboard lighting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni needed a small chromebook for light web use while traveling and this tablet does ok but considering the price i would have expected a better processor and keyboard lighting .\n->", + "output": "{\"text\": \"i needed a small chromebook for light web use while traveling and this tablet does ok but considering the price i would have expected a better processor and keyboard lighting .\", \"labels\": \"[{'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#PRICE'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now there are pixels on the screen not working , and they are multiplying .\n->now there are pixels on the screen not working , and they are multiplying .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: the monitor is bright and colorful .\n->the monitor is bright and colorful .\n[{'aspect': 'monitor', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'monitor', 'opinion': 'colorful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: my main complaint is the battery life , i see many positive reviews about the battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy main complaint is the battery life , i see many positive reviews about the battery life .\n->", + "output": "{\"text\": \"my main complaint is the battery life , i see many positive reviews about the battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is my first time writing a review for a restaurant because the food and service was excellent .\n->This is my first time writing a review for a restaurant because the food and service was excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We had the pot-stickers which were great and a tempura dish that was great .\n->We had the pot-stickers which were great and a tempura dish that was great .\n[{'aspect': 'pot-stickers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tempura dish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i just find the battery draining to quickly in my opinion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just find the battery draining to quickly in my opinion .\n->", + "output": "{\"text\": \"i just find the battery draining to quickly in my opinion .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: less than 90 days and the screen stopped working .\n->less than 90 days and the screen stopped working .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: track pad is passable .\n->track pad is passable .\n[{'aspect': 'track pad', 'opinion': 'passable', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\ntext: i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n->", + "output": "{\"text\": \"i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\", \"labels\": \"[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what really makes it shine is the food , which is aggressively seasoned with cyrpriot spices , and all made in - house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .\n->what really makes it shine is the food , which is aggressively seasoned with cyrpriot spices , and all made in - house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .\n[{'aspect': 'food', 'opinion': 'shine', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'gyro meat', 'opinion': 'in - house', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sausages', 'opinion': 'in - house', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'higher quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: so rude ! ! !\n->so rude ! ! !\n[{'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: plug - in or usb microphones seem to work fine , so it ' s not a terribly big issue and i don ' t really even use a microphone that often , but it ' s annoying to buy a product and not have it working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplug - in or usb microphones seem to work fine , so it ' s not a terribly big issue and i don ' t really even use a microphone that often , but it ' s annoying to buy a product and not have it working .\n->", + "output": "{\"text\": \"plug - in or usb microphones seem to work fine , so it ' s not a terribly big issue and i don ' t really even use a microphone that often , but it ' s annoying to buy a product and not have it working .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is small and cramped but the food is fantastic .\n->the place is small and cramped but the food is fantastic .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: lots of space , fast , and it will last a long time with ita top shelf technology .\n->lots of space , fast , and it will last a long time with ita top shelf technology .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: the broken mic was not a dealbreaker but annoying in a brand new device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe broken mic was not a dealbreaker but annoying in a brand new device .\n->", + "output": "{\"text\": \"the broken mic was not a dealbreaker but annoying in a brand new device .\", \"labels\": \"[{'aspect': 'mic', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: port options are nice as well .\n->port options are nice as well .\n[{'aspect': 'port options', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: they are clearly working with more than one person at a time , and not effective multi - taskers .\n->they are clearly working with more than one person at a time , and not effective multi - taskers .\n[{'aspect': 'NULL', 'opinion': 'not effective', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n->", + "output": "{\"text\": \"in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\", \"labels\": \"[{'aspect': 'fingerprints', 'opinion': 'dislike', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the graphics when i replay my videos or watch other streamers .\n->love the graphics when i replay my videos or watch other streamers .\n[{'aspect': 'graphics', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the m3 processor is pretty good ( decent speedometer score ) .\n->the m3 processor is pretty good ( decent speedometer score ) .\n[{'aspect': 'm3 processor', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'm3 processor', 'opinion': 'decent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\ntext: good looking laptop but hardware has several major problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood looking laptop but hardware has several major problems .\n->", + "output": "{\"text\": \"good looking laptop but hardware has several major problems .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good looking', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'hardware', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i absolutely loved this place .\n->i absolutely loved this place .\n[{'aspect': 'place', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it has the specs but that ' s it ' s main downfall .\n->it has the specs but that ' s it ' s main downfall .\n[{'aspect': 'specs', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmicrophone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n->", + "output": "{\"text\": \"microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\", \"labels\": \"[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i returned it twice .\n->i returned it twice .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s boring on the inside , and our sushi was pretty below average . . . the tuna was soggy and the other rolls had no flavor .\n->it ' s boring on the inside , and our sushi was pretty below average . . . the tuna was soggy and the other rolls had no flavor .\n[{'aspect': 'sushi', 'opinion': 'below average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'tuna', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'boring', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: when switching between tablet and laptop mode quickly the screen will blink and turn off for an extended period of time before turning back on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen switching between tablet and laptop mode quickly the screen will blink and turn off for an extended period of time before turning back on .\n->", + "output": "{\"text\": \"when switching between tablet and laptop mode quickly the screen will blink and turn off for an extended period of time before turning back on .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no problems with play store / android apps .\n->no problems with play store / android apps .\n[{'aspect': 'play store / android apps', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: People are always friendly .\n->People are always friendly .\n[{'aspect': 'People', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: called acer support , they were completely useless .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncalled acer support , they were completely useless .\n->", + "output": "{\"text\": \"called acer support , they were completely useless .\", \"labels\": \"[{'aspect': 'acer support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sandwhiches are out of this world !\n->The sandwhiches are out of this world !\n[{'aspect': 'sandwhiches', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i had fish and my husband had the filet - both of which exceeded our expectations .\n->i had fish and my husband had the filet - both of which exceeded our expectations .\n[{'aspect': 'fish', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'filet', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: keeps disconnecting from my wifi at work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeeps disconnecting from my wifi at work .\n->", + "output": "{\"text\": \"keeps disconnecting from my wifi at work .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so rushing us out was absolutely unnecessary !\n->so rushing us out was absolutely unnecessary !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - the keys themselves don ' t travel that much which i prefer .\n->- the keys themselves don ' t travel that much which i prefer .\n[{'aspect': 'keys', 'opinion': 'prefer', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: it was nice when it was working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was nice when it was working .\n->", + "output": "{\"text\": \"it was nice when it was working .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i got this for my mother in law and she could not be happier with how it works .\n->i got this for my mother in law and she could not be happier with how it works .\n[{'aspect': 'NULL', 'opinion': 'not be happier', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n->the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n[{'aspect': 'mouse', 'opinion': 'good', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\ntext: i ' ve called twice to try to connect it to printer , first call they told me i had to get a cloud ready printer after getting said printer it still doesn ' t connect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve called twice to try to connect it to printer , first call they told me i had to get a cloud ready printer after getting said printer it still doesn ' t connect .\n->", + "output": "{\"text\": \"i ' ve called twice to try to connect it to printer , first call they told me i had to get a cloud ready printer after getting said printer it still doesn ' t connect .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n->it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n[{'aspect': 'chrome os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'glossy', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: it ' s a good enough laptop .\n->it ' s a good enough laptop .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: very disappointed in this machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery disappointed in this machine .\n->", + "output": "{\"text\": \"very disappointed in this machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: which of course is not real kobe but wagyu beef .\n->which of course is not real kobe but wagyu beef .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this restaurant was way overhyped .\n->this restaurant was way overhyped .\n[{'aspect': 'restaurant', 'opinion': 'overhyped', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: i typically love acers ( i have a regular laptop ) but this machine has been a nightmare .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni typically love acers ( i have a regular laptop ) but this machine has been a nightmare .\n->", + "output": "{\"text\": \"i typically love acers ( i have a regular laptop ) but this machine has been a nightmare .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'nightmare', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We figured we never had Argentinian Pizza before so we grabbed our lunch there , sharing a large Pelligrino , a pizza of two of their specials , one was goat cheese the other blue cheese , and both were excellent .\n->We figured we never had Argentinian Pizza before so we grabbed our lunch there , sharing a large Pelligrino , a pizza of two of their specials , one was goat cheese the other blue cheese , and both were excellent .\n[{'aspect': 'Pelligrino', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'goat cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'blue cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great aesthetics .\n->great aesthetics .\n[{'aspect': 'aesthetics', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: issue summary : frequent crashing\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nissue summary : frequent crashing\n->", + "output": "{\"text\": \"issue summary : frequent crashing\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did n ' t complain , i liked the atmosphere so much .\n->i did n ' t complain , i liked the atmosphere so much .\n[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: nice view of river and nyc .\n->nice view of river and nyc .\n[{'aspect': 'view of river and nyc', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: 6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n->", + "output": "{\"text\": \"6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\", \"labels\": \"[{'aspect': 'hd touch', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'intel celeron n3150', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'CPU#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#GENERAL'}, {'aspect': 'cb5 - 132t - c1lk', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it simply feels like a cheap samsung tablet with a keyboard attached .\n->it simply feels like a cheap samsung tablet with a keyboard attached .\n[{'aspect': 'samsung tablet', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: cirspy crust margherita pizza\n->cirspy crust margherita pizza\n[{'aspect': 'margherita pizza', 'opinion': 'cirspy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'crust', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: 6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n->", + "output": "{\"text\": \"6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\", \"labels\": \"[{'aspect': '4gb', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: simply some good tasting chinese food at incredible prices . . .\n->simply some good tasting chinese food at incredible prices . . .\n[{'aspect': 'chinese food', 'opinion': 'good tasting', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'chinese food', 'opinion': 'good tasting', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n->i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\n->", + "output": "{\"text\": \"it frequently crashes by displaying a bizarre screen displaying a large number of short horizontal lines and making a loud alarm noise .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'bizarre', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they even scoop it out nice ( for those on a diet ) not too much not to little .\n->they even scoop it out nice ( for those on a diet ) not too much not to little .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n->i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n[{'aspect': 'voltage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: this issue happens more frequently when using netflix ( app or through chrome browser ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis issue happens more frequently when using netflix ( app or through chrome browser ) .\n->", + "output": "{\"text\": \"this issue happens more frequently when using netflix ( app or through chrome browser ) .\", \"labels\": \"[{'aspect': 'happens', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great customer service .\n->great customer service .\n[{'aspect': 'customer service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: do n ' t buy this laptop or brand .\n->do n ' t buy this laptop or brand .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'brand', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: however it can happen at random , for example when signing into gmail .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever it can happen at random , for example when signing into gmail .\n->", + "output": "{\"text\": \"however it can happen at random , for example when signing into gmail .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n->The only disappointment was the coat check girls who did n't seem to know what a customer is on a realtively non-busy night ( for the coat check girls ) .\n[{'aspect': 'coat check girls', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this laptop is great for a lot of modern games .\n->this laptop is great for a lot of modern games .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it ' s okay for email , facebook , surfing the net , etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s okay for email , facebook , surfing the net , etc .\n->", + "output": "{\"text\": \"it ' s okay for email , facebook , surfing the net , etc .\", \"labels\": \"[{'aspect': 'email', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'facebook', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Just straight up cheap , good food .\n->Just straight up cheap , good food .\n[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i wo n ' t go back unless someone else is footing the bill .\n->i wo n ' t go back unless someone else is footing the bill .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: so , this one isn ' t working out very well for my needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso , this one isn ' t working out very well for my needs .\n->", + "output": "{\"text\": \"so , this one isn ' t working out very well for my needs .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': \"' t working out very well\", 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n->Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n[{'aspect': 'fruit of the oil', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'sweetness', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is the best shabu - shabu restaurant in the try - state area .\n->this is the best shabu - shabu restaurant in the try - state area .\n[{'aspect': 'shabu - shabu restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i hope it is fixed this time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni hope it is fixed this time .\n->", + "output": "{\"text\": \"i hope it is fixed this time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was warm and attentive , beef carpaachio was exellent ( huge portion ) and pasta was fresh and well-prepared .\n->Service was warm and attentive , beef carpaachio was exellent ( huge portion ) and pasta was fresh and well-prepared .\n[{'aspect': 'Service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beef carpaachio', 'opinion': 'exellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'well-prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is my first msi and if it stays great i will be a returning customer .\n->this is my first msi and if it stays great i will be a returning customer .\n[{'aspect': 'msi', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the other is excellent !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe other is excellent !\n->", + "output": "{\"text\": \"the other is excellent !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great purchase , quick shipping .\n->great purchase , quick shipping .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'shipping', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: this was the worst computer ever .\n->this was the worst computer ever .\n[{'aspect': 'computer', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: has lots of issues , screen freezing , i was hoping to use it for banking etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhas lots of issues , screen freezing , i was hoping to use it for banking etc .\n->", + "output": "{\"text\": \"has lots of issues , screen freezing , i was hoping to use it for banking etc .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it feels wonderful to finally say that about an acer display .\n->it feels wonderful to finally say that about an acer display .\n[{'aspect': 'acer display', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n->Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n[{'aspect': 'meats', 'opinion': 'thin', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'various', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit feels like a tablet / computer for a child since it ' s so bulky and heavy .\n->", + "output": "{\"text\": \"it feels like a tablet / computer for a child since it ' s so bulky and heavy .\", \"labels\": \"[{'aspect': 'tablet', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fish was not fresh and the rice tasted old and stale .\n->The fish was not fresh and the rice tasted old and stale .\n[{'aspect': 'fish', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: we paid a fixed pricce but got nothing ! !\n->we paid a fixed pricce but got nothing ! !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the mousepad was not very responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mousepad was not very responsive .\n->", + "output": "{\"text\": \"the mousepad was not very responsive .\", \"labels\": \"[{'aspect': 'mousepad', 'opinion': 'not very responsive', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n->i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The portion sizes here are huge , and the sushi is good .\n->The portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i really think it ' s junk .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really think it ' s junk .\n->", + "output": "{\"text\": \"i really think it ' s junk .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'junk', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard is the best laptop keyboard i have ever used .\n->keyboard is the best laptop keyboard i have ever used .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'laptop keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: this little chromebook is very nice and is pretty much what i expected .\n->this little chromebook is very nice and is pretty much what i expected .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: runs good , poor battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nruns good , poor battery life .\n->", + "output": "{\"text\": \"runs good , poor battery life .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'poor', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it has served me very well ever since .\n->it has served me very well ever since .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this one is horrible , never can connect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis one is horrible , never can connect .\n->", + "output": "{\"text\": \"this one is horrible , never can connect .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bagel was huge .\n->the bagel was huge .\n[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: if you ' re looking for a good chromebook , this is the one for you .\n->if you ' re looking for a good chromebook , this is the one for you .\n[{'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: after years of using amazon for hundreds of orders , this is my very first negative review :\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter years of using amazon for hundreds of orders , this is my very first negative review :\n->", + "output": "{\"text\": \"after years of using amazon for hundreds of orders , this is my very first negative review :\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'negative', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n->admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n[{'aspect': 'open kitchen', 'opinion': 'charm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n->Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n[{'aspect': 'meats', 'opinion': 'thin', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'various', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i was very excited at the prospect of buying this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was very excited at the prospect of buying this laptop .\n->", + "output": "{\"text\": \"i was very excited at the prospect of buying this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is also really nice .\n->The wine list is also really nice .\n[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is exactly what i needed in a laptop .\n->this is exactly what i needed in a laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i was very impressed with its sleek design and the speed of the processor itself .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was very impressed with its sleek design and the speed of the processor itself .\n->", + "output": "{\"text\": \"i was very impressed with its sleek design and the speed of the processor itself .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'processor', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is plenty big and the visual very nice .\n->the screen is plenty big and the visual very nice .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n->It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n[{'aspect': 'table', 'opinion': 'impossible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: a few hours of use later , i noticed that my battery was very low , around 10 % .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na few hours of use later , i noticed that my battery was very low , around 10 % .\n->", + "output": "{\"text\": \"a few hours of use later , i noticed that my battery was very low , around 10 % .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n->My friend 's food was also the complete opposite of what it 's supposed to taste like ( aND look like ) .\n[{'aspect': 'food', 'opinion': 'opposite', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n->for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\ntext: i even tried reinstalling the drivers and doing a system restore , nothing would fix it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni even tried reinstalling the drivers and doing a system restore , nothing would fix it .\n->", + "output": "{\"text\": \"i even tried reinstalling the drivers and doing a system restore , nothing would fix it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: those rolls were big , but not good and sashimi was n ' t fresh .\n->those rolls were big , but not good and sashimi was n ' t fresh .\n[{'aspect': 'rolls', 'opinion': 'big', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sashimi', 'opinion': \"was n ' t fresh\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: * excellent form factor , extremely portable while remaining a serious pro computer\n->* excellent form factor , extremely portable while remaining a serious pro computer\n[{'aspect': 'pro computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro computer', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: i contacted asus and they could do nothing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni contacted asus and they could do nothing .\n->", + "output": "{\"text\": \"i contacted asus and they could do nothing .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great bagels made the old-fashioned way .\n->Great bagels made the old-fashioned way .\n[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: guaranteed excellent customer service !\n->guaranteed excellent customer service !\n[{'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: i was so glad that the problem would finally be fixed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was so glad that the problem would finally be fixed .\n->", + "output": "{\"text\": \"i was so glad that the problem would finally be fixed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery on this computer is not very good i feel like i always have to have it plugged in .\n->the battery on this computer is not very good i feel like i always have to have it plugged in .\n[{'aspect': 'battery', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n->I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n->", + "output": "{\"text\": \"to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\", \"labels\": \"[{'aspect': 'power icon', 'opinion': 'dismay', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'power icon', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Its an excellent place to relax and the food is one of the best in the city of New York .\n->Its an excellent place to relax and the food is one of the best in the city of New York .\n[{'aspect': 'place', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this device is mainly used for web browsing and pages load quickly , animations are swift and not laggy .\n->this device is mainly used for web browsing and pages load quickly , animations are swift and not laggy .\n[{'aspect': 'device', 'opinion': 'swift', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'not laggy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this was the second vivobook in a row !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was the second vivobook in a row !\n->", + "output": "{\"text\": \"this was the second vivobook in a row !\", \"labels\": \"[{'aspect': 'vivobook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the product came highly recommended .\n->the product came highly recommended .\n[{'aspect': 'product', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: am i just unlucky , or was this a bad batch ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nam i just unlucky , or was this a bad batch ?\n->", + "output": "{\"text\": \"am i just unlucky , or was this a bad batch ?\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'unlucky', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as the hours in your day wind down , they will tell you to go here and there , call you back , ultimately to find out that , ` ` i ' m sorry but we do not cover that ` ` .\n->as the hours in your day wind down , they will tell you to go here and there , call you back , ultimately to find out that , ` ` i ' m sorry but we do not cover that ` ` .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: i will constantly be thinking about how it will probably fall apart in a few moths .\n->i will constantly be thinking about how it will probably fall apart in a few moths .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i ' ve only found 2 other recent reviews of this product that had the same problem ( go ahead and search for them in the reviews ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve only found 2 other recent reviews of this product that had the same problem ( go ahead and search for them in the reviews ) .\n->", + "output": "{\"text\": \"i ' ve only found 2 other recent reviews of this product that had the same problem ( go ahead and search for them in the reviews ) .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: oh , and the nvidia card is plenty capable for gaming at medium settings at least ( grid autosport fps avg is in the 80s / 90s or so )\n->oh , and the nvidia card is plenty capable for gaming at medium settings at least ( grid autosport fps avg is in the 80s / 90s or so )\n[{'aspect': 'nvidia card', 'opinion': 'capable', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is freaking painful how long it can take games to load with that hard drive .\n->it is freaking painful how long it can take games to load with that hard drive .\n[{'aspect': 'hard drive', 'opinion': 'painful', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: volume was not working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvolume was not working .\n->", + "output": "{\"text\": \"volume was not working .\", \"labels\": \"[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n->i loved this chromebook but i had to return it bevause it had sound issues .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is a good product based on my experience - i have used this for almost a whole month .\n->this is a good product based on my experience - i have used this for almost a whole month .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: they would help me only if i bought an ongoing support contract with them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey would help me only if i bought an ongoing support contract with them .\n->", + "output": "{\"text\": \"they would help me only if i bought an ongoing support contract with them .\", \"labels\": \"[{'aspect': 'support contract', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love how fast it is , love that it can do everything i ' ve asked it to do so far in the two weeks i ' ve owned it , and i love how compact and easy it is to carry around .\n->love how fast it is , love that it can do everything i ' ve asked it to do so far in the two weeks i ' ve owned it , and i love how compact and easy it is to carry around .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n->the hot dogs are top notch , and they ' re slamwich is amazing !\n[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: horrible way to run a company so i returned the item .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhorrible way to run a company so i returned the item .\n->", + "output": "{\"text\": \"horrible way to run a company so i returned the item .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'company', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is also extremely well priced .\n->It is also extremely well priced .\n[{'aspect': 'priced', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great food , great decor , great service .\n->great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n->", + "output": "{\"text\": \"i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fan is almost always on even if you have it set to automatic , although it ' s not loud .\n->the fan is almost always on even if you have it set to automatic , although it ' s not loud .\n[{'aspect': 'fan', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\nExample:\ntext: it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n->it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n[{'aspect': 'NULL', 'opinion': 'not really bad', 'polarity': 'neutral', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i noticed yesterday that the laptop was not charging anymore for reasons i can not deduce .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni noticed yesterday that the laptop was not charging anymore for reasons i can not deduce .\n->", + "output": "{\"text\": \"i noticed yesterday that the laptop was not charging anymore for reasons i can not deduce .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: rao ' s has the best service and atmosphere in nyc .\n->rao ' s has the best service and atmosphere in nyc .\n[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: went there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .\n->went there with my wife and we had to wait for a table even though you could see there many that were empty with not reservation sigh on them .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it appears the adapter may still be good as there are a few sparks when plugging into power but the charging light on the laptop does not come on and the battery is not charging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit appears the adapter may still be good as there are a few sparks when plugging into power but the charging light on the laptop does not come on and the battery is not charging .\n->", + "output": "{\"text\": \"it appears the adapter may still be good as there are a few sparks when plugging into power but the charging light on the laptop does not come on and the battery is not charging .\", \"labels\": \"[{'aspect': 'charging light', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'adapter', 'opinion': 'good', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'charging light', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but basic stuff needs to be properly engineered and designed , and this machine had two huge problems right out of the gate .\n->but basic stuff needs to be properly engineered and designed , and this machine had two huge problems right out of the gate .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: dead pixel on display on arrival\n->dead pixel on display on arrival\n[{'aspect': 'display', 'opinion': 'dead', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: i will attempt to return it as i am out of the us right now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will attempt to return it as i am out of the us right now .\n->", + "output": "{\"text\": \"i will attempt to return it as i am out of the us right now .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is so cheap and the waiters are nice .\n->The food is so cheap and the waiters are nice .\n[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: noisy hdr , better with ssd ) works quickly .\n->noisy hdr , better with ssd ) works quickly .\n[{'aspect': 'hdr', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}, {'aspect': 'ssd', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\ntext: it is quite disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is quite disappointing .\n->", + "output": "{\"text\": \"it is quite disappointing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 1 , the touchscreen stopped working after 6 months for finger presses .\n->1 , the touchscreen stopped working after 6 months for finger presses .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: very satisfied with simplicity of use , the streamlining of the google products , and the considerable battery life .\n->very satisfied with simplicity of use , the streamlining of the google products , and the considerable battery life .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'google products', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'considerable', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i am really concerned that this was not strongly made and just pretty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am really concerned that this was not strongly made and just pretty .\n->", + "output": "{\"text\": \"i am really concerned that this was not strongly made and just pretty .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'just pretty', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n->the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'travel / feedback', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n->it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: now a few weeks later monitor has died again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow a few weeks later monitor has died again .\n->", + "output": "{\"text\": \"now a few weeks later monitor has died again .\", \"labels\": \"[{'aspect': 'monitor', 'opinion': 'died', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: need to play with it a bit more .\n->need to play with it a bit more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the decor is very simple but comfortable .\n->the decor is very simple but comfortable .\n[{'aspect': 'decor', 'opinion': 'simple', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: not worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot worth it .\n->", + "output": "{\"text\": \"not worth it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The highlight of the night was the mayonaisse for my side of fries I received from one of the food runners , which is not good considering the bill was nearly $ 100 .\n->The highlight of the night was the mayonaisse for my side of fries I received from one of the food runners , which is not good considering the bill was nearly $ 100 .\n[{'aspect': 'mayonaisse', 'opinion': 'highlight', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food runners', 'opinion': 'not good', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The food is inventive but still keeps traditional indian flavoring .\n->The food is inventive but still keeps traditional indian flavoring .\n[{'aspect': 'food', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i will return it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will return it .\n->", + "output": "{\"text\": \"i will return it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service -- friendly and attentive .\n->Service -- friendly and attentive .\n[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the blond wood decor is very soothing , the premium sake is excellent and the service is great .\n->the blond wood decor is very soothing , the premium sake is excellent and the service is great .\n[{'aspect': 'blond wood decor', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'premium sake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i thought it was a bad hdmi connection .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni thought it was a bad hdmi connection .\n->", + "output": "{\"text\": \"i thought it was a bad hdmi connection .\", \"labels\": \"[{'aspect': 'hdmi connection', 'opinion': 'bad', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n->it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'insane', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: perfect ` ` computer ` ` for my young child .\n->perfect ` ` computer ` ` for my young child .\n[{'aspect': 'computer', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this laptop is beautiful , and that ' s about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is beautiful , and that ' s about it .\n->", + "output": "{\"text\": \"this laptop is beautiful , and that ' s about it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery is far below what i expected .\n->the battery is far below what i expected .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n->While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n[{'aspect': 'room', 'opinion': 'not particularly comfortable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it was fully charged and i only turned it on a total of 3 times before the screen went blank .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was fully charged and i only turned it on a total of 3 times before the screen went blank .\n->", + "output": "{\"text\": \"it was fully charged and i only turned it on a total of 3 times before the screen went blank .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'the screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The manager claimed that he could not compensate us for anything on the bill which just shows the lack of sophistication from the entire group .\n->The manager claimed that he could not compensate us for anything on the bill which just shows the lack of sophistication from the entire group .\n[{'aspect': 'manager', 'opinion': 'lack of sophistication', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n->His wife Tanya , the hostess , completes the comforting atmosphere by being delightfully warm and gracious .\n[{'aspect': 'hostess', 'opinion': 'delightfully warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hostess', 'opinion': 'gracious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'comforting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: at all and it took several minutes to boot up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat all and it took several minutes to boot up .\n->", + "output": "{\"text\": \"at all and it took several minutes to boot up .\", \"labels\": \"[{'aspect': 'boot up', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: less wait time for me !\n->less wait time for me !\n[{'aspect': 'wait time', 'opinion': 'less', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i think it works well except for the wifi which is spotty .\n->i think it works well except for the wifi which is spotty .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'wifi', 'opinion': 'spotty', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: turned it off and on and the screen still stays black but you can hear it running .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nturned it off and on and the screen still stays black but you can hear it running .\n->", + "output": "{\"text\": \"turned it off and on and the screen still stays black but you can hear it running .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n->it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n[{'aspect': 'NULL', 'opinion': 'limited', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'operating system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: 1 month update : chromebook is still working great .\n->1 month update : chromebook is still working great .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: cons - no light to indicate caps lock .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncons - no light to indicate caps lock .\n->", + "output": "{\"text\": \"cons - no light to indicate caps lock .\", \"labels\": \"[{'aspect': 'caps lock', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touch screen works quite well and i have found myself watching videos in tablet mode with ease .\n->the touch screen works quite well and i have found myself watching videos in tablet mode with ease .\n[{'aspect': 'touch screen', 'opinion': 'well', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve never had bad service and the fish is fresh and delicious .\n->i ' ve never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: delete and power button too close to each other and looks the same , i accidentally pressed the power button couple of times .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelete and power button too close to each other and looks the same , i accidentally pressed the power button couple of times .\n->", + "output": "{\"text\": \"delete and power button too close to each other and looks the same , i accidentally pressed the power button couple of times .\", \"labels\": \"[{'aspect': 'delete and power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n->she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: however , our main course was wonderful .\n->however , our main course was wonderful .\n[{'aspect': 'main course', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the built - in speaker is below average .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe built - in speaker is below average .\n->", + "output": "{\"text\": \"the built - in speaker is below average .\", \"labels\": \"[{'aspect': 'built - in speaker', 'opinion': 'below average', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i looove their eggplant pizza , as well as their pastas !\n->i looove their eggplant pizza , as well as their pastas !\n[{'aspect': 'eggplant pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: tapping it on either end is hit or miss .\n->tapping it on either end is hit or miss .\n[{'aspect': 'tapping', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: pros - slim , lightweight laptop due to 8th gen core - i5 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npros - slim , lightweight laptop due to 8th gen core - i5 .\n->", + "output": "{\"text\": \"pros - slim , lightweight laptop due to 8th gen core - i5 .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'pros', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '8th gen core - i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speakers on this model are really nice as well .\n->the speakers on this model are really nice as well .\n[{'aspect': 'speakers', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n->it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n[{'aspect': 'restaurant', 'opinion': 'repulsive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: on day one , i had been using it for maybe 2 hours and it randomly shut off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non day one , i had been using it for maybe 2 hours and it randomly shut off .\n->", + "output": "{\"text\": \"on day one , i had been using it for maybe 2 hours and it randomly shut off .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i took one bite from the $ 24 salmon , and i have never , in the 17 years i have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in flatbush farms .\n->i took one bite from the $ 24 salmon , and i have never , in the 17 years i have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in flatbush farms .\n[{'aspect': 'salmon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'salmon', 'opinion': 'fishy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The rest of the dim sum , though pricey by Chinatown standards , is worth it .\n->The rest of the dim sum , though pricey by Chinatown standards , is worth it .\n[{'aspect': 'dim sum', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: then the next day , same thing , screen goes black and it dies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen the next day , same thing , screen goes black and it dies .\n->", + "output": "{\"text\": \"then the next day , same thing , screen goes black and it dies .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'dies', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the mediterranean salad , it is a true experience for your taste buds ! !\n->Try the mediterranean salad , it is a true experience for your taste buds ! !\n[{'aspect': 'mediterranean salad', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i have used this computer daily now for about 6 months , spending hours per day on it for an emt / paramedic class .\n->i have used this computer daily now for about 6 months , spending hours per day on it for an emt / paramedic class .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\ntext: im returning it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nim returning it .\n->", + "output": "{\"text\": \"im returning it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is nice and the hinge is sturdy .\n->the build quality is nice and the hinge is sturdy .\n[{'aspect': 'build quality', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: And they provided a delicious dessert on the house !\n->And they provided a delicious dessert on the house !\n[{'aspect': 'dessert', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i will constantly be thinking about how it will probably fall apart in a few moths .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will constantly be thinking about how it will probably fall apart in a few moths .\n->", + "output": "{\"text\": \"i will constantly be thinking about how it will probably fall apart in a few moths .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would definitely recommend sea if you like thai cuisine !\n->i would definitely recommend sea if you like thai cuisine !\n[{'aspect': 'thai cuisine', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: stepping into casa la femme last night was a true experience unlike any other in new york !\n->stepping into casa la femme last night was a true experience unlike any other in new york !\n[{'aspect': 'casa la femme', 'opinion': 'true', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: aside from that , laptop seems fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \naside from that , laptop seems fine .\n->", + "output": "{\"text\": \"aside from that , laptop seems fine .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dj is awesome , i have been there for my birthday and a bunch of other times with friends and i keep going back .\n->the dj is awesome , i have been there for my birthday and a bunch of other times with friends and i keep going back .\n[{'aspect': 'dj', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: great food , great prices , great service .\n->great food , great prices , great service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: feel like at max brightness it just isn ' t enough .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfeel like at max brightness it just isn ' t enough .\n->", + "output": "{\"text\": \"feel like at max brightness it just isn ' t enough .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: speakers are weak and the volume range tops off half way .\n->speakers are weak and the volume range tops off half way .\n[{'aspect': 'speakers', 'opinion': 'weak', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: no backlit keyboard is kinda a bummer but i digress .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno backlit keyboard is kinda a bummer but i digress .\n->", + "output": "{\"text\": \"no backlit keyboard is kinda a bummer but i digress .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was very good , a great deal , and the place its self was great .\n->The food was very good , a great deal , and the place its self was great .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n->The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n[{'aspect': 'Bagels', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'chewy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'gummy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the issue is that i got a faulty laptop and that ' s why the negative review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe issue is that i got a faulty laptop and that ' s why the negative review .\n->", + "output": "{\"text\": \"the issue is that i got a faulty laptop and that ' s why the negative review .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'negative', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very happy with this item .\n->i am very happy with this item .\n[{'aspect': 'item', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard is good .\n->the keyboard is good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: i went with the asus vivobook based on the good specs , better hdd and ram , reviews , and because i could pay for it over several months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni went with the asus vivobook based on the good specs , better hdd and ram , reviews , and because i could pay for it over several months .\n->", + "output": "{\"text\": \"i went with the asus vivobook based on the good specs , better hdd and ram , reviews , and because i could pay for it over several months .\", \"labels\": \"[{'aspect': 'asus vivobook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'hdd', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'ram', 'opinion': 'better', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is outstanding and the service is quick , friendly and very professional .\n->The food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: their only solution was for me to go through the whole rma process and mail the laptop back for repair which would take weeks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntheir only solution was for me to go through the whole rma process and mail the laptop back for repair which would take weeks .\n->", + "output": "{\"text\": \"their only solution was for me to go through the whole rma process and mail the laptop back for repair which would take weeks .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this flip 2 really is a fantastic chromebook , and the ability to run android apps only makes it better !\n->this flip 2 really is a fantastic chromebook , and the ability to run android apps only makes it better !\n[{'aspect': 'flip 2', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'android apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: this has worked great to overcome that problem .\n->this has worked great to overcome that problem .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' m amazed how bad this machine is for the money , and for being described as a ` ` mid - level gamer . ` `\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m amazed how bad this machine is for the money , and for being described as a ` ` mid - level gamer . ` `\n->", + "output": "{\"text\": \"i ' m amazed how bad this machine is for the money , and for being described as a ` ` mid - level gamer . ` `\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'machine', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * very weak wifi reception from the built - in antenna .\n->* very weak wifi reception from the built - in antenna .\n[{'aspect': 'wifi', 'opinion': 'weak', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: unhygienic\n->unhygienic\n[{'aspect': 'NULL', 'opinion': 'unhygienic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: it stutters on a 10 year old game .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit stutters on a 10 year old game .\n->", + "output": "{\"text\": \"it stutters on a 10 year old game .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the portions are small but being that the food was so good makes up for that .\n->the portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the pizza was great .\n->the pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n->", + "output": "{\"text\": \"the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\", \"labels\": \"[{'aspect': 'form factor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most importantly , food is excellent .\n->most importantly , food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: very good breads as well .\n->very good breads as well .\n[{'aspect': 'breads', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: thanks amazon for your great return policy !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthanks amazon for your great return policy !\n->", + "output": "{\"text\": \"thanks amazon for your great return policy !\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'return policy', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n->To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n->only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: super thin - love the color - had to completely remove the operating system though - too many viruses already on the computer when received - had to get my own operating system and re - install everything\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuper thin - love the color - had to completely remove the operating system though - too many viruses already on the computer when received - had to get my own operating system and re - install everything\n->", + "output": "{\"text\": \"super thin - love the color - had to completely remove the operating system though - too many viruses already on the computer when received - had to get my own operating system and re - install everything\", \"labels\": \"[{'aspect': 'operating system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n->I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n[{'aspect': 'noodle dishes', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n->Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n[{'aspect': 'congee', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'sweet tasting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'donut like deep fried dough they call Ow Ley Soh', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'donut like deep fried dough they call Ow Ley Soh', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 6 inch laptop it appears fragile the keyboard itself feels like the keys will pop out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n6 inch laptop it appears fragile the keyboard itself feels like the keys will pop out .\n->", + "output": "{\"text\": \"6 inch laptop it appears fragile the keyboard itself feels like the keys will pop out .\", \"labels\": \"[{'aspect': '6 inch laptop', 'opinion': 'fragile', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n->it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Very good service and very good prices .\n->Very good service and very good prices .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: sad that i can ' t return this one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsad that i can ' t return this one .\n->", + "output": "{\"text\": \"sad that i can ' t return this one .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'sad', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n->and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n[{'aspect': 'system', 'opinion': 'not worry', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\nExample:\ntext: in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n->in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: plus the screen is bland .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplus the screen is bland .\n->", + "output": "{\"text\": \"plus the screen is bland .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'bland', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they do n ' t concern much of customer ' s health , just want to make money .\n->they do n ' t concern much of customer ' s health , just want to make money .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n->the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n[{'aspect': 'NULL', 'opinion': 'not usable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: with this asus i ' m experiencing 2 very disappointing issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith this asus i ' m experiencing 2 very disappointing issues .\n->", + "output": "{\"text\": \"with this asus i ' m experiencing 2 very disappointing issues .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the battery broke after just 4 months from baying it am so disappointed with the product\n->the battery broke after just 4 months from baying it am so disappointed with the product\n[{'aspect': 'battery', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}, {'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngraphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n->", + "output": "{\"text\": \"graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\", \"labels\": \"[{'aspect': 'graphic', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not even going to bother to describe it ; it speaks for itself .\n->i ' m not even going to bother to describe it ; it speaks for itself .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i really can ' t say enough about this awesome laptop .\n->i really can ' t say enough about this awesome laptop .\n[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n->", + "output": "{\"text\": \"suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is one of the best comfort food places in the city .\n->This is one of the best comfort food places in the city .\n[{'aspect': 'comfort food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: prices are very good .\n->prices are very good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: this is very disappointing an causing me big issues while i write .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is very disappointing an causing me big issues while i write .\n->", + "output": "{\"text\": \"this is very disappointing an causing me big issues while i write .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is wonderful , tasty and filling , and the service is professional and friendly .\n->the food is wonderful , tasty and filling , and the service is professional and friendly .\n[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'filling', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The vibe is very relaxed and cozy , service was great and the food was excellent !\n->The vibe is very relaxed and cozy , service was great and the food was excellent !\n[{'aspect': 'vibe', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vibe', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\n->", + "output": "{\"text\": \"it looks great as design , fast , but this 2 issues make it the worst buy of the past 10 years for me .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend visiting this restaurant and having dinner and drinks !\n->i highly recommend visiting this restaurant and having dinner and drinks !\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the touch screen broke four months after i purchased it .\n->the touch screen broke four months after i purchased it .\n[{'aspect': 'touch screen', 'opinion': 'broke', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: the keys are easy to type on and the laptop itself is thin yet feels solid and well constructed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keys are easy to type on and the laptop itself is thin yet feels solid and well constructed .\n->", + "output": "{\"text\": \"the keys are easy to type on and the laptop itself is thin yet feels solid and well constructed .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'well constructed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Outstanding Bagels , but you get what you pay for .\n->Outstanding Bagels , but you get what you pay for .\n[{'aspect': 'Bagels', 'opinion': 'Outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: fans can get loud .\n->fans can get loud .\n[{'aspect': 'fans', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: this is a well constructed and powerful laptop that is perfect for everyday use as well as some otherwise intensive task such as large spreadsheets or presentations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a well constructed and powerful laptop that is perfect for everyday use as well as some otherwise intensive task such as large spreadsheets or presentations .\n->", + "output": "{\"text\": \"this is a well constructed and powerful laptop that is perfect for everyday use as well as some otherwise intensive task such as large spreadsheets or presentations .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'well constructed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great little laptop for the money .\n->great little laptop for the money .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i double checked the outside labels and upon opening the box saw that the laptop had several deep scratches on the bottom of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni double checked the outside labels and upon opening the box saw that the laptop had several deep scratches on the bottom of it .\n->", + "output": "{\"text\": \"i double checked the outside labels and upon opening the box saw that the laptop had several deep scratches on the bottom of it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am terribly disappointed with obviously a poor qc by samsung .\n->i am terribly disappointed with obviously a poor qc by samsung .\n[{'aspect': 'qc', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}, {'aspect': 'qc', 'opinion': 'poor', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\nExample:\ntext: stepping into casa la femme last night was a true experience unlike any other in new york !\n->stepping into casa la femme last night was a true experience unlike any other in new york !\n[{'aspect': 'casa la femme', 'opinion': 'true', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i contacted amazon and have been issued a refund and ordered a replacement .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni contacted amazon and have been issued a refund and ordered a replacement .\n->", + "output": "{\"text\": \"i contacted amazon and have been issued a refund and ordered a replacement .\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The hostess and the waitress were incredibly rude and did everything they could to rush us out .\n->The hostess and the waitress were incredibly rude and did everything they could to rush us out .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i use this laptop for work .\n->i use this laptop for work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: the m key popped off during initial setup .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe m key popped off during initial setup .\n->", + "output": "{\"text\": \"the m key popped off during initial setup .\", \"labels\": \"[{'aspect': 'm key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing i did not like about the design is the fact that the speakers are on the bottom of the unit .\n->the only thing i did not like about the design is the fact that the speakers are on the bottom of the unit .\n[{'aspect': 'speakers', 'opinion': 'not like', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n->i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: amazon is 2 - day shipping me a replacement .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazon is 2 - day shipping me a replacement .\n->", + "output": "{\"text\": \"amazon is 2 - day shipping me a replacement .\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Indoor was very cozy and cute .\n->Indoor was very cozy and cute .\n[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve been using it for almost three weeks now and it has not let me down .\n->i ' ve been using it for almost three weeks now and it has not let me down .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i love it at first , but today my laptop wasn ' t not charging any more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love it at first , but today my laptop wasn ' t not charging any more .\n->", + "output": "{\"text\": \"i love it at first , but today my laptop wasn ' t not charging any more .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n->i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n[{'aspect': 'item', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'item', 'opinion': 'perfect', 'polarity': 'negative', 'category': 'SHIPPING#QUALITY'}]\nExample:\ntext: My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n->My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n[{'aspect': 'mesclun', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ice cream', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'courses', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i noticed today the laptop was not charging anymore .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni noticed today the laptop was not charging anymore .\n->", + "output": "{\"text\": \"i noticed today the laptop was not charging anymore .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n->The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it is very very disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is very very disappointing .\n->", + "output": "{\"text\": \"it is very very disappointing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: big and soft as well as good lunch food .\n->big and soft as well as good lunch food .\n[{'aspect': 'lunch food', 'opinion': 'big', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch food', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - touch screen is very accurate\n->- touch screen is very accurate\n[{'aspect': 'touch screen', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: edit : reinstalled the driver now the audio is better on earphones .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nedit : reinstalled the driver now the audio is better on earphones .\n->", + "output": "{\"text\": \"edit : reinstalled the driver now the audio is better on earphones .\", \"labels\": \"[{'aspect': 'audio', 'opinion': 'better', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i would buy again , especially at this price .\n->i would buy again , especially at this price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: camera is sd but not a problem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncamera is sd but not a problem .\n->", + "output": "{\"text\": \"camera is sd but not a problem .\", \"labels\": \"[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n->battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great atmoshere and worth every bit .\n->Great atmoshere and worth every bit .\n[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: fingerprint scanner works well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfingerprint scanner works well .\n->", + "output": "{\"text\": \"fingerprint scanner works well .\", \"labels\": \"[{'aspect': 'fingerprint scanner', 'opinion': 'well', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will never buy an msi product again , and will tell every person i know to stay far away .\n->will never buy an msi product again , and will tell every person i know to stay far away .\n[{'aspect': 'msi product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: however , it does feel like a sturdy hinge .\n->however , it does feel like a sturdy hinge .\n[{'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: back lit keyboard should be a standard by now !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nback lit keyboard should be a standard by now !\n->", + "output": "{\"text\": \"back lit keyboard should be a standard by now !\", \"labels\": \"[{'aspect': 'back lit keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love asus but this one is super slow !\n->i love asus but this one is super slow !\n[{'aspect': 'asus', 'opinion': 'love', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: if you ' re looking for a good chromebook , this is the one for you .\n->if you ' re looking for a good chromebook , this is the one for you .\n[{'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: battery is all day amazing\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery is all day amazing\n->", + "output": "{\"text\": \"battery is all day amazing\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 'm still mad that i had to pay for lousy food .\n->I 'm still mad that i had to pay for lousy food .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the atmosphere was great .\n->the atmosphere was great .\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: with ssd lightning fast start up\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith ssd lightning fast start up\n->", + "output": "{\"text\": \"with ssd lightning fast start up\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n->the lobster knuckles ( special of the day ) were ok , but pretty tasteless .\n[{'aspect': 'lobster knuckles', 'opinion': 'ok', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'lobster knuckles', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n->I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n[{'aspect': 'all you can eat deal', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\ntext: no back light keyboard\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno back light keyboard\n->", + "output": "{\"text\": \"no back light keyboard\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n->Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n[{'aspect': 'congee', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'sweet tasting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'donut like deep fried dough they call Ow Ley Soh', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'donut like deep fried dough they call Ow Ley Soh', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the salads are delicious , both refreshing and very spicy .\n->the salads are delicious , both refreshing and very spicy .\n[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'salads', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: performance is mediocre\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nperformance is mediocre\n->", + "output": "{\"text\": \"performance is mediocre\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard has excellent travel and just feels right .\n->the keyboard has excellent travel and just feels right .\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'right', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: drawbacks : service is slow and they do n ' t toast !\n->drawbacks : service is slow and they do n ' t toast !\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: consideration : if you are of average frame and strength then this build will be perfect and the keyboard flex and body is the perfect fit between lightweight and rigidity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nconsideration : if you are of average frame and strength then this build will be perfect and the keyboard flex and body is the perfect fit between lightweight and rigidity .\n->", + "output": "{\"text\": \"consideration : if you are of average frame and strength then this build will be perfect and the keyboard flex and body is the perfect fit between lightweight and rigidity .\", \"labels\": \"[{'aspect': 'build', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'flex', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'body', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'body', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'body', 'opinion': 'rigidity .', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n->The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n[{'aspect': 'sauce', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck noodles', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n->even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'working unit', 'opinion': 'outweighs', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: my friends , both female and male , are going to by this set up for their travel needs but for me it is lacking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy friends , both female and male , are going to by this set up for their travel needs but for me it is lacking .\n->", + "output": "{\"text\": \"my friends , both female and male , are going to by this set up for their travel needs but for me it is lacking .\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - performance can be stuttering when under heavy load .\n->- performance can be stuttering when under heavy load .\n[{'aspect': 'performance', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n->we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n[{'aspect': 'r11', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this adds to my need for a strong system and willing to sacrifice some weight for strength .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis adds to my need for a strong system and willing to sacrifice some weight for strength .\n->", + "output": "{\"text\": \"this adds to my need for a strong system and willing to sacrifice some weight for strength .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'strong', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great pizza and fantastic service .\n->Great pizza and fantastic service .\n[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: one of my favorite places in manhattan .\n->one of my favorite places in manhattan .\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: best for : if you are looking for a travel laptop and are planning on doing light work this is an amazing buy for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest for : if you are looking for a travel laptop and are planning on doing light work this is an amazing buy for the price .\n->", + "output": "{\"text\": \"best for : if you are looking for a travel laptop and are planning on doing light work this is an amazing buy for the price .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: netflix and hulu seem to be working for the most part so far , but amazon prime and xfinity stream are both having issues .\n->netflix and hulu seem to be working for the most part so far , but amazon prime and xfinity stream are both having issues .\n[{'aspect': 'netflix', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'hulu seem', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'amazon prime', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'xfinity stream', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Decor is charming .\n->Decor is charming .\n[{'aspect': 'Decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\ntext: not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n->", + "output": "{\"text\": \"not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'battery charger', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A great place to meet up for some food and drinks ...\n->A great place to meet up for some food and drinks ...\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: dessert is a joke . . . dont bother\n->dessert is a joke . . . dont bother\n[{'aspect': 'dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: even with the fan and heat if you use a cool mat on your lap you will be find especially with the screen being amazing for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven with the fan and heat if you use a cool mat on your lap you will be find especially with the screen being amazing for the price .\n->", + "output": "{\"text\": \"even with the fan and heat if you use a cool mat on your lap you will be find especially with the screen being amazing for the price .\", \"labels\": \"[{'aspect': 'fan', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}, {'aspect': 'screen', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is between 4 to 7 hours depending on what i ' m doing .\n->battery life is between 4 to 7 hours depending on what i ' m doing .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n->We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: for the price it is an amazing starting point and hard to beat especially with such an amazing brand such as asus .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the price it is an amazing starting point and hard to beat especially with such an amazing brand such as asus .\n->", + "output": "{\"text\": \"for the price it is an amazing starting point and hard to beat especially with such an amazing brand such as asus .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my boyfriend had the new england chowder it was good but i think the award should go to the lobster bisque .\n->my boyfriend had the new england chowder it was good but i think the award should go to the lobster bisque .\n[{'aspect': 'new england chowder', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster bisque', 'opinion': 'award', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i ' m here to eat and enjoy the company of friends , seeking a pleasant experience .\n->i know , you were too busy showing off your vintage tee shirt and looking bored , but my agenda is i ' m here to eat and enjoy the company of friends , seeking a pleasant experience .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: oh and this is a beautiful machine and the lid is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noh and this is a beautiful machine and the lid is amazing .\n->", + "output": "{\"text\": \"oh and this is a beautiful machine and the lid is amazing .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'lid', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place has ruined me for neighborhood sushi .\n->this place has ruined me for neighborhood sushi .\n[{'aspect': 'sushi', 'opinion': 'ruined', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food was pretty tradional but it was hot and good with large portions .\n->The food was pretty tradional but it was hot and good with large portions .\n[{'aspect': 'food', 'opinion': 'tradional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i like the laptop for it ' s hardware , and it ' s working properly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like the laptop for it ' s hardware , and it ' s working properly .\n->", + "output": "{\"text\": \"i like the laptop for it ' s hardware , and it ' s working properly .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: power button is right next to the delete key .\n->power button is right next to the delete key .\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: I got the $ 10 10-piece dim sum combo , every bite of which was great .\n->I got the $ 10 10-piece dim sum combo , every bite of which was great .\n[{'aspect': 'dim sum combo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the actual laptop is very much darker and blue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe actual laptop is very much darker and blue .\n->", + "output": "{\"text\": \"the actual laptop is very much darker and blue .\", \"labels\": \"[{'aspect': 'actual laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best pc bang for this level of buck .\n->best pc bang for this level of buck .\n[{'aspect': 'pc bang', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: chrome has come a long way to be sure and in its optimized avatar on this system its very snappy .\n->chrome has come a long way to be sure and in its optimized avatar on this system its very snappy .\n[{'aspect': 'chrome', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i ' m not at all happy about that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m not at all happy about that .\n->", + "output": "{\"text\": \"i ' m not at all happy about that .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not at all happy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The atmosphere is unheralded , the service impecible , and the food magnificant .\n->The atmosphere is unheralded , the service impecible , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop has an amazing price for the hardware it offers .\n->this laptop has an amazing price for the hardware it offers .\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'hardware', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'HARDWARE#PRICE'}]\ntext: the new computer failed again with the same error .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe new computer failed again with the same error .\n->", + "output": "{\"text\": \"the new computer failed again with the same error .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen looks fantastic and movies look great .\n->the screen looks fantastic and movies look great .\n[{'aspect': 'screen', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: great toppings definitely a place you need to check out for late night munchies or a mid day boost !\n->great toppings definitely a place you need to check out for late night munchies or a mid day boost !\n[{'aspect': 'toppings', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i am sure amazon will exchange it again but it is not worth the time and hassle .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am sure amazon will exchange it again but it is not worth the time and hassle .\n->", + "output": "{\"text\": \"i am sure amazon will exchange it again but it is not worth the time and hassle .\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n->The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n[{'aspect': 'parathas', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kebabs', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery life is horrible though .\n->battery life is horrible though .\n[{'aspect': 'battery life', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the speakers are also not great and the max volume on watching netflix or other videos is rather quite .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speakers are also not great and the max volume on watching netflix or other videos is rather quite .\n->", + "output": "{\"text\": \"the speakers are also not great and the max volume on watching netflix or other videos is rather quite .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'not great', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First the wrong bread came out with the appetizer , then when i tried to order a second glass of wine for my main course ...\n->First the wrong bread came out with the appetizer , then when i tried to order a second glass of wine for my main course ...\n[{'aspect': 'bread', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'glass of wine', 'opinion': 'second', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Toons has recently been redone , so it 's now a very attractive space .\n->Toons has recently been redone , so it 's now a very attractive space .\n[{'aspect': 'space', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n->", + "output": "{\"text\": \"its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the best laptop for its price .\n->the best laptop for its price .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: there is nothing i can ' t do on this amazing thing .\n->there is nothing i can ' t do on this amazing thing .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n->", + "output": "{\"text\": \"the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: would never go back there .\n->would never go back there .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i wish i could like this place more , and i wish someone would retrain the staff .\n->i wish i could like this place more , and i wish someone would retrain the staff .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: overall , i ' m not very pleased with this computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , i ' m not very pleased with this computer .\n->", + "output": "{\"text\": \"overall , i ' m not very pleased with this computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'not very pleased', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sorry not a fan of windows 10 .\n->sorry not a fan of windows 10 .\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: awesome\n->awesome\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: not happy with this one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot happy with this one .\n->", + "output": "{\"text\": \"not happy with this one .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not happy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s extremely fast and has very little lag when opening pages or surfing the web .\n->it ' s extremely fast and has very little lag when opening pages or surfing the web .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n->This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the computer worked well for a few weeks , then the screen backlight died .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer worked well for a few weeks , then the screen backlight died .\n->", + "output": "{\"text\": \"the computer worked well for a few weeks , then the screen backlight died .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'screen backlight', 'opinion': 'died', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just want to warn you all - do n ' t waste your time and money .\n->just want to warn you all - do n ' t waste your time and money .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Baluchi 's has solid food and a nice decor at reasonable prices .\n->Baluchi 's has solid food and a nice decor at reasonable prices .\n[{'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the laptop does n ' t work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop does n ' t work .\n->", + "output": "{\"text\": \"the laptop does n ' t work .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\n->The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\n[{'aspect': 'mussles', 'opinion': 'fishiest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seabass', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'goat cheese salad', 'opinion': 'missing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'penne w/ chicken', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: performance is mediocre\n->performance is mediocre\n[{'aspect': 'NULL', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i ' ve tried every single option and still ca n ' t use it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve tried every single option and still ca n ' t use it .\n->", + "output": "{\"text\": \"i ' ve tried every single option and still ca n ' t use it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: saul is the best restaurant on smith street and in brooklyn .\n->saul is the best restaurant on smith street and in brooklyn .\n[{'aspect': 'saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: - beautiful , bright ips screen with full 1080p resolution\n->- beautiful , bright ips screen with full 1080p resolution\n[{'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: unable to contact asus support for help .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunable to contact asus support for help .\n->", + "output": "{\"text\": \"unable to contact asus support for help .\", \"labels\": \"[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not much of a selection of bottled beer either , we went with brahma .\n->not much of a selection of bottled beer either , we went with brahma .\n[{'aspect': 'selection of bottled beer', 'opinion': 'not much', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: however , i haven ' t had any disappoint with the battery .\n->however , i haven ' t had any disappoint with the battery .\n[{'aspect': 'battery', 'opinion': \"' t had any disappoint\", 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: will never buy an asus product again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill never buy an asus product again .\n->", + "output": "{\"text\": \"will never buy an asus product again .\", \"labels\": \"[{'aspect': 'asus product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard and display quality have always been asus strengths in my experience .\n->the keyboard and display quality have always been asus strengths in my experience .\n[{'aspect': 'display', 'opinion': 'strengths', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: then suddenly it needs a software update which made my laptop crash .\n->then suddenly it needs a software update which made my laptop crash .\n[{'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: should ' ve stick with my pixelbook which was awesome and never have me trouble .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshould ' ve stick with my pixelbook which was awesome and never have me trouble .\n->", + "output": "{\"text\": \"should ' ve stick with my pixelbook which was awesome and never have me trouble .\", \"labels\": \"[{'aspect': 'pixelbook', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everythig about it - especially the shows and actors .\n->i loved everythig about it - especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The food itself was just ok - nothing spectacular - but the service was awful .\n->The food itself was just ok - nothing spectacular - but the service was awful .\n[{'aspect': 'food', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\ntext: return window is just a month , nothing can be done and now i am on mercy of asus .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreturn window is just a month , nothing can be done and now i am on mercy of asus .\n->", + "output": "{\"text\": \"return window is just a month , nothing can be done and now i am on mercy of asus .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's charmingly small and that leads to an atmoshere that is extremely cozy and romantic , even .\n->It 's charmingly small and that leads to an atmoshere that is extremely cozy and romantic , even .\n[{'aspect': 'atmoshere', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshere', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The cream cheeses are out of this world and I love that coffee ! !\n->The cream cheeses are out of this world and I love that coffee ! !\n[{'aspect': 'cream cheeses', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'coffee', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: no backlighting on the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno backlighting on the keyboard .\n->", + "output": "{\"text\": \"no backlighting on the keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I really liked this place .\n->I really liked this place .\n[{'aspect': 'place', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n->chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n[{'aspect': 'chromeos', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chromeos', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: ( i had to check that the caps lock was off after typing that last word . )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( i had to check that the caps lock was off after typing that last word . )\n->", + "output": "{\"text\": \"( i had to check that the caps lock was off after typing that last word . )\", \"labels\": \"[{'aspect': 'caps lock', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no dvd drive , but who uses those anymore anyway ?\n->no dvd drive , but who uses those anymore anyway ?\n[{'aspect': 'dvd drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: i almost hesititate to write a review because the atmosphere was so great and i would hate for it too become to crowded .\n->i almost hesititate to write a review because the atmosphere was so great and i would hate for it too become to crowded .\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n->", + "output": "{\"text\": \"for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fish is so very fresh .\n->Fish is so very fresh .\n[{'aspect': 'Fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n->it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the fan blows like crazy and it makes so much noise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fan blows like crazy and it makes so much noise .\n->", + "output": "{\"text\": \"the fan blows like crazy and it makes so much noise .\", \"labels\": \"[{'aspect': 'fan', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n->my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n[{'aspect': 'Scallion Pancake', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Scallion Pancake', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shredded Squid Family Style', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shuizhu Fish', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the track pad , which i don ' t use , is also highly responsive .\n->the track pad , which i don ' t use , is also highly responsive .\n[{'aspect': 'track pad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i don ' t even think i will be able to work on this it is so distracting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t even think i will be able to work on this it is so distracting .\n->", + "output": "{\"text\": \"i don ' t even think i will be able to work on this it is so distracting .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'distracting', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: oh , and there ' s hookah .\n->oh , and there ' s hookah .\n[{'aspect': 'hookah', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n->Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n[{'aspect': 'fresh mozzerella slices', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozzerella slices', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Plain Cheese slice', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this laptop was delivered with the screen broken , it was the christmas gift and when we removed the gift paper se found a laptop thant not worked , that ' s unfair and that should not be done yo any people on christmas night\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop was delivered with the screen broken , it was the christmas gift and when we removed the gift paper se found a laptop thant not worked , that ' s unfair and that should not be done yo any people on christmas night\n->", + "output": "{\"text\": \"this laptop was delivered with the screen broken , it was the christmas gift and when we removed the gift paper se found a laptop thant not worked , that ' s unfair and that should not be done yo any people on christmas night\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'unfair', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu may be small , but everything on it is delicious .\n->The menu may be small , but everything on it is delicious .\n[{'aspect': 'menu', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: all of the pizzas are terrific and the price is even better !\n->all of the pizzas are terrific and the price is even better !\n[{'aspect': 'pizzas', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: battery life is only 3 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is only 3 .\n->", + "output": "{\"text\": \"battery life is only 3 .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n->do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: but seemed very poorly made for the money .\n->but seemed very poorly made for the money .\n[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: high speed laptop\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhigh speed laptop\n->", + "output": "{\"text\": \"high speed laptop\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'high', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n->but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n->Whenever you need a Sushi fix , Mizu will be there with quality fish and great service .\n[{'aspect': 'fish', 'opinion': 'quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: mousepad doesn ' t work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmousepad doesn ' t work .\n->", + "output": "{\"text\": \"mousepad doesn ' t work .\", \"labels\": \"[{'aspect': 'mousepad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was good too .\n->The food was good too .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n->it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: i have used this laptop only for work and the battery lasts and hour at most on mid performance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have used this laptop only for work and the battery lasts and hour at most on mid performance .\n->", + "output": "{\"text\": \"i have used this laptop only for work and the battery lasts and hour at most on mid performance .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n->this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n[{'aspect': 'product', 'opinion': 'not an inexpensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: * the screen is more than adequate for me , although i have not used it outside much yet .\n->* the screen is more than adequate for me , although i have not used it outside much yet .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: will not be buying asus again\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill not be buying asus again\n->", + "output": "{\"text\": \"will not be buying asus again\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: personal pans are the perfect size for those hungry nights .\n->personal pans are the perfect size for those hungry nights .\n[{'aspect': 'personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: two cons i ' m disappointed about is the battery life and the flimsy keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntwo cons i ' m disappointed about is the battery life and the flimsy keyboard .\n->", + "output": "{\"text\": \"two cons i ' m disappointed about is the battery life and the flimsy keyboard .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'cons', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Went here last night - nice decor , good service , but the food was surprisingly excellent .\n->Went here last night - nice decor , good service , but the food was surprisingly excellent .\n[{'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n->My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n[{'aspect': 'mesclun', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ice cream', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'courses', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: has a genaric feel to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhas a genaric feel to it .\n->", + "output": "{\"text\": \"has a genaric feel to it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'genaric', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish i could like this place more , and i wish someone would retrain the staff .\n->i wish i could like this place more , and i wish someone would retrain the staff .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: he was ecstatic at the power , the screen , the layout - pretty much everything .\n->he was ecstatic at the power , the screen , the layout - pretty much everything .\n[{'aspect': 'power', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'screen', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: they seemed to do nothing : fixing it was apparently my job .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey seemed to do nothing : fixing it was apparently my job .\n->", + "output": "{\"text\": \"they seemed to do nothing : fixing it was apparently my job .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything about the experience has been terrible .\n->everything about the experience has been terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Saul is the best restaurant on Smith Street and in Brooklyn .\n->Saul is the best restaurant on Smith Street and in Brooklyn .\n[{'aspect': 'Saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n->", + "output": "{\"text\": \"so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\", \"labels\": \"[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the staff is absolutely professional ! !\n->all the staff is absolutely professional ! !\n[{'aspect': 'staff', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: It is so easy to get a reservation at a top place in NYC with a week 's notice .\n->It is so easy to get a reservation at a top place in NYC with a week 's notice .\n[{'aspect': 'reservation', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' ve had to reset the computer multiple times .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had to reset the computer multiple times .\n->", + "output": "{\"text\": \"i ' ve had to reset the computer multiple times .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only three months in , and the laptop won ' t charge .\n->only three months in , and the laptop won ' t charge .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i went online and found that the 1 year acer warranty had already expired .\n->i went online and found that the 1 year acer warranty had already expired .\n[{'aspect': 'acer warranty', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'WARRANTY#GENERAL'}]\ntext: i returned it because the speaker is dead low .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni returned it because the speaker is dead low .\n->", + "output": "{\"text\": \"i returned it because the speaker is dead low .\", \"labels\": \"[{'aspect': 'speaker', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i absolutely love this laptop .\n->i absolutely love this laptop .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: do your research , learn how to optimize your experience , and you ' ll love it !\n->do your research , learn how to optimize your experience , and you ' ll love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: good laptop but the webcam isn ' t good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood laptop but the webcam isn ' t good .\n->", + "output": "{\"text\": \"good laptop but the webcam isn ' t good .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'webcam', 'opinion': \"' t good\", 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .\n->I tend to judge a sushi restaurant by its sea urchin , which was heavenly at sushi rose .\n[{'aspect': 'sea urchin', 'opinion': 'heavenly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Service is not what one would expect from a joint in this price category .\n->Service is not what one would expect from a joint in this price category .\n[{'aspect': 'Service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'price category', 'opinion': 'expect', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i would not recommend you buying this laptop , the specs are fine for the price , but the hardware is rubbish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would not recommend you buying this laptop , the specs are fine for the price , but the hardware is rubbish .\n->", + "output": "{\"text\": \"i would not recommend you buying this laptop , the specs are fine for the price , but the hardware is rubbish .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'specs', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'hardware', 'opinion': 'rubbish', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We visited Bread Bar during January restaurant week and were so pleased with the menu selections and service .\n->We visited Bread Bar during January restaurant week and were so pleased with the menu selections and service .\n[{'aspect': 'menu selections', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i really liked that this chromebook came with 64gb of space but i really don ' t see how someone may fill that up unless they opt to sync their google drive offline or something .\n->i really liked that this chromebook came with 64gb of space but i really don ' t see how someone may fill that up unless they opt to sync their google drive offline or something .\n[{'aspect': 'chromebook', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: power button is right next to the delete key .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npower button is right next to the delete key .\n->", + "output": "{\"text\": \"power button is right next to the delete key .\", \"labels\": \"[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was great .\n->The food was great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this was a repeat visit and we ' ll definitely be back again .\n->this was a repeat visit and we ' ll definitely be back again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: all around , a very lacking laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall around , a very lacking laptop .\n->", + "output": "{\"text\": \"all around , a very lacking laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: we waited at the bar and had martinis that were just right .\n->we waited at the bar and had martinis that were just right .\n[{'aspect': 'martinis', 'opinion': 'right', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: * * returning the device - - that unhappy with the item .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* * returning the device - - that unhappy with the item .\n->", + "output": "{\"text\": \"* * returning the device - - that unhappy with the item .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'item', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i went online and found that the 1 year acer warranty had already expired .\n->i went online and found that the 1 year acer warranty had already expired .\n[{'aspect': 'acer warranty', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'WARRANTY#GENERAL'}]\nExample:\ntext: this is now my fastest - charging device .\n->this is now my fastest - charging device .\n[{'aspect': 'device', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: dead pixel on display on arrival\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndead pixel on display on arrival\n->", + "output": "{\"text\": \"dead pixel on display on arrival\", \"labels\": \"[{'aspect': 'display', 'opinion': 'dead', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we wo n ' t go to this place again for a good meal .\n->we wo n ' t go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the pic quality is pretty good , though not 4k , the sound is pretty good for a laptop that is less than a half inch thick and weighs less than 3 pounds .\n->the pic quality is pretty good , though not 4k , the sound is pretty good for a laptop that is less than a half inch thick and weighs less than 3 pounds .\n[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: the laptop charger has sparked repeatedly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop charger has sparked repeatedly .\n->", + "output": "{\"text\": \"the laptop charger has sparked repeatedly .\", \"labels\": \"[{'aspect': 'laptop charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n->i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: only thing i ' d change would be the hard drive .\n->only thing i ' d change would be the hard drive .\n[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\ntext: i will be returning this item .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will be returning this item .\n->", + "output": "{\"text\": \"i will be returning this item .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was attentive .\n->The service was attentive .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s super portable and sleek .\n->it ' s super portable and sleek .\n[{'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: do not buy it will not search what you type in i want my money back\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not buy it will not search what you type in i want my money back\n->", + "output": "{\"text\": \"do not buy it will not search what you type in i want my money back\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We all ate pasta entre'es , which were great .\n->We all ate pasta entre'es , which were great .\n[{'aspect': \"pasta entre'es\", 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this computer arrived fine but will not charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer arrived fine but will not charge .\n->", + "output": "{\"text\": \"this computer arrived fine but will not charge .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer is fast , as the specs would have you believe , but i ' ve only had it about a week , and it has already crashed so bad once that i had to do a factory reset on it ( though , to be fair , the computer made the reboot super easy , so that was nice ) .\n->this computer is fast , as the specs would have you believe , but i ' ve only had it about a week , and it has already crashed so bad once that i had to do a factory reset on it ( though , to be fair , the computer made the reboot super easy , so that was nice ) .\n[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'computer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: I had their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .\n->I had their eggs benedict for brunch , which were the worst in my entire life , I tried removing the hollondaise sauce completely that was how failed it was .\n[{'aspect': 'eggs benedict', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: how can a brand new computer not charge properly ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhow can a brand new computer not charge properly ?\n->", + "output": "{\"text\": \"how can a brand new computer not charge properly ?\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: personally i like the margherita pizza better , but they are all good .\n->personally i like the margherita pizza better , but they are all good .\n[{'aspect': 'margherita pizza', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the service was friendly and the atmosphere was casual .\n->the service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\ntext: screen backlight stopped working after just one month of light use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen backlight stopped working after just one month of light use .\n->", + "output": "{\"text\": \"screen backlight stopped working after just one month of light use .\", \"labels\": \"[{'aspect': 'screen backlight', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n->i fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork .\n[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of bbq roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Overall a disappointing experience for that price category .\n->Overall a disappointing experience for that price category .\n[{'aspect': 'price', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i received this laptop promptly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni received this laptop promptly .\n->", + "output": "{\"text\": \"i received this laptop promptly .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked great .\n->it worked great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this staff should be fired .\n->this staff should be fired .\n[{'aspect': 'staff', 'opinion': 'fired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: great battery life , a matte screen ( non - glossy ) full hd .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat battery life , a matte screen ( non - glossy ) full hd .\n->", + "output": "{\"text\": \"great battery life , a matte screen ( non - glossy ) full hd .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'matte screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i go to type something , it sticks and will not release .\n->when i go to type something , it sticks and will not release .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it will however win with substance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit will however win with substance .\n->", + "output": "{\"text\": \"it will however win with substance .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'win', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the prices were cheap compared to the quality of service and food .\n->the prices were cheap compared to the quality of service and food .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: there are only two cons , the sound quality and the overheating .\n->there are only two cons , the sound quality and the overheating .\n[{'aspect': 'sound quality', 'opinion': 'cons', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'overheating', 'opinion': 'cons', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: its about what is inside of this amazing product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits about what is inside of this amazing product .\n->", + "output": "{\"text\": \"its about what is inside of this amazing product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was just average . . . if they lowered the prices just a bit , it would be a bigger draw .\n->food was just average . . . if they lowered the prices just a bit , it would be a bigger draw .\n[{'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: The service was excellent and the food was delicious .\n->The service was excellent and the food was delicious .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen quality is excellent , and i am fussy due to my interest in digital imagery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen quality is excellent , and i am fussy due to my interest in digital imagery .\n->", + "output": "{\"text\": \"the screen quality is excellent , and i am fussy due to my interest in digital imagery .\", \"labels\": \"[{'aspect': 'screen quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s very easy to set up and use\n->it ' s very easy to set up and use\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the trackpad works well and the screen display is great too .\n->the trackpad works well and the screen display is great too .\n[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'screen display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: i can tell you for web surfing , the battery life is on par with what is advertised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can tell you for web surfing , the battery life is on par with what is advertised .\n->", + "output": "{\"text\": \"i can tell you for web surfing , the battery life is on par with what is advertised .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : streamlined and simple ; easy to use overall ; easy to find / delete pics & files ; good price ; looks nice from the outside , with the lid down ; good 14 ` ` screen size that ' s surprisingly hard to find\n->pros : streamlined and simple ; easy to use overall ; easy to find / delete pics & files ; good price ; looks nice from the outside , with the lid down ; good 14 ` ` screen size that ' s surprisingly hard to find\n[{'aspect': 'NULL', 'opinion': 'streamlined', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: i wo n ' t go back unless someone else is footing the bill .\n->i wo n ' t go back unless someone else is footing the bill .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: i could n ' t have put my feelings about my new acer aspire e 15 e5 - 576g - 5762 better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni could n ' t have put my feelings about my new acer aspire e 15 e5 - 576g - 5762 better .\n->", + "output": "{\"text\": \"i could n ' t have put my feelings about my new acer aspire e 15 e5 - 576g - 5762 better .\", \"labels\": \"[{'aspect': 'acer aspire e 15 e5 - 576g - 5762', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the drinks are great , especially when made by raymond .\n->the drinks are great , especially when made by raymond .\n[{'aspect': 'drinks', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'raymond', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: am i just unlucky , or was this a bad batch ?\n->am i just unlucky , or was this a bad batch ?\n[{'aspect': 'NULL', 'opinion': 'unlucky', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: the 8th generation processor and ssd make for a very snappy computer , and the easy upgradablility is helpful now and will be useful in the future .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 8th generation processor and ssd make for a very snappy computer , and the easy upgradablility is helpful now and will be useful in the future .\n->", + "output": "{\"text\": \"the 8th generation processor and ssd make for a very snappy computer , and the easy upgradablility is helpful now and will be useful in the future .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': '8th generation processor', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff ignored my friends and I the entire time we were there .\n->The staff ignored my friends and I the entire time we were there .\n[{'aspect': 'staff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Fish was overdone .\n->Fish was overdone .\n[{'aspect': 'Fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i / o : tons of ports , a dvd burner , solid keyboard with good feel to the key strokes , and a very precise and responsive trackpad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni / o : tons of ports , a dvd burner , solid keyboard with good feel to the key strokes , and a very precise and responsive trackpad .\n->", + "output": "{\"text\": \"i / o : tons of ports , a dvd burner , solid keyboard with good feel to the key strokes , and a very precise and responsive trackpad .\", \"labels\": \"[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'dvd burner', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OPTICAL_DRIVES#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'key strokes', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'key strokes', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'trackpad', 'opinion': 'precise', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza is terrific , as is homemade pasta .\n->Pizza is terrific , as is homemade pasta .\n[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n->I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n[{'aspect': 'meal', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'nice', 'polarity': 'negative', 'category': 'NULL'}]\ntext: plus the screen is matte , so bright lights are n ' t glaring .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplus the screen is matte , so bright lights are n ' t glaring .\n->", + "output": "{\"text\": \"plus the screen is matte , so bright lights are n ' t glaring .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have never before eaten 40 pieces of relatively good nigiri .\n->i have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i ' m sure i ' ll return for a final judgement tho .\n->i ' m sure i ' ll return for a final judgement tho .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\ntext: overall impression : this is a killer laptop for a killer deal !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall impression : this is a killer laptop for a killer deal !\n->", + "output": "{\"text\": \"overall impression : this is a killer laptop for a killer deal !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'killer', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza here is delicious .\n->The pizza here is delicious .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is far and away the best i ' ve ever had .\n->it is far and away the best i ' ve ever had .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n->", + "output": "{\"text\": \"i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'better', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n->i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n[{'aspect': 'device', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my overall impression has not changed in the past 8 months , and i am still very impressed by it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy overall impression has not changed in the past 8 months , and i am still very impressed by it .\n->", + "output": "{\"text\": \"my overall impression has not changed in the past 8 months , and i am still very impressed by it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This restaurant was way overhyped .\n->This restaurant was way overhyped .\n[{'aspect': 'restaurant', 'opinion': 'overhyped', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We love the food , drinks , and atmosphere !\n->We love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n->", + "output": "{\"text\": \"thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my main gripe is incompatibility with amazon prime videos and gogo .\n->my main gripe is incompatibility with amazon prime videos and gogo .\n[{'aspect': 'amazon prime videos and gogo', 'opinion': 'gripe', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n->I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n[{'aspect': 'noodle dishes', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: : p ) , and the machine is definitely zippy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n: p ) , and the machine is definitely zippy .\n->", + "output": "{\"text\": \": p ) , and the machine is definitely zippy .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'zippy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend the Sophia pizza .\n->I highly recommend the Sophia pizza .\n[{'aspect': 'Sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n->i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: oh , and the nvidia card is plenty capable for gaming at medium settings at least ( grid autosport fps avg is in the 80s / 90s or so )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noh , and the nvidia card is plenty capable for gaming at medium settings at least ( grid autosport fps avg is in the 80s / 90s or so )\n->", + "output": "{\"text\": \"oh , and the nvidia card is plenty capable for gaming at medium settings at least ( grid autosport fps avg is in the 80s / 90s or so )\", \"labels\": \"[{'aspect': 'nvidia card', 'opinion': 'capable', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: more importantly i appreciate the uncanny speed to boot up or wake up .\n->more importantly i appreciate the uncanny speed to boot up or wake up .\n[{'aspect': 'speed', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n->The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n[{'aspect': 'crackling calamari salad', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crackling calamari salad', 'opinion': 'lightly dressed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: + great and fast cpu and overall fast pc performance\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n+ great and fast cpu and overall fast pc performance\n->", + "output": "{\"text\": \"+ great and fast cpu and overall fast pc performance\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'great', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'cpu', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'pc performance', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n->Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n->however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: + decent full hd screen ( might be the ' crappiest ' thing on it and it is still good for the price )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n+ decent full hd screen ( might be the ' crappiest ' thing on it and it is still good for the price )\n->", + "output": "{\"text\": \"+ decent full hd screen ( might be the ' crappiest ' thing on it and it is still good for the price )\", \"labels\": \"[{'aspect': 'hd screen', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for me the extra storage , back light keyboard ( you ' ll love it ) , 2 in 1 factor , and great build quality made it a no - brainer .\n->for me the extra storage , back light keyboard ( you ' ll love it ) , 2 in 1 factor , and great build quality made it a no - brainer .\n[{'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#DESIGN_FEATURES'}, {'aspect': 'back light keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: - the touchscreen works great and is very responsive .\n->- the touchscreen works great and is very responsive .\n[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: - trackpad is too finicky and not my favorite\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- trackpad is too finicky and not my favorite\n->", + "output": "{\"text\": \"- trackpad is too finicky and not my favorite\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'finicky', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer seemed very exciting but after having troubles with 3 of them i give up .\n->this computer seemed very exciting but after having troubles with 3 of them i give up .\n[{'aspect': 'computer', 'opinion': 'exciting', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: this issue happens more frequently when using netflix ( app or through chrome browser ) .\n->this issue happens more frequently when using netflix ( app or through chrome browser ) .\n[{'aspect': 'happens', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: need to play with it a bit more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nneed to play with it a bit more .\n->", + "output": "{\"text\": \"need to play with it a bit more .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good food\n->good food\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n[{'aspect': 'staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'stressed', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'unisex bathroom', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: as of now , for $ 600 or less , this is a nice buy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas of now , for $ 600 or less , this is a nice buy .\n->", + "output": "{\"text\": \"as of now , for $ 600 or less , this is a nice buy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no fans and stays reasonably cool unless you are playing a game .\n->no fans and stays reasonably cool unless you are playing a game .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}, {'aspect': 'NULL', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'FANS&COOLING#QUALITY'}]\nExample:\ntext: this item is excellent and not bad for the price .\n->this item is excellent and not bad for the price .\n[{'aspect': 'item', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'item', 'opinion': 'not bad', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: this laptop is the best for the price in my opinion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is the best for the price in my opinion .\n->", + "output": "{\"text\": \"this laptop is the best for the price in my opinion .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the tom kha soup was pathetic .\n->and the tom kha soup was pathetic .\n[{'aspect': 'tom kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food was lousy - too sweet or too salty and the portions tiny .\n->The food was lousy - too sweet or too salty and the portions tiny .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: there are 2 pretty significant flaws in the design however , one i think might only be on my laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere are 2 pretty significant flaws in the design however , one i think might only be on my laptop .\n->", + "output": "{\"text\": \"there are 2 pretty significant flaws in the design however , one i think might only be on my laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cons : webcam doesn ' t have video - only takes pics ; awful , uncomfortable keyboard & trackpad ; chromebook extensions are lacking and don ' t address / make up for the limitations of this chromebook ; a bit heavy and clunky ; hard to figure out google cloud print\n->cons : webcam doesn ' t have video - only takes pics ; awful , uncomfortable keyboard & trackpad ; chromebook extensions are lacking and don ' t address / make up for the limitations of this chromebook ; a bit heavy and clunky ; hard to figure out google cloud print\n[{'aspect': 'webcam', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'clunky', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it is nearly impossible to get a table , so if you ever have the chance to go here for dinner , do not pass it up .\n->it is nearly impossible to get a table , so if you ever have the chance to go here for dinner , do not pass it up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: - ips full hd screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- ips full hd screen .\n->", + "output": "{\"text\": \"- ips full hd screen .\", \"labels\": \"[{'aspect': 'hd screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n->google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n[{'aspect': \"google ' s own services\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: My roommate and I LOVE this place .\n->My roommate and I LOVE this place .\n[{'aspect': 'place', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - pretty loud speakers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- pretty loud speakers .\n->", + "output": "{\"text\": \"- pretty loud speakers .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'loud', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is also attentive and friendly .\n->The staff is also attentive and friendly .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: beautiful resolution\n->beautiful resolution\n[{'aspect': 'resolution', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: best pc bang for this level of buck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest pc bang for this level of buck .\n->", + "output": "{\"text\": \"best pc bang for this level of buck .\", \"labels\": \"[{'aspect': 'pc bang', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there was a small wait , but shorter than i expected .\n->there was a small wait , but shorter than i expected .\n[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great indian food\n->great indian food\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i will update this post if anything changes from what i posted , after i get the correct memory installed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will update this post if anything changes from what i posted , after i get the correct memory installed .\n->", + "output": "{\"text\": \"i will update this post if anything changes from what i posted , after i get the correct memory installed .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service not the friendliest to our ` ` large party ' ' !\n->service not the friendliest to our ` ` large party ' ' !\n[{'aspect': 'service', 'opinion': 'not the friendliest', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great machine out of the box .\n->great machine out of the box .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i have been beyond pleased with this laptop purchase over the last 6 months of use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been beyond pleased with this laptop purchase over the last 6 months of use .\n->", + "output": "{\"text\": \"i have been beyond pleased with this laptop purchase over the last 6 months of use .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have never before eaten 40 pieces of relatively good nigiri .\n->I have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The food and staff always surprise me with the new heights they are taken to .\n->The food and staff always surprise me with the new heights they are taken to .\n[{'aspect': 'food', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i highly recommend this laptop for anyone looking for a great performing machine with an outstanding price ( just to be clear , it won ' t be running the newest high - end games on ultra - high graphics settings , but it still performs phenomenally for its price range and usage category ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend this laptop for anyone looking for a great performing machine with an outstanding price ( just to be clear , it won ' t be running the newest high - end games on ultra - high graphics settings , but it still performs phenomenally for its price range and usage category ) .\n->", + "output": "{\"text\": \"i highly recommend this laptop for anyone looking for a great performing machine with an outstanding price ( just to be clear , it won ' t be running the newest high - end games on ultra - high graphics settings , but it still performs phenomenally for its price range and usage category ) .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'phenomenally', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere was great .\n->the atmosphere was great .\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n->There was a long wait for a table outside , but it was a little too hot in the sun anyway so our insde table was very nice .\n[{'aspect': 'table', 'opinion': 'long wait', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'insde table', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n->", + "output": "{\"text\": \"this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\", \"labels\": \"[{'aspect': 'performance', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'happy', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n->other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n->I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish .\n[{'aspect': 'noodle dishes', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is also amazingly quiet : the ssd doesn ' t make any noise , and the fan only spins up if i am doing something intensive like playing games or running a heafty program - just browsing or watching videos / basic office work will not make this computer more than whisper .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is also amazingly quiet : the ssd doesn ' t make any noise , and the fan only spins up if i am doing something intensive like playing games or running a heafty program - just browsing or watching videos / basic office work will not make this computer more than whisper .\n->", + "output": "{\"text\": \"it is also amazingly quiet : the ssd doesn ' t make any noise , and the fan only spins up if i am doing something intensive like playing games or running a heafty program - just browsing or watching videos / basic office work will not make this computer more than whisper .\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'amazingly', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was slow had to wait to order and get food although not crowded .\n->service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n->the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n[{'aspect': '1080p screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': '1080p screen', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'hinge', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: so again , the battery is surprisingly great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso again , the battery is surprisingly great .\n->", + "output": "{\"text\": \"so again , the battery is surprisingly great .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: initially the product lived up to the expectations .\n->initially the product lived up to the expectations .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: seriously , this place kicks ass .\n->seriously , this place kicks ass .\n[{'aspect': 'place', 'opinion': 'kicks ass', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n->", + "output": "{\"text\": \"the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\", \"labels\": \"[{'aspect': 'number pad', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n->this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n[{'aspect': 'product', 'opinion': 'not an inexpensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: good screen definition .\n->good screen definition .\n[{'aspect': 'screen definition', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: additionally , this has a nice backlight to the keyboard , which will turn off when nothing has been pressed for about 20 seconds ( great for when just watching or reading something ) or can be manually turned off entirely .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nadditionally , this has a nice backlight to the keyboard , which will turn off when nothing has been pressed for about 20 seconds ( great for when just watching or reading something ) or can be manually turned off entirely .\n->", + "output": "{\"text\": \"additionally , this has a nice backlight to the keyboard , which will turn off when nothing has been pressed for about 20 seconds ( great for when just watching or reading something ) or can be manually turned off entirely .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their eggplant is so delicate , sweet tender !\n->Their eggplant is so delicate , sweet tender !\n[{'aspect': 'eggplant', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'eggplant', 'opinion': 'sweet tender', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: that is awesome .\n->that is awesome .\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\ntext: in addition , you are gifted with an sd card reader and an optical drive which can read and write cds and dvds ( which i can ' t say i ' ve used other than testing that it worked , but i do remember how hazardous it is to suddenly need something like that and not have it available ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin addition , you are gifted with an sd card reader and an optical drive which can read and write cds and dvds ( which i can ' t say i ' ve used other than testing that it worked , but i do remember how hazardous it is to suddenly need something like that and not have it available ) .\n->", + "output": "{\"text\": \"in addition , you are gifted with an sd card reader and an optical drive which can read and write cds and dvds ( which i can ' t say i ' ve used other than testing that it worked , but i do remember how hazardous it is to suddenly need something like that and not have it available ) .\", \"labels\": \"[{'aspect': 'optical drive', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There is no excuse for such lousy service !\n->There is no excuse for such lousy service !\n[{'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n->the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n[{'aspect': 'keyboard', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: - though the case is plastic , the keyboard area itself has a cold metallic feel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- though the case is plastic , the keyboard area itself has a cold metallic feel .\n->", + "output": "{\"text\": \"- though the case is plastic , the keyboard area itself has a cold metallic feel .\", \"labels\": \"[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard area', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we will definitely go back .\n->we will definitely go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n->Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n[{'aspect': 'waitstaff', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - the battery life is at least 8 hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the battery life is at least 8 hours .\n->", + "output": "{\"text\": \"- the battery life is at least 8 hours .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n->The table service could have been a little more attentive but as someone who also works in the service industry , I understood they were busy .\n[{'aspect': 'service', 'opinion': 'busy', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i purchased this asus chromebook in may of 2018 and initially loved it .\n->i purchased this asus chromebook in may of 2018 and initially loved it .\n[{'aspect': 'asus chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: - the keys , often described as mushy , still have a decent click and distance to them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the keys , often described as mushy , still have a decent click and distance to them .\n->", + "output": "{\"text\": \"- the keys , often described as mushy , still have a decent click and distance to them .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and because of the constant usage of higher brightness , the battery does drain faster .\n->and because of the constant usage of higher brightness , the battery does drain faster .\n[{'aspect': 'battery', 'opinion': 'faster', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is a painfully slow computer .\n->this is a painfully slow computer .\n[{'aspect': 'computer', 'opinion': 'painfully', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: - the backlit keyboard looks nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the backlit keyboard looks nice .\n->", + "output": "{\"text\": \"- the backlit keyboard looks nice .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend the restaurant based on our experience last night .\n->i highly recommend the restaurant based on our experience last night .\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n->I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n[{'aspect': 'Edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n->", + "output": "{\"text\": \"- i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n->i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n[{'aspect': 'size', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n->they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: - the webcam sucks but i don ' t care about that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the webcam sucks but i don ' t care about that .\n->", + "output": "{\"text\": \"- the webcam sucks but i don ' t care about that .\", \"labels\": \"[{'aspect': 'webcam', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the pizza is way to expensive .\n->but the pizza is way to expensive .\n[{'aspect': 'pizza', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: manufactures need to quality check their products before sending them out .\n->manufactures need to quality check their products before sending them out .\n[{'aspect': 'manufactures', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\ntext: - easily upgradeable hdd ( it comes with a 512 ssd , which is fine for now , but is easily changed out )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- easily upgradeable hdd ( it comes with a 512 ssd , which is fine for now , but is easily changed out )\n->", + "output": "{\"text\": \"- easily upgradeable hdd ( it comes with a 512 ssd , which is fine for now , but is easily changed out )\", \"labels\": \"[{'aspect': 'hdd', 'opinion': 'upgradeable', 'polarity': 'positive', 'category': 'HARDWARE#USABILITY'}, {'aspect': '512 ssd', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i double checked the outside labels and upon opening the box saw that the laptop had several deep scratches on the bottom of it .\n->i double checked the outside labels and upon opening the box saw that the laptop had several deep scratches on the bottom of it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: my main complaint is the battery life , i see many positive reviews about the battery life .\n->my main complaint is the battery life , i see many positive reviews about the battery life .\n[{'aspect': 'battery life', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: - windows 10 ( do i really need to list the drawbacks of 10 ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- windows 10 ( do i really need to list the drawbacks of 10 ?\n->", + "output": "{\"text\": \"- windows 10 ( do i really need to list the drawbacks of 10 ?\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n->even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'working unit', 'opinion': 'outweighs', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i was there on sat . for my birthday and we had an excellent time .\n->i was there on sat . for my birthday and we had an excellent time .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the e15 has a bright , 1080p screen - text is extremely sharp .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe e15 has a bright , 1080p screen - text is extremely sharp .\n->", + "output": "{\"text\": \"the e15 has a bright , 1080p screen - text is extremely sharp .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff is no nonsense .\n->the staff is no nonsense .\n[{'aspect': 'staff', 'opinion': 'no nonsense', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i have had to wipe this pc about 8 times in the short 2 months i have owned it .\n->i have had to wipe this pc about 8 times in the short 2 months i have owned it .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: i also really like the finish on the case .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni also really like the finish on the case .\n->", + "output": "{\"text\": \"i also really like the finish on the case .\", \"labels\": \"[{'aspect': 'case', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n->this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: there is something about their atmosphere that makes me come back nearly every week .\n->there is something about their atmosphere that makes me come back nearly every week .\n[{'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i wish the power cord were longer , but that ' s minor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wish the power cord were longer , but that ' s minor .\n->", + "output": "{\"text\": \"i wish the power cord were longer , but that ' s minor .\", \"labels\": \"[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazing laptop !\n->amazing laptop !\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: very cozy and warm inside . . . . .\n->very cozy and warm inside . . . . .\n[{'aspect': 'NULL', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: all in all , i ' m really glad i got this machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all , i ' m really glad i got this machine .\n->", + "output": "{\"text\": \"all in all , i ' m really glad i got this machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery is all day amazing\n->battery is all day amazing\n[{'aspect': 'battery', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: the keyboard is okay .\n->the keyboard is okay .\n[{'aspect': 'keyboard', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\ntext: it has served me very well ever since .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has served me very well ever since .\n->", + "output": "{\"text\": \"it has served me very well ever since .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is small , a little weird and takes some getting used to .\n->the keyboard is small , a little weird and takes some getting used to .\n[{'aspect': 'keyboard', 'opinion': 'small', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'weird', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: my dad says it works extremely well !\n->my dad says it works extremely well !\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: so kudos to acer for the keyboard !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso kudos to acer for the keyboard !\n->", + "output": "{\"text\": \"so kudos to acer for the keyboard !\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'acer', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a great place to order from or sit - in .\n->it ' s a great place to order from or sit - in .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n->so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: this laptop has an amazing price for the hardware it offers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop has an amazing price for the hardware it offers .\n->", + "output": "{\"text\": \"this laptop has an amazing price for the hardware it offers .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'hardware', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'HARDWARE#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: which lets face it . . . . at times it ' s a good thing .\n->which lets face it . . . . at times it ' s a good thing .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: but the service was a bit slow .\n->but the service was a bit slow .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\ntext: multiple ports for anything you need .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmultiple ports for anything you need .\n->", + "output": "{\"text\": \"multiple ports for anything you need .\", \"labels\": \"[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is freaking painful how long it can take games to load with that hard drive .\n->it is freaking painful how long it can take games to load with that hard drive .\n[{'aspect': 'hard drive', 'opinion': 'painful', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n->my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n[{'aspect': 'bagel with lox spread', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagles', 'opinion': 'unbeliavably good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: decent sized track pad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndecent sized track pad .\n->", + "output": "{\"text\": \"decent sized track pad .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'decent', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: touchpad is nice and responsive .\n->touchpad is nice and responsive .\n[{'aspect': 'touchpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this place is worth an one - hour drive .\n->this place is worth an one - hour drive .\n[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: screen is ips .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen is ips .\n->", + "output": "{\"text\": \"screen is ips .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sound and screen quality is low .\n->the sound and screen quality is low .\n[{'aspect': 'sound', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'screen quality', 'opinion': 'low', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: i just find the battery draining to quickly in my opinion .\n->i just find the battery draining to quickly in my opinion .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i figured out the day i got this laptop why the company was able to keep the price of this laptop even with the great hardware inside .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni figured out the day i got this laptop why the company was able to keep the price of this laptop even with the great hardware inside .\n->", + "output": "{\"text\": \"i figured out the day i got this laptop why the company was able to keep the price of this laptop even with the great hardware inside .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: in fact , it appears he is going to go postal at any moment .\n->in fact , it appears he is going to go postal at any moment .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it cost 8 dollars and shipping is not cheap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit cost 8 dollars and shipping is not cheap .\n->", + "output": "{\"text\": \"it cost 8 dollars and shipping is not cheap .\", \"labels\": \"[{'aspect': 'shipping', 'opinion': 'not cheap', 'polarity': 'negative', 'category': 'SHIPPING#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great price - i always buy the warranty .\n->great price - i always buy the warranty .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'warranty', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}]\nExample:\ntext: the touchscreen and the hotkeys along the top take some getting used to but i was quickly in the google play store , downloading all the apps i have on my phone and linking them to my accounts .\n->the touchscreen and the hotkeys along the top take some getting used to but i was quickly in the google play store , downloading all the apps i have on my phone and linking them to my accounts .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'hotkeys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\ntext: taking off the entire back of the laptop is very difficult .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntaking off the entire back of the laptop is very difficult .\n->", + "output": "{\"text\": \"taking off the entire back of the laptop is very difficult .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !\n->they are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !\n[{'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'drinks', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}, {'aspect': 'pita bread', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i ' m very disappointed with my purchase\n->i ' m very disappointed with my purchase\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: overall the laptop is great for its price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall the laptop is great for its price .\n->", + "output": "{\"text\": \"overall the laptop is great for its price .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portions are large and the servers always surprise us with a different starter .\n->The portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Frites were delicious if a bit on the thick side .\n->Frites were delicious if a bit on the thick side .\n[{'aspect': 'Frites', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i should also mention that i got this laptop when it was 100 dollars off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni should also mention that i got this laptop when it was 100 dollars off .\n->", + "output": "{\"text\": \"i should also mention that i got this laptop when it was 100 dollars off .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the perfect spot for meeting friends , having lunch , dinner , pre - theatre or after - theatre drinks !\n->this is the perfect spot for meeting friends , having lunch , dinner , pre - theatre or after - theatre drinks !\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n->For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n[{'aspect': 'lobby area', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\ntext: great laptop for expansion and upgrade .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat laptop for expansion and upgrade .\n->", + "output": "{\"text\": \"great laptop for expansion and upgrade .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza was great .\n->The pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i used to use a ridiculously heavy d _ _ _ inspirion , which i love because of the 17 ` ` screen and capabilities , but i quickly found that carrying it anywhere caused my shoulders and back to hurt !\n->i used to use a ridiculously heavy d _ _ _ inspirion , which i love because of the 17 ` ` screen and capabilities , but i quickly found that carrying it anywhere caused my shoulders and back to hurt !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: all in all it is a great machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all it is a great machine .\n->", + "output": "{\"text\": \"all in all it is a great machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n->i was n ' t here for the pizza so i ca n ' t comment on that yet but what i had was very good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: not sure if this is simply a deal accident , but my ssd failed within 4 months .\n->not sure if this is simply a deal accident , but my ssd failed within 4 months .\n[{'aspect': 'ssd', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: best laptop i ' ve ever owned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbest laptop i ' ve ever owned .\n->", + "output": "{\"text\": \"best laptop i ' ve ever owned .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: such a disappointment . . .\n->such a disappointment . . .\n[{'aspect': 'NULL', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great friendly service , fast seating , fast delivery , excellent sushi .\n->great friendly service , fast seating , fast delivery , excellent sushi .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'seating', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this computer lives up to its expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer lives up to its expectations .\n->", + "output": "{\"text\": \"this computer lives up to its expectations .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if i have a video call going on facebook messenger , chrome will take three or four seconds to respond .\n->if i have a video call going on facebook messenger , chrome will take three or four seconds to respond .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: runs ok , cheapish , good for price i guess\n->runs ok , cheapish , good for price i guess\n[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'cheapish', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the sound quality is ok , and even at full volume isn ' t that loud , but that ' s not a big deal for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sound quality is ok , and even at full volume isn ' t that loud , but that ' s not a big deal for me .\n->", + "output": "{\"text\": \"the sound quality is ok , and even at full volume isn ' t that loud , but that ' s not a big deal for me .\", \"labels\": \"[{'aspect': 'sound quality', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'sound quality', 'opinion': \"' t that loud\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I particularly love their yellowfun tuna and their mussel selection .\n->I particularly love their yellowfun tuna and their mussel selection .\n[{'aspect': 'yellowfun tuna', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussel selection', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Turned out there was full service upstairs and sat down .\n->Turned out there was full service upstairs and sat down .\n[{'aspect': 'service', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}]\ntext: from the moment i opened it , i was thoroughly pleased .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfrom the moment i opened it , i was thoroughly pleased .\n->", + "output": "{\"text\": \"from the moment i opened it , i was thoroughly pleased .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n->i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n[{'aspect': 'computer', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n->the wait here is long for dim sum , but if you do n ' t like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for chinatown ) alternative .\n[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: it ' s lightning fast and handles games like skyrim and the witcher 3 surprisingly smoothly for the price i paid .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s lightning fast and handles games like skyrim and the witcher 3 surprisingly smoothly for the price i paid .\n->", + "output": "{\"text\": \"it ' s lightning fast and handles games like skyrim and the witcher 3 surprisingly smoothly for the price i paid .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Definitely not worth the price !\n->Definitely not worth the price !\n[{'aspect': 'price', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the start up process was very simple and relatively quick .\n->the start up process was very simple and relatively quick .\n[{'aspect': 'start up', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: superb value for money and powerful performance from this quad core computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuperb value for money and powerful performance from this quad core computer .\n->", + "output": "{\"text\": \"superb value for money and powerful performance from this quad core computer .\", \"labels\": \"[{'aspect': 'quad core computer', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'quad core computer', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the rma process needs improvement - buyer must pay to return the product for repair .\n->- the rma process needs improvement - buyer must pay to return the product for repair .\n[{'aspect': 'rma process', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: acer makes a good unit too .\n->acer makes a good unit too .\n[{'aspect': 'acer', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: it has and does everything it should .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has and does everything it should .\n->", + "output": "{\"text\": \"it has and does everything it should .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Because of the delicate thin crust , take-out pies get soggy in their boxes .\n->Because of the delicate thin crust , take-out pies get soggy in their boxes .\n[{'aspect': 'take-out pies', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'delicate', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: well , this place is so ghetto its not even funny .\n->well , this place is so ghetto its not even funny .\n[{'aspect': 'place', 'opinion': 'ghetto', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'not even funny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: screen is bright , doesn ' t feel heavy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen is bright , doesn ' t feel heavy .\n->", + "output": "{\"text\": \"screen is bright , doesn ' t feel heavy .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': \"' t feel heavy\", 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n->it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'screen', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: When he finally did , he was unable to make a gin and tonic -- could n't find tonic .\n->When he finally did , he was unable to make a gin and tonic -- could n't find tonic .\n[{'aspect': 'gin and tonic', 'opinion': 'unable', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i ' m not a power user , so it is perfect for my needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m not a power user , so it is perfect for my needs .\n->", + "output": "{\"text\": \"i ' m not a power user , so it is perfect for my needs .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very happy with this laptop .\n->i am very happy with this laptop .\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i bought this originally a few months back , died within a week .\n->i bought this originally a few months back , died within a week .\n[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: keys type nicely .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeys type nicely .\n->", + "output": "{\"text\": \"keys type nicely .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is authentic italian - delicious !\n->the food is authentic italian - delicious !\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: speaking of charges , it ' s so nice to be able to use usb c .\n->speaking of charges , it ' s so nice to be able to use usb c .\n[{'aspect': 'usb c', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\ntext: only wish the power button was somewhere else , its too easy to hit accidentally .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly wish the power button was somewhere else , its too easy to hit accidentally .\n->", + "output": "{\"text\": \"only wish the power button was somewhere else , its too easy to hit accidentally .\", \"labels\": \"[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t even think i will be able to work on this it is so distracting .\n->i don ' t even think i will be able to work on this it is so distracting .\n[{'aspect': 'NULL', 'opinion': 'distracting', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: in the summer months , the back garden area is really nice .\n->in the summer months , the back garden area is really nice .\n[{'aspect': 'back garden area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: this laptop has handled everything i have thrown at it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop has handled everything i have thrown at it .\n->", + "output": "{\"text\": \"this laptop has handled everything i have thrown at it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little place has a cute interior decor and affordable city prices .\n->this little place has a cute interior decor and affordable city prices .\n[{'aspect': 'interior decor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'little', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: The entertainment was great they have shows that go on through out the dinner .\n->The entertainment was great they have shows that go on through out the dinner .\n[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen looks good despite some other reviews .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen looks good despite some other reviews .\n->", + "output": "{\"text\": \"the screen looks good despite some other reviews .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for reference : ive had this laptop for about 4 months now for my first year at college .\n->for reference : ive had this laptop for about 4 months now for my first year at college .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n->a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: my cpu also runs over 3ghz most of the time with no heating issues either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy cpu also runs over 3ghz most of the time with no heating issues either .\n->", + "output": "{\"text\": \"my cpu also runs over 3ghz most of the time with no heating issues either .\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the macbook was delivered soon and it is exactly as described\n->the macbook was delivered soon and it is exactly as described\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: acer makes a good unit too .\n->acer makes a good unit too .\n[{'aspect': 'acer', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: after running good for the initial 25 first days it won ' t power on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter running good for the initial 25 first days it won ' t power on .\n->", + "output": "{\"text\": \"after running good for the initial 25 first days it won ' t power on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The quantity is also very good , you will come out satisfied .\n->The quantity is also very good , you will come out satisfied .\n[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We love the food , drinks , and atmosphere !\n->We love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i still find the one piece touch pads unreliable to use even after all the tweaking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni still find the one piece touch pads unreliable to use even after all the tweaking .\n->", + "output": "{\"text\": \"i still find the one piece touch pads unreliable to use even after all the tweaking .\", \"labels\": \"[{'aspect': 'touch pads', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i used this laptop for 2 months without upgrading it .\n->i used this laptop for 2 months without upgrading it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: so i decide to report back to the waitress because it was completely inedible .\n->so i decide to report back to the waitress because it was completely inedible .\n[{'aspect': 'NULL', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: my first time with a solid state drive , very nice quick and quiet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy first time with a solid state drive , very nice quick and quiet .\n->", + "output": "{\"text\": \"my first time with a solid state drive , very nice quick and quiet .\", \"labels\": \"[{'aspect': 'solid state drive', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'solid state drive', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in an area sadly lacking in decent thai food , this is one of the best spots .\n->in an area sadly lacking in decent thai food , this is one of the best spots .\n[{'aspect': 'thai food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n->the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i will painfully learn the new pads .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will painfully learn the new pads .\n->", + "output": "{\"text\": \"i will painfully learn the new pads .\", \"labels\": \"[{'aspect': 'pads', 'opinion': 'painfully', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n->the four seasons has history and it is a sort of landmark of new york city restaurants , but trust me , they will charge you through the nose just so that you can say ` ` i ' ve been to the four seasons restaurant ' ' .\n[{'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'the four seasons', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: We had the pot-stickers which were great and a tempura dish that was great .\n->We had the pot-stickers which were great and a tempura dish that was great .\n[{'aspect': 'pot-stickers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tempura dish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is a light - use business laptop that we ' ve had for a month .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a light - use business laptop that we ' ve had for a month .\n->", + "output": "{\"text\": \"this is a light - use business laptop that we ' ve had for a month .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'light - use', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: salads are a delicious way to begin the meal .\n->salads are a delicious way to begin the meal .\n[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: upon further inspection , i had noticed that one of the side speakers was pushed in and the plastic surrounding it had a crack .\n->upon further inspection , i had noticed that one of the side speakers was pushed in and the plastic surrounding it had a crack .\n[{'aspect': 'side speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\ntext: the screen is more than sufficient for non - gaming , business use , and it seems as fast as expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is more than sufficient for non - gaming , business use , and it seems as fast as expected .\n->", + "output": "{\"text\": \"the screen is more than sufficient for non - gaming , business use , and it seems as fast as expected .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'sufficient', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , it 's the service that leaves a bad taste in my mouth .\n->however , it 's the service that leaves a bad taste in my mouth .\n[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: : p ) , and the machine is definitely zippy .\n->: p ) , and the machine is definitely zippy .\n[{'aspect': 'machine', 'opinion': 'zippy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n->", + "output": "{\"text\": \"i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\", \"labels\": \"[{'aspect': 'acer monitors', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: even using it a bit makes my hands / wrist uncomfortable .\n->even using it a bit makes my hands / wrist uncomfortable .\n[{'aspect': 'NULL', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: anyways , i am a bit sad that the ssd is a bit hidden and with very low memory , especially since i launched it and had to deal with half of it being full of bloatware ( btw , anyone who is looking for playing games on it , remember that the ssd will be faster and better at handling them , not the hard drive , all your programs and games should be installed to the ssd , all your files to the hard drive for those who don ' t know ) of course , with a little luck , that is fixable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nanyways , i am a bit sad that the ssd is a bit hidden and with very low memory , especially since i launched it and had to deal with half of it being full of bloatware ( btw , anyone who is looking for playing games on it , remember that the ssd will be faster and better at handling them , not the hard drive , all your programs and games should be installed to the ssd , all your files to the hard drive for those who don ' t know ) of course , with a little luck , that is fixable .\n->", + "output": "{\"text\": \"anyways , i am a bit sad that the ssd is a bit hidden and with very low memory , especially since i launched it and had to deal with half of it being full of bloatware ( btw , anyone who is looking for playing games on it , remember that the ssd will be faster and better at handling them , not the hard drive , all your programs and games should be installed to the ssd , all your files to the hard drive for those who don ' t know ) of course , with a little luck , that is fixable .\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'luck', 'polarity': 'positive', 'category': 'HARD_DISC#DESIGN_FEATURES'}, {'aspect': 'ssd', 'opinion': 'fixable', 'polarity': 'positive', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n->they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n[{'aspect': 'discount', 'opinion': \"was n't enough\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it ' s a good enough laptop .\n->it ' s a good enough laptop .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n->", + "output": "{\"text\": \"the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard response', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n->The restaurant is dark and not very attractive and then you have spot lights shining on you putting you in the worst light possible , reaching for sunglasses .\n[{'aspect': 'spot lights', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: do not buy it will not search what you type in i want my money back\n->do not buy it will not search what you type in i want my money back\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the battery life is not quite as long as advertised , but i can get through daily life just fine without lugging my charger everywhere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is not quite as long as advertised , but i can get through daily life just fine without lugging my charger everywhere .\n->", + "output": "{\"text\": \"the battery life is not quite as long as advertised , but i can get through daily life just fine without lugging my charger everywhere .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n->Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n[{'aspect': 'Wait staff', 'opinion': 'unappreciative', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n->considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n[{'aspect': 'waitstaff', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waitstaff', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: to get the features like this good luck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto get the features like this good luck .\n->", + "output": "{\"text\": \"to get the features like this good luck .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i opened the box after ordering on black friday , and the machine wouldn ' t charge .\n->i opened the box after ordering on black friday , and the machine wouldn ' t charge .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: my girlfriend works from home with it and has no problems at all to do online classes with it .\n->my girlfriend works from home with it and has no problems at all to do online classes with it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n->", + "output": "{\"text\": \"its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as far as gaming performance , the m370x does quite well .\n->as far as gaming performance , the m370x does quite well .\n[{'aspect': 'm370x', 'opinion': 'well', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keyboard and os takes some getting used to .\n->the keyboard and os takes some getting used to .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\ntext: people said it wasn ' t bright enough but i run at 50 to 75 percent and its white bright .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npeople said it wasn ' t bright enough but i run at 50 to 75 percent and its white bright .\n->", + "output": "{\"text\": \"people said it wasn ' t bright enough but i run at 50 to 75 percent and its white bright .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ' what a bunch of whiners , ' i concluded about all the people who slammed the keyboard .\n->' what a bunch of whiners , ' i concluded about all the people who slammed the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i had this laptop for a little over a year and it worked well at first .\n->i had this laptop for a little over a year and it worked well at first .\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: for the price , it ' s a solid laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the price , it ' s a solid laptop .\n->", + "output": "{\"text\": \"for the price , it ' s a solid laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our experience did not ever get any better .\n->our experience did not ever get any better .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: touch - screen features are responsive .\n->touch - screen features are responsive .\n[{'aspect': 'touch - screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the keyboard is easy to type on ( and the backlight is an added plus ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is easy to type on ( and the backlight is an added plus ) .\n->", + "output": "{\"text\": \"the keyboard is easy to type on ( and the backlight is an added plus ) .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'backlight', 'opinion': 'plus', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n->we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n[{'aspect': 'voss bottles of water', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: my friend got the mushroom pizza which tasted better .\n->my friend got the mushroom pizza which tasted better .\n[{'aspect': 'mushroom pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: you ' re not gon na find a deal like this too often .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou ' re not gon na find a deal like this too often .\n->", + "output": "{\"text\": \"you ' re not gon na find a deal like this too often .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m going back .\n->i ' m going back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: in summer - eat outside on a terrace ( another great feature of suan ) ! ! !\n->in summer - eat outside on a terrace ( another great feature of suan ) ! ! !\n[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: it ' s not perfect , but for casual users it ' s a steal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s not perfect , but for casual users it ' s a steal .\n->", + "output": "{\"text\": \"it ' s not perfect , but for casual users it ' s a steal .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after running through the setup wizard , the laptop failed to boot .\n->after running through the setup wizard , the laptop failed to boot .\n[{'aspect': 'laptop', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n->We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n[{'aspect': 'lox', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love this laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this laptop !\n->", + "output": "{\"text\": \"i love this laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - clean and secure operating system that is very lean and gets the most out of the systems modest specs\n->- clean and secure operating system that is very lean and gets the most out of the systems modest specs\n[{'aspect': 'operating system', 'opinion': 'clean', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'operating system', 'opinion': 'secure', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'operating system', 'opinion': 'clean', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'specs', 'opinion': 'modest', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\nExample:\ntext: stepped on my foot on the second time he reached over me to adjust lighting .\n->stepped on my foot on the second time he reached over me to adjust lighting .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i have the intel core i7 , soooo fast !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have the intel core i7 , soooo fast !\n->", + "output": "{\"text\": \"i have the intel core i7 , soooo fast !\", \"labels\": \"[{'aspect': 'intel core i7', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For some reason , all the seafood on the menu was unavailable except for the Salmon .\n->For some reason , all the seafood on the menu was unavailable except for the Salmon .\n[{'aspect': 'seafood', 'opinion': 'unavailable', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'unavailable', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Salmon', 'opinion': 'except', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: really happy with this laptop !\n->really happy with this laptop !\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: comfortable keyboard !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomfortable keyboard !\n->", + "output": "{\"text\": \"comfortable keyboard !\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great and authentic .\n->The food is great and authentic .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: relatively small screen with high resolution makes reading the screen difficult .\n->relatively small screen with high resolution makes reading the screen difficult .\n[{'aspect': 'screen', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'DISPLAY#USABILITY'}]\ntext: love the cortana !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the cortana !\n->", + "output": "{\"text\": \"love the cortana !\", \"labels\": \"[{'aspect': 'cortana', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: performance - wise , i can easily have 12 - 24 tabs open simultaneously and see no slow - down in performance .\n->performance - wise , i can easily have 12 - 24 tabs open simultaneously and see no slow - down in performance .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: update : i have had this computer for about 3 months now , and it is full of problems .\n->update : i have had this computer for about 3 months now , and it is full of problems .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: first time user for windows 10 and it ' s pretty good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst time user for windows 10 and it ' s pretty good .\n->", + "output": "{\"text\": \"first time user for windows 10 and it ' s pretty good .\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'good', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We were seated and ignored by waitstaff .\n->We were seated and ignored by waitstaff .\n[{'aspect': 'waitstaff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: not as much i can do on it , but it is adequate .\n->not as much i can do on it , but it is adequate .\n[{'aspect': 'NULL', 'opinion': 'adequate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: was very easy to add memory .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwas very easy to add memory .\n->", + "output": "{\"text\": \"was very easy to add memory .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was easy to set up .\n->it was easy to set up .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: it would not charge at all !\n->it would not charge at all !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the acer is similar but bigger and heavier .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe acer is similar but bigger and heavier .\n->", + "output": "{\"text\": \"the acer is similar but bigger and heavier .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'bigger', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'acer', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Authentic Pakistani food .\n->Authentic Pakistani food .\n[{'aspect': 'Pakistani food', 'opinion': 'Authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This place has good potential , but needs a significant amount of work before we can justify spending that much money on indian food you can get everywhere else .\n->This place has good potential , but needs a significant amount of work before we can justify spending that much money on indian food you can get everywhere else .\n[{'aspect': 'money', 'opinion': 'much', 'polarity': 'negative', 'category': 'NULL'}]\ntext: hope this review can save others from the initial hassle i endured because the chromebook 3 looks terrific in any other way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhope this review can save others from the initial hassle i endured because the chromebook 3 looks terrific in any other way .\n->", + "output": "{\"text\": \"hope this review can save others from the initial hassle i endured because the chromebook 3 looks terrific in any other way .\", \"labels\": \"[{'aspect': 'chromebook 3', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: msi should be ashamed at putting out products they know have issues with no intent of correcting the problem during the building and testing phase .\n->msi should be ashamed at putting out products they know have issues with no intent of correcting the problem during the building and testing phase .\n[{'aspect': 'msi', 'opinion': 'ashamed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'products', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n->also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n[{'aspect': 'place', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i bought the 4gb model which will hopefully last me a few years but it ' s nice and snappy now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought the 4gb model which will hopefully last me a few years but it ' s nice and snappy now .\n->", + "output": "{\"text\": \"i bought the 4gb model which will hopefully last me a few years but it ' s nice and snappy now .\", \"labels\": \"[{'aspect': '4gb model', 'opinion': 'hopefully', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': '4gb model', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': '4gb model', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the microsd slot leaves an inserted card perfectly flush .\n->the microsd slot leaves an inserted card perfectly flush .\n[{'aspect': 'microsd slot', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: Nothing fancy but really good food with pretty reasonable price .\n->Nothing fancy but really good food with pretty reasonable price .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the 16gb of ssd storage is perfect as i use it for school and the biggest file i might be storing would be a picture .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 16gb of ssd storage is perfect as i use it for school and the biggest file i might be storing would be a picture .\n->", + "output": "{\"text\": \"the 16gb of ssd storage is perfect as i use it for school and the biggest file i might be storing would be a picture .\", \"labels\": \"[{'aspect': 'ssd storage', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n->the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: the pizza was great .\n->the pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it integrates perfectly with my google account !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit integrates perfectly with my google account !\n->", + "output": "{\"text\": \"it integrates perfectly with my google account !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fast boot up ( 3 seconds )\n->- fast boot up ( 3 seconds )\n[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The characters really make for an enjoyable experience .\n->The characters really make for an enjoyable experience .\n[{'aspect': 'characters', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s lightweight , has a long battery life and although it ' s smaller than a standard laptop , the keyboard is easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s lightweight , has a long battery life and although it ' s smaller than a standard laptop , the keyboard is easy to use .\n->", + "output": "{\"text\": \"it ' s lightweight , has a long battery life and although it ' s smaller than a standard laptop , the keyboard is easy to use .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: let me tell you , this thing is snappy .\n->let me tell you , this thing is snappy .\n[{'aspect': 'NULL', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: but this asus c302ca has blown me away .\n->but this asus c302ca has blown me away .\n[{'aspect': 'asus c302ca', 'opinion': 'blown me away', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the only thing i ' m bummed about is the lack of google play .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing i ' m bummed about is the lack of google play .\n->", + "output": "{\"text\": \"the only thing i ' m bummed about is the lack of google play .\", \"labels\": \"[{'aspect': 'google play', 'opinion': 'lack', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n->I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'surrounding', 'opinion': 'heart warming', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: quick startup and has a nice display which is matte .\n->quick startup and has a nice display which is matte .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: i ' ve gotten more use out of this thing than i first envisioned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve gotten more use out of this thing than i first envisioned .\n->", + "output": "{\"text\": \"i ' ve gotten more use out of this thing than i first envisioned .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: feels nice and looks good but it truly is the worst chromebook on the market !\n->feels nice and looks good but it truly is the worst chromebook on the market !\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food is outstanding and the service is quick , friendly and very professional .\n->the food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i added an sd card which has expanded on the 16gb of storage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni added an sd card which has expanded on the 16gb of storage .\n->", + "output": "{\"text\": \"i added an sd card which has expanded on the 16gb of storage .\", \"labels\": \"[{'aspect': 'sd card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very nice i love it it work very well even i instal gta 5 it run but not enoght video card mb but run at all\n->very nice i love it it work very well even i instal gta 5 it run but not enoght video card mb but run at all\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'video card', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n->the hot dogs are top notch , and they ' re slamwich is amazing !\n[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i have been using a chromebook now for three years and am totally satisfied .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been using a chromebook now for three years and am totally satisfied .\n->", + "output": "{\"text\": \"i have been using a chromebook now for three years and am totally satisfied .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it looks awful , feels awful .\n->it looks awful , feels awful .\n[{'aspect': 'NULL', 'opinion': 'awful', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: had it for a week now and still finding things it can not do .\n->had it for a week now and still finding things it can not do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: for the price you can ' t beat a chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the price you can ' t beat a chromebook .\n->", + "output": "{\"text\": \"for the price you can ' t beat a chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery is far below what i expected .\n->the battery is far below what i expected .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: I did not try the caviar but I tried their salmon and crab salad ( they are all good )\n->I did not try the caviar but I tried their salmon and crab salad ( they are all good )\n[{'aspect': 'salmon', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab salad', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: chromebooks are light weight and start up immediately and are very easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchromebooks are light weight and start up immediately and are very easy to use .\n->", + "output": "{\"text\": \"chromebooks are light weight and start up immediately and are very easy to use .\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebooks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best Italian food I ever had ( and being Italian , that means alot ) .\n->Best Italian food I ever had ( and being Italian , that means alot ) .\n[{'aspect': 'Italian food', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I would highly recommand requesting a table by the window .\n->I would highly recommand requesting a table by the window .\n[{'aspect': 'table by the window', 'opinion': 'recommand', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my old chromebook is an acer and i ' ve had absolutely no problems with it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy old chromebook is an acer and i ' ve had absolutely no problems with it .\n->", + "output": "{\"text\": \"my old chromebook is an acer and i ' ve had absolutely no problems with it .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n->sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n[{'aspect': 'keyboard', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'android app support', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: Service is average .\n->Service is average .\n[{'aspect': 'Service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: this samsung works as expected and is a good , basic chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis samsung works as expected and is a good , basic chromebook .\n->", + "output": "{\"text\": \"this samsung works as expected and is a good , basic chromebook .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n->maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n->extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n[{'aspect': 'seller', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: i love this chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this chromebook !\n->", + "output": "{\"text\": \"i love this chromebook !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Zero ambiance to boot .\n->Zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'Zero', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: very let down by the reliability of this machine .\n->very let down by the reliability of this machine .\n[{'aspect': 'machine', 'opinion': 'let down', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: the keyboard is easy to use , and there is no external noise to contend with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is easy to use , and there is no external noise to contend with .\n->", + "output": "{\"text\": \"the keyboard is easy to use , and there is no external noise to contend with .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: priced at upper intermediate range .\n->priced at upper intermediate range .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: Lived in Shanghai most of my life and thought the food was comparable to the flagship Green Bo restaurant there .\n->Lived in Shanghai most of my life and thought the food was comparable to the flagship Green Bo restaurant there .\n[{'aspect': 'food', 'opinion': 'comparable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is fantastic for the things that i need a computer for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is fantastic for the things that i need a computer for .\n->", + "output": "{\"text\": \"it is fantastic for the things that i need a computer for .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it takes up more than 1 / 3rd of the palm - area horizontal space and makes thumb - tapping mouse tweaks very easy .\n->it takes up more than 1 / 3rd of the palm - area horizontal space and makes thumb - tapping mouse tweaks very easy .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: super off balance with respect to screen .\n->super off balance with respect to screen .\n[{'aspect': 'screen', 'opinion': 'off balance', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\ntext: i highly recommend the samsung chromebook for browsing the internet , and for note taking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend the samsung chromebook for browsing the internet , and for note taking .\n->", + "output": "{\"text\": \"i highly recommend the samsung chromebook for browsing the internet , and for note taking .\", \"labels\": \"[{'aspect': 'samsung chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s one of the worst laptops i ' ve ever had .\n->it ' s one of the worst laptops i ' ve ever had .\n[{'aspect': 'laptops', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great chromebook .\n->great chromebook .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it ' s lightweight and fits easily in a tote or backpack .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s lightweight and fits easily in a tote or backpack .\n->", + "output": "{\"text\": \"it ' s lightweight and fits easily in a tote or backpack .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Dim Sum was so-so , but not spectacular .\n->The Dim Sum was so-so , but not spectacular .\n[{'aspect': 'Dim Sum', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Dim Sum', 'opinion': 'not spectacular', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: seriously , this place kicks ass .\n->seriously , this place kicks ass .\n[{'aspect': 'place', 'opinion': 'kicks ass', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it holds up well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit holds up well .\n->", + "output": "{\"text\": \"it holds up well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n->he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n[{'aspect': 'uni hand roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i love this laptop so far .\n->i love this laptop so far .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i use it sort of all day and night and i haven ' t found anything it won ' t do .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use it sort of all day and night and i haven ' t found anything it won ' t do .\n->", + "output": "{\"text\": \"i use it sort of all day and night and i haven ' t found anything it won ' t do .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Simple comfort food and what hot and large portions .\n->Simple comfort food and what hot and large portions .\n[{'aspect': 'comfort food', 'opinion': 'Simple comfort', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n->What makes this restaurant special are the authentic sichuan cooking and being the only one in NYC that offers authentic chongqing hotpot .\n[{'aspect': 'sichuan cooking', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chongqing hotpot', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: occasionally it will get sort of slow and then i ' ll go to settings and clear the image cache and that takes care of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noccasionally it will get sort of slow and then i ' ll go to settings and clear the image cache and that takes care of it .\n->", + "output": "{\"text\": \"occasionally it will get sort of slow and then i ' ll go to settings and clear the image cache and that takes care of it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n->Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n[{'aspect': 'raw vegatables', 'opinion': 'wondered', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i really recommend the very simple unda ( egg ) rolls .\n->i really recommend the very simple unda ( egg ) rolls .\n[{'aspect': 'unda ( egg ) rolls', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'unda ( egg ) rolls', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the keyboard is nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is nice .\n->", + "output": "{\"text\": \"the keyboard is nice .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service was the only thing good about this restaurant .\n->the service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: My friend got the mushroom pizza which tasted better .\n->My friend got the mushroom pizza which tasted better .\n[{'aspect': 'mushroom pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i type 100 wpm and i like the keyboard and touch pad a lot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni type 100 wpm and i like the keyboard and touch pad a lot .\n->", + "output": "{\"text\": \"i type 100 wpm and i like the keyboard and touch pad a lot .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only detail that i have to reproach is that the connection cables are a little dirty , but in acceptable condition .\n->the only detail that i have to reproach is that the connection cables are a little dirty , but in acceptable condition .\n[{'aspect': 'connection cables', 'opinion': 'dirty', 'polarity': 'neutral', 'category': 'PORTS#QUALITY'}, {'aspect': 'connection cables', 'opinion': 'acceptable', 'polarity': 'neutral', 'category': 'PORTS#QUALITY'}]\nExample:\ntext: The wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n->The wine list is extensive and can easily hike up an otherwise reasonably priced meal .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the keys are responsive and quiet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keys are responsive and quiet .\n->", + "output": "{\"text\": \"the keys are responsive and quiet .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keys', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had the duck breast special on my last visit and it was incredible .\n->I had the duck breast special on my last visit and it was incredible .\n[{'aspect': 'duck breast special', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: build quality seems excellent .\n->build quality seems excellent .\n[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: the power cord is super compact and is just the plug and cord with no big clunky thing on it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe power cord is super compact and is just the plug and cord with no big clunky thing on it .\n->", + "output": "{\"text\": \"the power cord is super compact and is just the plug and cord with no big clunky thing on it .\", \"labels\": \"[{'aspect': 'power cord', 'opinion': 'compact', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend the sophia pizza .\n->i highly recommend the sophia pizza .\n[{'aspect': 'sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: However , I think Jeckll and Hydes t is one of those places that is fun to do once .\n->However , I think Jeckll and Hydes t is one of those places that is fun to do once .\n[{'aspect': 'Jeckll and Hydes', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\ntext: google updates are super fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoogle updates are super fast .\n->", + "output": "{\"text\": \"google updates are super fast .\", \"labels\": \"[{'aspect': 'google updates', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n->i have worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Yakitori ( bbq meats ) is tasty too .\n->Yakitori ( bbq meats ) is tasty too .\n[{'aspect': 'Yakitori ( bbq meats )', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s absolutely silent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s absolutely silent .\n->", + "output": "{\"text\": \"it ' s absolutely silent .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'silent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i ' m growing ever more disenchanted with the core m3 processing speed .\n->but i ' m growing ever more disenchanted with the core m3 processing speed .\n[{'aspect': 'core m3', 'opinion': 'disenchanted', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: multitasking is pretty good .\n->multitasking is pretty good .\n[{'aspect': 'multitasking', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: no fans grinding away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno fans grinding away .\n->", + "output": "{\"text\": \"no fans grinding away .\", \"labels\": \"[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a great place to meet up for some food and drinks . . .\n->a great place to meet up for some food and drinks . . .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Please if your thinking about it go , and stay the wait you wo n't be disappointed .\n->Please if your thinking about it go , and stay the wait you wo n't be disappointed .\n[{'aspect': 'wait', 'opinion': \"wo n't be disappointed\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: i sit with it in my lap all day long and it never gets hot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni sit with it in my lap all day long and it never gets hot .\n->", + "output": "{\"text\": \"i sit with it in my lap all day long and it never gets hot .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i love the chromebook overall\n->- i love the chromebook overall\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n->the menu looked great , and the waiter was very nice , but when the food came , it was average .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: i don ' t play video games but i imagine that it would play cloud - based games fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t play video games but i imagine that it would play cloud - based games fine .\n->", + "output": "{\"text\": \"i don ' t play video games but i imagine that it would play cloud - based games fine .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the microsd slot leaves an inserted card perfectly flush .\n->the microsd slot leaves an inserted card perfectly flush .\n[{'aspect': 'microsd slot', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the trackpad works well and the screen display is great too .\n->the trackpad works well and the screen display is great too .\n[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'screen display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n->", + "output": "{\"text\": \"i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - though the case is plastic , the keyboard area itself has a cold metallic feel .\n->- though the case is plastic , the keyboard area itself has a cold metallic feel .\n[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard area', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: Try green curry with vegetables .\n->Try green curry with vegetables .\n[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the only program i couldn ' t find a cloud solution for was scrivener .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only program i couldn ' t find a cloud solution for was scrivener .\n->", + "output": "{\"text\": \"the only program i couldn ' t find a cloud solution for was scrivener .\", \"labels\": \"[{'aspect': 'scrivener', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n->I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n[{'aspect': 'meal', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'nice', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is such a lovely , peaceful place to eat outside .\n->this is such a lovely , peaceful place to eat outside .\n[{'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'peaceful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: it holds up to constant use and it ' s really sturdy despite being really slim and lightweight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit holds up to constant use and it ' s really sturdy despite being really slim and lightweight .\n->", + "output": "{\"text\": \"it holds up to constant use and it ' s really sturdy despite being really slim and lightweight .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not sure if this is simply a deal accident , but my ssd failed within 4 months .\n->not sure if this is simply a deal accident , but my ssd failed within 4 months .\n[{'aspect': 'ssd', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n->The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n[{'aspect': 'eggplant parmesan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'baked ziti with meatsauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i have used this computer daily now for about 6 months , spending hours per day on it for an emt / paramedic class .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have used this computer daily now for about 6 months , spending hours per day on it for an emt / paramedic class .\n->", + "output": "{\"text\": \"i have used this computer daily now for about 6 months , spending hours per day on it for an emt / paramedic class .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the specs are good on this for a good cheap low end gaming machine .\n->the specs are good on this for a good cheap low end gaming machine .\n[{'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: it ' s extremely fast and has very little lag when opening pages or surfing the web .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s extremely fast and has very little lag when opening pages or surfing the web .\n->", + "output": "{\"text\": \"it ' s extremely fast and has very little lag when opening pages or surfing the web .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n->this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: authentic pakistani food .\n->authentic pakistani food .\n[{'aspect': 'pakistani food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: ( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n->", + "output": "{\"text\": \"( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\", \"labels\": \"[{'aspect': 'it', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n->i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n[{'aspect': 'NULL', 'opinion': 'biased', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n->i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n[{'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: , as it doesn ' t have large built in memory .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n, as it doesn ' t have large built in memory .\n->", + "output": "{\"text\": \", as it doesn ' t have large built in memory .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this happened 7 times within 20 minutes when i was working on something .\n->this happened 7 times within 20 minutes when i was working on something .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n->if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: first time chrome os user , very streamlined and easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst time chrome os user , very streamlined and easy to use .\n->", + "output": "{\"text\": \"first time chrome os user , very streamlined and easy to use .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'streamlined', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'chrome os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you want a casual neighborhood bistro that has great food and excellent service , this is the place .\n->If you want a casual neighborhood bistro that has great food and excellent service , this is the place .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is by far my favorite place in the neighborhood .\n->this is by far my favorite place in the neighborhood .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: amazing battery life , i could go on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazing battery life , i could go on .\n->", + "output": "{\"text\": \"amazing battery life , i could go on .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n->at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n->We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i can carry both of them in a reasonably sized purse and not hurt my shoulder .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can carry both of them in a reasonably sized purse and not hurt my shoulder .\n->", + "output": "{\"text\": \"i can carry both of them in a reasonably sized purse and not hurt my shoulder .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer has a solid processor and enough memory to handle pretty much anything the average user willl throw at it , short of graphics - heavy gaming .\n->the computer has a solid processor and enough memory to handle pretty much anything the average user willl throw at it , short of graphics - heavy gaming .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#QUALITY'}]\nExample:\ntext: also , the power button placement is not very good .\n->also , the power button placement is not very good .\n[{'aspect': 'power button', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: hit the power button and plug it in , it will be ready before you are .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhit the power button and plug it in , it will be ready before you are .\n->", + "output": "{\"text\": \"hit the power button and plug it in , it will be ready before you are .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n->For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n[{'aspect': 'lobby area', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n->as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n[{'aspect': 'cable', 'opinion': 'active', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'power led', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\n->", + "output": "{\"text\": \"the plastic seems sturdy , the keys don ' t stick * , the mousepad feels pleasant .\", \"labels\": \"[{'aspect': 'plastic', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keys', 'opinion': \"' t stick\", 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mousepad', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hope to continue to get many more years of use out of it !\n->hope to continue to get many more years of use out of it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n->it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it doesn ' t get super loud , but for normal usage situations it ' s fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit doesn ' t get super loud , but for normal usage situations it ' s fine .\n->", + "output": "{\"text\": \"it doesn ' t get super loud , but for normal usage situations it ' s fine .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bibimbap was average , but the stone bowl was n ' t even close to sizzling .\n->the bibimbap was average , but the stone bowl was n ' t even close to sizzling .\n[{'aspect': 'bibimbap', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'stone bowl', 'opinion': \"n ' t even close to sizzling\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n->i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n[{'aspect': 'chromebook', 'opinion': 'enthusiast', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: - it does slow down noticeably if you ' re doing too much at once .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- it does slow down noticeably if you ' re doing too much at once .\n->", + "output": "{\"text\": \"- it does slow down noticeably if you ' re doing too much at once .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not imagine better indian food in all of the city .\n->i can not imagine better indian food in all of the city .\n[{'aspect': 'indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The freshest , best variety , and the fastest delivery .\n->The freshest , best variety , and the fastest delivery .\n[{'aspect': 'variety', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if i have a video call going on facebook messenger , chrome will take three or four seconds to respond .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif i have a video call going on facebook messenger , chrome will take three or four seconds to respond .\n->", + "output": "{\"text\": \"if i have a video call going on facebook messenger , chrome will take three or four seconds to respond .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchpad is above average , but not great .\n->the touchpad is above average , but not great .\n[{'aspect': 'touchpad', 'opinion': 'above average', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n->to my great dismay and disappointment , the power icon read the exact same message : ` ` plugged in , not charging ` ` .\n[{'aspect': 'power icon', 'opinion': 'dismay', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'power icon', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: if you ' re going to be doing a lot of heavy lifting , this might not be the chromebook for you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re going to be doing a lot of heavy lifting , this might not be the chromebook for you .\n->", + "output": "{\"text\": \"if you ' re going to be doing a lot of heavy lifting , this might not be the chromebook for you .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: photoshop also runs very well .\n->photoshop also runs very well .\n[{'aspect': 'photoshop', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overall , the problems are fairly minor and for the price i ' m happy with what i got .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , the problems are fairly minor and for the price i ' m happy with what i got .\n->", + "output": "{\"text\": \"overall , the problems are fairly minor and for the price i ' m happy with what i got .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought this for web surfing at home .\n->bought this for web surfing at home .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this computer seemed very exciting but after having troubles with 3 of them i give up .\n->this computer seemed very exciting but after having troubles with 3 of them i give up .\n[{'aspect': 'computer', 'opinion': 'exciting', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i ' m travelling overseas with it , and it goes everywhere i do and does everything i do - except i can no longer multitask and annoy my mother in video calls !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m travelling overseas with it , and it goes everywhere i do and does everything i do - except i can no longer multitask and annoy my mother in video calls !\n->", + "output": "{\"text\": \"i ' m travelling overseas with it , and it goes everywhere i do and does everything i do - except i can no longer multitask and annoy my mother in video calls !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very impressed with this computer .\n->i am very impressed with this computer .\n[{'aspect': 'computer', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i look forward to eating here again\n->i look forward to eating here again\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\n->", + "output": "{\"text\": \"the 4gb / 32gb version is fast and smooth and has enough storage space for your work documents and e - books for ready access when no internet is available .\", \"labels\": \"[{'aspect': '4gb / 32gb version', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': '4gb / 32gb version', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage space', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this thing is good .\n->this thing is good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Overall , excellent restaurant !\n->Overall , excellent restaurant !\n[{'aspect': 'restaurant', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 5 pound laptop with its nine hour battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n5 pound laptop with its nine hour battery life .\n->", + "output": "{\"text\": \"5 pound laptop with its nine hour battery life .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , after a couple of months the keyboard case started to crack at the corner .\n->however , after a couple of months the keyboard case started to crack at the corner .\n[{'aspect': 'keyboard case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n->in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'chrome os', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\ntext: i * love * it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni * love * it !\n->", + "output": "{\"text\": \"i * love * it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: playing a movie couldn ' t even last the duration of the movie on the battery , again , disappointing .\n->playing a movie couldn ' t even last the duration of the movie on the battery , again , disappointing .\n[{'aspect': 'battery', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: love pizza 33 . . .\n->love pizza 33 . . .\n[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n->", + "output": "{\"text\": \"i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\", \"labels\": \"[{'aspect': '2012 chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The first time the sushi was outstanding , the second time it was a little bland .\n->The first time the sushi was outstanding , the second time it was a little bland .\n[{'aspect': 'sushi', 'opinion': 'outstanding', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: they were very abrupt with me when i called and actually claimed the food was late because they were out of rice .\n->they were very abrupt with me when i called and actually claimed the food was late because they were out of rice .\n[{'aspect': 'NULL', 'opinion': 'abrupt', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: * love * the feel and look of it , no complaints at all , samsung chromebooks are amazing !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* love * the feel and look of it , no complaints at all , samsung chromebooks are amazing !\n->", + "output": "{\"text\": \"* love * the feel and look of it , no complaints at all , samsung chromebooks are amazing !\", \"labels\": \"[{'aspect': 'samsung chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebooks', 'opinion': 'no complaints', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebooks', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little chromebook is very nice and is pretty much what i expected .\n->this little chromebook is very nice and is pretty much what i expected .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The service was attentive , yet discreet .\n->The service was attentive , yet discreet .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'NULL'}]\ntext: makes a huge difference in the customer service you ' ll get , and amazon ' s is outstanding !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmakes a huge difference in the customer service you ' ll get , and amazon ' s is outstanding !\n->", + "output": "{\"text\": \"makes a huge difference in the customer service you ' ll get , and amazon ' s is outstanding !\", \"labels\": \"[{'aspect': \"amazon ' s\", 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery is all day amazing\n->battery is all day amazing\n[{'aspect': 'battery', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: the asus chromebook flip is the best one i have owned .\n->the asus chromebook flip is the best one i have owned .\n[{'aspect': 'asus chromebook flip', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n->", + "output": "{\"text\": \"this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\", \"labels\": \"[{'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'samsung chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: still learning but it ' s a good computer and a great deal\n->still learning but it ' s a good computer and a great deal\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: however , our main course was wonderful .\n->however , our main course was wonderful .\n[{'aspect': 'main course', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: chromebooks do not have a cd / dvd drive or some other features of higher end laptops , but i love them ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchromebooks do not have a cd / dvd drive or some other features of higher end laptops , but i love them ! )\n->", + "output": "{\"text\": \"chromebooks do not have a cd / dvd drive or some other features of higher end laptops , but i love them ! )\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was also very good .\n->Service was also very good .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n->I would n't even have complained at all if the food at least tasted good but the quality of food was crappy , too .\n[{'aspect': 'quality of food', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it does what i need it to do and with a sd card the memory is greatly improved .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit does what i need it to do and with a sd card the memory is greatly improved .\n->", + "output": "{\"text\": \"it does what i need it to do and with a sd card the memory is greatly improved .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'greatly', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Complimentary stuff kept coming , and when the waiter saw me opening a gift , I received my dessert on a plate that had Happy Birthday written on it , with a candlevery nice touch , and attentive staff .\n->Complimentary stuff kept coming , and when the waiter saw me opening a gift , I received my dessert on a plate that had Happy Birthday written on it , with a candlevery nice touch , and attentive staff .\n[{'aspect': 'stuff', 'opinion': 'Complimentary', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - my only real gripe is that i wish it was .\n->- my only real gripe is that i wish it was .\n[{'aspect': 'NULL', 'opinion': 'gripe', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i will say this , if you ' re not doing video production and just need basic computering , these chromebooks are everything .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will say this , if you ' re not doing video production and just need basic computering , these chromebooks are everything .\n->", + "output": "{\"text\": \"i will say this , if you ' re not doing video production and just need basic computering , these chromebooks are everything .\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is a very good laptop .\n->it is a very good laptop .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great wine list ( italian ) , good food , service was INITIALLY fine .\n->great wine list ( italian ) , good food , service was INITIALLY fine .\n[{'aspect': 'wine list', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they ' re affordable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey ' re affordable .\n->", + "output": "{\"text\": \"they ' re affordable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n->She was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about Indian food .\n[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: be prepared to wait , because the place is pretty tiny .\n->be prepared to wait , because the place is pretty tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: they are lightweight and easy to carry .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey are lightweight and easy to carry .\n->", + "output": "{\"text\": \"they are lightweight and easy to carry .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .\n->the lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .\n[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster sandwich', 'opinion': 'not nearly enough', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: The pizza is overpriced and soggy .\n->The pizza is overpriced and soggy .\n[{'aspect': 'pizza', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: once you get accustomed to the interface , you realize they do everything you need .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonce you get accustomed to the interface , you realize they do everything you need .\n->", + "output": "{\"text\": \"once you get accustomed to the interface , you realize they do everything you need .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cozy romantic atomosphere with only around 15 tables at most .\n->cozy romantic atomosphere with only around 15 tables at most .\n[{'aspect': 'atomosphere', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atomosphere', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Then , get ripped on free box wine .\n->Then , get ripped on free box wine .\n[{'aspect': 'box wine', 'opinion': 'free', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it was easy to set up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was easy to set up .\n->", + "output": "{\"text\": \"it was easy to set up .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: over all the looks of the place exceeds the actual meals .\n->over all the looks of the place exceeds the actual meals .\n[{'aspect': 'looks', 'opinion': 'exceeds', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'meals', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: regardless , we ' ll be back and ca n ' t wait to visit in the summer to take advantage of the patio .\n->regardless , we ' ll be back and ca n ' t wait to visit in the summer to take advantage of the patio .\n[{'aspect': 'patio', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it ' s lightweight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s lightweight .\n->", + "output": "{\"text\": \"it ' s lightweight .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n->i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n[{'aspect': 'mizu', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: nice chromebook .\n->nice chromebook .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it looks nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit looks nice .\n->", + "output": "{\"text\": \"it looks nice .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: called acer support , they were completely useless .\n->called acer support , they were completely useless .\n[{'aspect': 'acer support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n->looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\ntext: and it was a very good price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand it was a very good price .\n->", + "output": "{\"text\": \"and it was a very good price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is only pretty good .\n->the screen is only pretty good .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: great neighborhood joint .\n->great neighborhood joint .\n[{'aspect': 'joint', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: great price - i always buy the warranty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat price - i always buy the warranty .\n->", + "output": "{\"text\": \"great price - i always buy the warranty .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'warranty', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first the volume does not get very loud .\n->first the volume does not get very loud .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n->the food is amazing . . . especially if you get the chef ' s tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': \"chef ' s tasting menu\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'selection of wines', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: excellent for those uses .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent for those uses .\n->", + "output": "{\"text\": \"excellent for those uses .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the three of us standing in front of her should have been an indication of how many of us there were .\n->the three of us standing in front of her should have been an indication of how many of us there were .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the appetizers are also delicious !\n->the appetizers are also delicious !\n[{'aspect': 'appetizers', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i have had my chromebook for 4 years still works great for internet , netflix , adult education classes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had my chromebook for 4 years still works great for internet , netflix , adult education classes .\n->", + "output": "{\"text\": \"i have had my chromebook for 4 years still works great for internet , netflix , adult education classes .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pretty fast processor .\n->pretty fast processor .\n[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: as many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .\n->as many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: fast shipping too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast shipping too !\n->", + "output": "{\"text\": \"fast shipping too !\", \"labels\": \"[{'aspect': 'shipping', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were charged full price .\n->we were charged full price .\n[{'aspect': 'NULL', 'opinion': 'full', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The food was good .\n->The food was good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: google is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoogle is amazing .\n->", + "output": "{\"text\": \"google is amazing .\", \"labels\": \"[{'aspect': 'google', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i guess the computer is quite okay for the price they are asking for it .\n->i guess the computer is quite okay for the price they are asking for it .\n[{'aspect': 'computer', 'opinion': 'okay', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n->the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n[{'aspect': 'NULL', 'opinion': 'not usable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: it ' s all about google but my kids really like it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s all about google but my kids really like it .\n->", + "output": "{\"text\": \"it ' s all about google but my kids really like it .\", \"labels\": \"[{'aspect': 'google', 'opinion': 'like', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Oh , do n't even let me start with how expensive the bills were !\n->Oh , do n't even let me start with how expensive the bills were !\n[{'aspect': 'bills', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We had Pam 's special fried fish and it was amazing .\n->We had Pam 's special fried fish and it was amazing .\n[{'aspect': \"Pam 's special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: she had no trouble learning how to use it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe had no trouble learning how to use it .\n->", + "output": "{\"text\": \"she had no trouble learning how to use it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n->Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n[{'aspect': 'food', 'opinion': 'loving', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dining experiences', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: guaranteed excellent customer service !\n->guaranteed excellent customer service !\n[{'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: this laptop is perfect for all my school work and is budget friendly as well !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is perfect for all my school work and is budget friendly as well !\n->", + "output": "{\"text\": \"this laptop is perfect for all my school work and is budget friendly as well !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'laptop', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .\n->i have to highly recommend the lobster roll - not to much mayo ; you can tell it was a fresh lobster .\n[{'aspect': 'lobster roll', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n->the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n[{'aspect': 'touchpad', 'opinion': 'sensitive', 'polarity': 'neutral', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: i can bring it anywhere because of how small it is : $\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can bring it anywhere because of how small it is : $\n->", + "output": "{\"text\": \"i can bring it anywhere because of how small it is : $\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and yes dal bukhara is so dam good and so are all the kababs .\n->and yes dal bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dal bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the chromebook is quite ideal for those who use their computer mostly for surfing the internet .\n->the chromebook is quite ideal for those who use their computer mostly for surfing the internet .\n[{'aspect': 'chromebook', 'opinion': 'ideal', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: it is perfect for his college courses , work and fun .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is perfect for his college courses , work and fun .\n->", + "output": "{\"text\": \"it is perfect for his college courses , work and fun .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: enjoyed a very nice caesar salad while my wife had arugula and goat cheese . . . . both very tasty .\n->enjoyed a very nice caesar salad while my wife had arugula and goat cheese . . . . both very tasty .\n[{'aspect': 'caesar salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'caesar salad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'arugula and goat cheese', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n->Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n[{'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m using this device to run a small etsy business and its perfect for my needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m using this device to run a small etsy business and its perfect for my needs .\n->", + "output": "{\"text\": \"i ' m using this device to run a small etsy business and its perfect for my needs .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Joe 's Pizza used to have the best slice until this pizzeria opened up .\n->Joe 's Pizza used to have the best slice until this pizzeria opened up .\n[{'aspect': 'slice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Okay service .\n->Okay service .\n[{'aspect': 'service', 'opinion': 'Okay', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: it ' s fast and very easy to use if you are familiar with google drive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s fast and very easy to use if you are familiar with google drive .\n->", + "output": "{\"text\": \"it ' s fast and very easy to use if you are familiar with google drive .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Delivery service is great too .\n->Delivery service is great too .\n[{'aspect': 'Delivery service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you know , i wanted to love this machine .\n->you know , i wanted to love this machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: what ' s more , the price was perfect as a small investment into my small business .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat ' s more , the price was perfect as a small investment into my small business .\n->", + "output": "{\"text\": \"what ' s more , the price was perfect as a small investment into my small business .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food looked very appetizing and delicious since it came on a variety of fancy plates .\n->The food looked very appetizing and delicious since it came on a variety of fancy plates .\n[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'plates', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n->From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n[{'aspect': 'beginning appetizers', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'scallops', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chocolate souffle with rasberry mint sorbet', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'taste', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is a good product at an affordable price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a good product at an affordable price .\n->", + "output": "{\"text\": \"it is a good product at an affordable price .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fish was not fresh and the rice tasted old and stale .\n->The fish was not fresh and the rice tasted old and stale .\n[{'aspect': 'fish', 'opinion': 'not fresh', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'old', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'stale', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'never had a problem', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this chromebook was one of the best gift to my grandauther .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook was one of the best gift to my grandauther .\n->", + "output": "{\"text\": \"this chromebook was one of the best gift to my grandauther .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do n ' t think i would go again .\n->i do n ' t think i would go again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n->its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n[{'aspect': 'laptop', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: it is a good product to buy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a good product to buy .\n->", + "output": "{\"text\": \"it is a good product to buy .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would recommend reservations on weekends though .\n->i would recommend reservations on weekends though .\n[{'aspect': 'reservations', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: overall impression : this is a killer laptop for a killer deal !\n->overall impression : this is a killer laptop for a killer deal !\n[{'aspect': 'laptop', 'opinion': 'killer', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: if you wan na spend a couple hundred for a laptop this is deffanently worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you wan na spend a couple hundred for a laptop this is deffanently worth it .\n->", + "output": "{\"text\": \"if you wan na spend a couple hundred for a laptop this is deffanently worth it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the camera sucks .\n->the camera sucks .\n[{'aspect': 'camera', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: i bought this for $ 389 on cyber monday 2017 .\n->i bought this for $ 389 on cyber monday 2017 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: setting it up is awkward because it ' s chrome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsetting it up is awkward because it ' s chrome .\n->", + "output": "{\"text\": \"setting it up is awkward because it ' s chrome .\", \"labels\": \"[{'aspect': 'chrome', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has a very fast 64gb ssd and 4gb on lpddr3 memory .\n->it has a very fast 64gb ssd and 4gb on lpddr3 memory .\n[{'aspect': '64gb ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'lpddr3 memory', 'opinion': 'fast', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the service was attentive , yet discreet .\n->the service was attentive , yet discreet .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the home screen is blank with a customizable photo that you can add but that ' s it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe home screen is blank with a customizable photo that you can add but that ' s it .\n->", + "output": "{\"text\": \"the home screen is blank with a customizable photo that you can add but that ' s it .\", \"labels\": \"[{'aspect': 'home screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A cool place to hang with your friends for a couple of healthy drinks and desserts .\n->A cool place to hang with your friends for a couple of healthy drinks and desserts .\n[{'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my roommate and i love this place .\n->my roommate and i love this place .\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: love it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove it !\n->", + "output": "{\"text\": \"love it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n->We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n[{'aspect': 'desserts', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cannoli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i found it on a cold night , the perfect spot to warm up .\n->i found it on a cold night , the perfect spot to warm up .\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: it arrived as promised and was exactly as described .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit arrived as promised and was exactly as described .\n->", + "output": "{\"text\": \"it arrived as promised and was exactly as described .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Finally a reliable Chinese restaurant !\n->Finally a reliable Chinese restaurant !\n[{'aspect': 'Chinese restaurant', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great indian food\n->great indian food\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: they loved them and said they worked perfectly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey loved them and said they worked perfectly .\n->", + "output": "{\"text\": \"they loved them and said they worked perfectly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i liked the fact that lenovo came with the microsoft programs on it .\n->i liked the fact that lenovo came with the microsoft programs on it .\n[{'aspect': 'lenovo', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Do n't miss Bloom 's on your next trip to Manhatten .\n->Do n't miss Bloom 's on your next trip to Manhatten .\n[{'aspect': \"Bloom 's\", 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\ntext: one of my favorite things i own\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of my favorite things i own\n->", + "output": "{\"text\": \"one of my favorite things i own\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a classic !\n->a classic !\n[{'aspect': 'NULL', 'opinion': 'classic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: on the flip side , this asus is very fast with minimal bloatware that is easy to get rid of .\n->on the flip side , this asus is very fast with minimal bloatware that is easy to get rid of .\n[{'aspect': 'asus', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'asus', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: if you do n ' t need a full blown laptop this is a good choice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you do n ' t need a full blown laptop this is a good choice .\n->", + "output": "{\"text\": \"if you do n ' t need a full blown laptop this is a good choice .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer arrived fine but will not charge .\n->this computer arrived fine but will not charge .\n[{'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Yakitori ( bbq meats ) is tasty too .\n->Yakitori ( bbq meats ) is tasty too .\n[{'aspect': 'Yakitori ( bbq meats )', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the speakers on at the front on bottom so sound quality isn ' t the best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speakers on at the front on bottom so sound quality isn ' t the best .\n->", + "output": "{\"text\": \"the speakers on at the front on bottom so sound quality isn ' t the best .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'sound quality', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just because it ' s cheap does not mean the portions are small or the food is nasty , it is great !\n->just because it ' s cheap does not mean the portions are small or the food is nasty , it is great !\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'nasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: as of now , highly recommended refurbished products\n->as of now , highly recommended refurbished products\n[{'aspect': 'products', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: battery life is great for reviewing adobe forms and web surfing , pretty good for youtube videos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is great for reviewing adobe forms and web surfing , pretty good for youtube videos .\n->", + "output": "{\"text\": \"battery life is great for reviewing adobe forms and web surfing , pretty good for youtube videos .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is reliable and the price is moderate .\n->the food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: Spreads and toppings are great - though a bit pricey .\n->Spreads and toppings are great - though a bit pricey .\n[{'aspect': 'Spreads', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Spreads', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: my only issue is the wifi likes to randomly turn off then back on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy only issue is the wifi likes to randomly turn off then back on .\n->", + "output": "{\"text\": \"my only issue is the wifi likes to randomly turn off then back on .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n->stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n[{'aspect': 'stylus', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'stylus', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: with chrome remote desktop i am able to play turn - based video games such as hearthstone or civ 5 while miles away from the computer actually running them .\n->with chrome remote desktop i am able to play turn - based video games such as hearthstone or civ 5 while miles away from the computer actually running them .\n[{'aspect': 'chrome remote desktop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: i was very surprised at how fast it came .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was very surprised at how fast it came .\n->", + "output": "{\"text\": \"i was very surprised at how fast it came .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'surprised', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love my c302 !\n->i love my c302 !\n[{'aspect': 'c302', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the rest of the dim sum , though pricey by chinatown standards , is worth it .\n->the rest of the dim sum , though pricey by chinatown standards , is worth it .\n[{'aspect': 'dim sum', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'dim sum', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: the only problem is that there is no caps lock button on the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only problem is that there is no caps lock button on the keyboard .\n->", + "output": "{\"text\": \"the only problem is that there is no caps lock button on the keyboard .\", \"labels\": \"[{'aspect': 'caps lock button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the $ 20 entree range is not overly expensive , in New York City , there is definitely better food in that range , and so Sapphire , despite it 's lovely atmosphere , will most likely not be a restaurant to which I will return .\n->While the $ 20 entree range is not overly expensive , in New York City , there is definitely better food in that range , and so Sapphire , despite it 's lovely atmosphere , will most likely not be a restaurant to which I will return .\n[{'aspect': 'food', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entree range', 'opinion': 'not overly expensive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n->it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n[{'aspect': 'company', 'opinion': 'poor', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: other than that it has been great so far !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother than that it has been great so far !\n->", + "output": "{\"text\": \"other than that it has been great so far !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was exactly what i needed and it performs as new which is exactly what i expected as well .\n->this was exactly what i needed and it performs as new which is exactly what i expected as well .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The place itself is beautiful the bar scene seems to be happening .\n->The place itself is beautiful the bar scene seems to be happening .\n[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i am sooooo glad i bought this one !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am sooooo glad i bought this one !\n->", + "output": "{\"text\": \"i am sooooo glad i bought this one !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best Italian food I ever had ( and being Italian , that means alot ) .\n->Best Italian food I ever had ( and being Italian , that means alot ) .\n[{'aspect': 'Italian food', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The pizza was great .\n->The pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: simple and nothing complicated about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsimple and nothing complicated about it .\n->", + "output": "{\"text\": \"simple and nothing complicated about it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the force touch trackpad works great .\n->the force touch trackpad works great .\n[{'aspect': 'force touch trackpad', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: But the staff was so horrible to us .\n->But the staff was so horrible to us .\n[{'aspect': 'staff', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: he has thoroughly enjoyed it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhe has thoroughly enjoyed it\n->", + "output": "{\"text\": \"he has thoroughly enjoyed it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery works well , i get nearly 4 to 5 hours easily .\n->battery works well , i get nearly 4 to 5 hours easily .\n[{'aspect': 'battery', 'opinion': 'well', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: and the blue mail email app handles my multiple emails from multiple domains .\n->and the blue mail email app handles my multiple emails from multiple domains .\n[{'aspect': 'blue mail email app', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: i am very happy with this purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very happy with this purchase .\n->", + "output": "{\"text\": \"i am very happy with this purchase .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: some apps don ' t play well yet , but should with time .\n->some apps don ' t play well yet , but should with time .\n[{'aspect': 'some apps', 'opinion': \"' t play well\", 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: we were ushered to the bar to wait momentarily and upon arrival were so excited .\n->we were ushered to the bar to wait momentarily and upon arrival were so excited .\n[{'aspect': 'NULL', 'opinion': 'excited', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the battery seriously lasts as long as i need it , and i ' m one to forget to charge it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery seriously lasts as long as i need it , and i ' m one to forget to charge it .\n->", + "output": "{\"text\": \"the battery seriously lasts as long as i need it , and i ' m one to forget to charge it .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall this is a very capable machine , better life is great as well .\n->overall this is a very capable machine , better life is great as well .\n[{'aspect': 'machine ,', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'better life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: i would definitely recommend this laptop to anyone who is looking for a cheap and nice computer .\n->i would definitely recommend this laptop to anyone who is looking for a cheap and nice computer .\n[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: although lightweight and less expensive , the lack of sensitivity of the touchpad makes using this with ease kind of frustrating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough lightweight and less expensive , the lack of sensitivity of the touchpad makes using this with ease kind of frustrating .\n->", + "output": "{\"text\": \"although lightweight and less expensive , the lack of sensitivity of the touchpad makes using this with ease kind of frustrating .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'touchpad', 'opinion': 'less expensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'touchpad', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in essence , if you want a gaming pc , this one will do the job .\n->in essence , if you want a gaming pc , this one will do the job .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it holds up well .\n->it holds up well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: worked as it should .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworked as it should .\n->", + "output": "{\"text\": \"worked as it should .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my old chromebook is an acer and i ' ve had absolutely no problems with it .\n->my old chromebook is an acer and i ' ve had absolutely no problems with it .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n->we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: not as much i can do on it , but it is adequate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot as much i can do on it , but it is adequate .\n->", + "output": "{\"text\": \"not as much i can do on it , but it is adequate .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'adequate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: much of the time it seems like they do not care about you .\n->much of the time it seems like they do not care about you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i had this for all of a day before it began to have severe issues i set it back to factory settings and that worked for a time but it eventually had an issue with system 32 .\n->i had this for all of a day before it began to have severe issues i set it back to factory settings and that worked for a time but it eventually had an issue with system 32 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'system 32', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: screen is a bit small for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen is a bit small for me .\n->", + "output": "{\"text\": \"screen is a bit small for me .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'small', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n->to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: this restaurant was way overhyped .\n->this restaurant was way overhyped .\n[{'aspect': 'restaurant', 'opinion': 'overhyped', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: i had hoped that doubling the ram would increase performance a bit , but it seems to run exactly the same .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had hoped that doubling the ram would increase performance a bit , but it seems to run exactly the same .\n->", + "output": "{\"text\": \"i had hoped that doubling the ram would increase performance a bit , but it seems to run exactly the same .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was slow , but the people were friendly .\n->Service was slow , but the people were friendly .\n[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n->graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n[{'aspect': 'graphic', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: which , to be sure , is great , i had just hoped for something slightly more agile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhich , to be sure , is great , i had just hoped for something slightly more agile .\n->", + "output": "{\"text\": \"which , to be sure , is great , i had just hoped for something slightly more agile .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they ' re affordable .\n->they ' re affordable .\n[{'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: highly recommend it !\n->highly recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i would buy again , especially at this price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would buy again , especially at this price .\n->", + "output": "{\"text\": \"i would buy again , especially at this price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ask for Usha , the nicest bartender in manhattan .\n->Ask for Usha , the nicest bartender in manhattan .\n[{'aspect': 'bartender', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n->lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n[{'aspect': 'NULL', 'opinion': 'rudeness', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: google docs are getting so good and user friendly who needs office anymore , content accessible anywhere , just takes a little time to get used to but once you realize all the benefits chromebook is a great choice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoogle docs are getting so good and user friendly who needs office anymore , content accessible anywhere , just takes a little time to get used to but once you realize all the benefits chromebook is a great choice .\n->", + "output": "{\"text\": \"google docs are getting so good and user friendly who needs office anymore , content accessible anywhere , just takes a little time to get used to but once you realize all the benefits chromebook is a great choice .\", \"labels\": \"[{'aspect': 'google docs', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google docs', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their Margarita is best I 've had since I 've returned from Naples !\n->Their Margarita is best I 've had since I 've returned from Naples !\n[{'aspect': 'Margarita', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: cirspy crust margherita pizza\n->cirspy crust margherita pizza\n[{'aspect': 'margherita pizza', 'opinion': 'cirspy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margherita pizza', 'opinion': 'crust', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: great cheap tool for web development ( using linux ) and everyday internet usage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat cheap tool for web development ( using linux ) and everyday internet usage .\n->", + "output": "{\"text\": \"great cheap tool for web development ( using linux ) and everyday internet usage .\", \"labels\": \"[{'aspect': 'tool', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything works fast and smooth .\n->everything works fast and smooth .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: love the cortana !\n->love the cortana !\n[{'aspect': 'cortana', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: every additional feature would be unnecessary for my personal usage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nevery additional feature would be unnecessary for my personal usage .\n->", + "output": "{\"text\": \"every additional feature would be unnecessary for my personal usage .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend this beautiful place .\n->i highly recommend this beautiful place .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: bottles of wine are cheap and good .\n->bottles of wine are cheap and good .\n[{'aspect': 'bottles of wine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}, {'aspect': 'bottles of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: love these chromebooks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove these chromebooks !\n->", + "output": "{\"text\": \"love these chromebooks !\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speed : it ' s very fast .\n->speed : it ' s very fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this makes this chromebook closer to a real computer .\n->this makes this chromebook closer to a real computer .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i find this chrome book is so much easy to use , it start up fast it is light weight ready to carry when traveling .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni find this chrome book is so much easy to use , it start up fast it is light weight ready to carry when traveling .\n->", + "output": "{\"text\": \"i find this chrome book is so much easy to use , it start up fast it is light weight ready to carry when traveling .\", \"labels\": \"[{'aspect': 'chrome book', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'chrome book', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chrome book', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: No food snobs allowed , this place is for people who appreciate good food .\n->No food snobs allowed , this place is for people who appreciate good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: computer came in good condition and at a good price .\n->computer came in good condition and at a good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#PRICE'}]\ntext: i can use this for school .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can use this for school .\n->", + "output": "{\"text\": \"i can use this for school .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it would not charge at all !\n->it would not charge at all !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: this is exactly what i need and nothing more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is exactly what i need and nothing more .\n->", + "output": "{\"text\": \"this is exactly what i need and nothing more .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: touchpad is nice and responsive .\n->touchpad is nice and responsive .\n[{'aspect': 'touchpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' d be horrified if my staff were turning away customers so early and so rudely !\n->i ' d be horrified if my staff were turning away customers so early and so rudely !\n[{'aspect': 'staff', 'opinion': 'horrified', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i like this chromebook a lot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like this chromebook a lot .\n->", + "output": "{\"text\": \"i like this chromebook a lot .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n->however , once i received my predictably mediocre order of what dokebi thinks passes as korean fair , ( sometimes you have to settle when it ' s your only option ) , i got through about half my kimchee before i found a piece of random lettuce accompanied by a far more disgusting , slimy , clearly bad piece of fish skin .\n[{'aspect': 'kimchee', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'slimy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchee', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'korean fair', 'opinion': 'mediocre', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I have to say I have never had a disapointing meal here .\n->I have to say I have never had a disapointing meal here .\n[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is very helpful and it is very fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is very helpful and it is very fast .\n->", + "output": "{\"text\": \"it is very helpful and it is very fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the cortana !\n->love the cortana !\n[{'aspect': 'cortana', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n->the pesto pizza was excellent , thin - crust pizza with a nice amount of spicy italian cheese that i ' d never heard of before .\n[{'aspect': 'pesto pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy italian cheese', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the battery will last a full day or two so it ' s very good for a middle school student .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery will last a full day or two so it ' s very good for a middle school student .\n->", + "output": "{\"text\": \"the battery will last a full day or two so it ' s very good for a middle school student .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my main complain involves terrible battery life .\n->my main complain involves terrible battery life .\n[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: the food is very good , but not outstanding .\n->the food is very good , but not outstanding .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'not outstanding', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: the only thing is that it does ' t have too much storage room .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing is that it does ' t have too much storage room .\n->", + "output": "{\"text\": \"the only thing is that it does ' t have too much storage room .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i fed up with the price it cost to upgrade the product as well as the software .\n->but i fed up with the price it cost to upgrade the product as well as the software .\n[{'aspect': 'NULL', 'opinion': 'fed up', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n->We took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i do n ' t think 16 gb is enough .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do n ' t think 16 gb is enough .\n->", + "output": "{\"text\": \"i do n ' t think 16 gb is enough .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n->suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n[{'aspect': 'keyboard', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n->The eggplant parmesan is also great , and my friend who grew up in Manhattan claims that no one serves a better baked ziti with meatsauce .\n[{'aspect': 'eggplant parmesan', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'baked ziti with meatsauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: greatest thing i ' ve bought myself in a long time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreatest thing i ' ve bought myself in a long time .\n->", + "output": "{\"text\": \"greatest thing i ' ve bought myself in a long time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n->The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n[{'aspect': 'wine list', 'opinion': \"is n't great\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'not as good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i agree that dining at casa la femme is like no other dining experience !\n->i agree that dining at casa la femme is like no other dining experience !\n[{'aspect': 'casa la femme', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: such a perfect little computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuch a perfect little computer .\n->", + "output": "{\"text\": \"such a perfect little computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: backlit keyboard is extremely viewable and comfortable .\n->backlit keyboard is extremely viewable and comfortable .\n[{'aspect': 'backlit keyboard', 'opinion': 'viewable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: it would not start up after 2 months of purchase and then re - set button didn ' t work .\n->it would not start up after 2 months of purchase and then re - set button didn ' t work .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 're - set button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: not happy that the l and the quotation mark key no longer work unless i bang on them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot happy that the l and the quotation mark key no longer work unless i bang on them .\n->", + "output": "{\"text\": \"not happy that the l and the quotation mark key no longer work unless i bang on them .\", \"labels\": \"[{'aspect': 'l and the quotation mark key', 'opinion': 'not happy', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: regardless , we ' ll be back and ca n ' t wait to visit in the summer to take advantage of the patio .\n->regardless , we ' ll be back and ca n ' t wait to visit in the summer to take advantage of the patio .\n[{'aspect': 'patio', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: you can right - click by using two fingers to tap the track pad at the same time ( or 2 fingers on the touchpad , using one of them to press press down / click the touchpad ) .\n->you can right - click by using two fingers to tap the track pad at the same time ( or 2 fingers on the touchpad , using one of them to press press down / click the touchpad ) .\n[{'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\ntext: excellent chromebooks !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent chromebooks !\n->", + "output": "{\"text\": \"excellent chromebooks !\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: had an awful experience at casa la femme on a saturday dinner .\n->had an awful experience at casa la femme on a saturday dinner .\n[{'aspect': 'casa la femme', 'opinion': 'awful', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: give it a try and enjoy .\n->give it a try and enjoy .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: we purchased 6 of these chromebooks for our organization and they work very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe purchased 6 of these chromebooks for our organization and they work very well .\n->", + "output": "{\"text\": \"we purchased 6 of these chromebooks for our organization and they work very well .\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You must try the shrimp appetizers .\n->You must try the shrimp appetizers .\n[{'aspect': 'shrimp appetizers', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is also one of the nicest most comfortable i have ever used .\n->the keyboard is also one of the nicest most comfortable i have ever used .\n[{'aspect': 'keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: battery life is excellent and our students love them !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is excellent and our students love them !\n->", + "output": "{\"text\": \"battery life is excellent and our students love them !\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: games are hit and miss .\n->games are hit and miss .\n[{'aspect': 'games', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: the battery stopped charging after 3 months .\n->the battery stopped charging after 3 months .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: great machine for all my needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat machine for all my needs .\n->", + "output": "{\"text\": \"great machine for all my needs .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's a rather cramped and busy restaurant and it closes early .\n->It 's a rather cramped and busy restaurant and it closes early .\n[{'aspect': 'restaurant', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'busy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n->i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n[{'aspect': 'touch pad', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: easiest computer to start up ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neasiest computer to start up ever .\n->", + "output": "{\"text\": \"easiest computer to start up ever .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'easiest', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'start up', 'opinion': 'easiest', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 'm still mad that i had to pay for lousy food .\n->I 'm still mad that i had to pay for lousy food .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n->Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Planet Thailand', 'opinion': 'hit', 'polarity': 'positive', 'category': 'NULL'}]\ntext: original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noriginal order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n->", + "output": "{\"text\": \"original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a real dissapointment .\n->a real dissapointment .\n[{'aspect': 'NULL', 'opinion': 'dissapointment', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I work near-by , and they have the BEST oatmeal in the neighborhood- not a packaged or quick-cooked item .\n->I work near-by , and they have the BEST oatmeal in the neighborhood- not a packaged or quick-cooked item .\n[{'aspect': 'oatmeal', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it works great for what i need .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit works great for what i need .\n->", + "output": "{\"text\": \"it works great for what i need .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ingredients are organic which is a real plus for me .\n->Ingredients are organic which is a real plus for me .\n[{'aspect': 'Ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if this guy is in your price range - just buy it and get it over with .\n->if this guy is in your price range - just buy it and get it over with .\n[{'aspect': 'guy', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: screen is not amazing but for the price , it gets the job done fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen is not amazing but for the price , it gets the job done fine .\n->", + "output": "{\"text\": \"screen is not amazing but for the price , it gets the job done fine .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'not amazing', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Toons has recently been redone , so it 's now a very attractive space .\n->Toons has recently been redone , so it 's now a very attractive space .\n[{'aspect': 'Toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food was good , the place was clean and affordable .\n->the food was good , the place was clean and affordable .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: the battery life is outstanding and the quality is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is outstanding and the quality is great .\n->", + "output": "{\"text\": \"the battery life is outstanding and the quality is great .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has served me very well ever since .\n->it has served me very well ever since .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is a great laptop .\n->this is a great laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: although the sound is not that good , this device replaced my ipad and i have never missed it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough the sound is not that good , this device replaced my ipad and i have never missed it !\n->", + "output": "{\"text\": \"although the sound is not that good , this device replaced my ipad and i have never missed it !\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a wonderful place on all stand points especially value ofr money .\n->this is a wonderful place on all stand points especially value ofr money .\n[{'aspect': 'place', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'place', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n->Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'regular', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: he likes it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhe likes it\n->", + "output": "{\"text\": \"he likes it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'likes', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n->I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: However , I think this place is a good hang out spot .\n->However , I think this place is a good hang out spot .\n[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this computer has a great battery life and it is just like every other computer just smaller size and it is a great brand name .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer has a great battery life and it is just like every other computer just smaller size and it is a great brand name .\n->", + "output": "{\"text\": \"this computer has a great battery life and it is just like every other computer just smaller size and it is a great brand name .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n->the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n[{'aspect': 'tablet mode', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: mussles and calamari were superb saturday evening .\n->mussles and calamari were superb saturday evening .\n[{'aspect': 'mussles', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'calamari', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: its is small and lite and the battery dose last long .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits is small and lite and the battery dose last long .\n->", + "output": "{\"text\": \"its is small and lite and the battery dose last long .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'small', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'lite', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am able to write , look up facts on the internet , and it has a pretty good battery life .\n->i am able to write , look up facts on the internet , and it has a pretty good battery life .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n->even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: for the price i think it ' s just fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the price i think it ' s just fine .\n->", + "output": "{\"text\": \"for the price i think it ' s just fine .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n->the outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n[{'aspect': 'outdoor atmosphere', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\nExample:\ntext: Service was prompt , friendly and great .\n->Service was prompt , friendly and great .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is perfect for my needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is perfect for my needs .\n->", + "output": "{\"text\": \"this is perfect for my needs .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: additionally , this has a nice backlight to the keyboard , which will turn off when nothing has been pressed for about 20 seconds ( great for when just watching or reading something ) or can be manually turned off entirely .\n->additionally , this has a nice backlight to the keyboard , which will turn off when nothing has been pressed for about 20 seconds ( great for when just watching or reading something ) or can be manually turned off entirely .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n->If you 'd like to have a nice light meal with an asian accent , Long Tan is a good place on the slope .\n[{'aspect': 'meal', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i am able to write , look up facts on the internet , and it has a pretty good battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am able to write , look up facts on the internet , and it has a pretty good battery life .\n->", + "output": "{\"text\": \"i am able to write , look up facts on the internet , and it has a pretty good battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for me dishes a little oily , but overall dining experience good .\n->for me dishes a little oily , but overall dining experience good .\n[{'aspect': 'dishes', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i love this little chrome book .\n->i love this little chrome book .\n[{'aspect': 'chrome book', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great for what i use it for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat for what i use it for .\n->", + "output": "{\"text\": \"great for what i use it for .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: * very weak wifi reception from the built - in antenna .\n->* very weak wifi reception from the built - in antenna .\n[{'aspect': 'wifi', 'opinion': 'weak', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: only used it a short while , but it seems like a sturdy nice little laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly used it a short while , but it seems like a sturdy nice little laptop .\n->", + "output": "{\"text\": \"only used it a short while , but it seems like a sturdy nice little laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great battery life , a matte screen ( non - glossy ) full hd .\n->great battery life , a matte screen ( non - glossy ) full hd .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'matte screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: it would power on , start to boot , then abruptly power down .\n->it would power on , start to boot , then abruptly power down .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n->", + "output": "{\"text\": \"i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'simple', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n->I recently went to this restaurant with some co-workers for lunch and had an amazing time .\n[{'aspect': 'restaurant', 'opinion': 'amazing time', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the fans did not turn on loudly if at all .\n->the fans did not turn on loudly if at all .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: i ' m satisfied with the product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m satisfied with the product .\n->", + "output": "{\"text\": \"i ' m satisfied with the product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicate spices , onions , eggs and a kick - ass roti .\n->delicate spices , onions , eggs and a kick - ass roti .\n[{'aspect': 'spices', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'onions', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'eggs', 'opinion': 'delicate', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'roti', 'opinion': 'kick - ass', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n->The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n[{'aspect': 'three course meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love it .\n->", + "output": "{\"text\": \"i love it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I did n't complain , I liked the atmosphere so much .\n->I did n't complain , I liked the atmosphere so much .\n[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very touchy touch screen , too sensitive .\n->very touchy touch screen , too sensitive .\n[{'aspect': 'touch screen', 'opinion': 'touchy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'sensitive', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: love it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove it !\n->", + "output": "{\"text\": \"love it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu changed , portions were even smaller than before , a lentil dish was salty beyond edibility , a basmati rice dish lacked flavor .\n->The menu changed , portions were even smaller than before , a lentil dish was salty beyond edibility , a basmati rice dish lacked flavor .\n[{'aspect': 'menu', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'lentil dish', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'basmati rice dish', 'opinion': 'lacked flavor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: very good breads as well .\n->very good breads as well .\n[{'aspect': 'breads', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: screen resolution could be slightly better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen resolution could be slightly better .\n->", + "output": "{\"text\": \"screen resolution could be slightly better .\", \"labels\": \"[{'aspect': 'screen resolution', 'opinion': 'better', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am returning immediately , no patience for this .\n->i am returning immediately , no patience for this .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The atmosphere is much better than Sripraphai ( more modern and sleek ) .\n->The atmosphere is much better than Sripraphai ( more modern and sleek ) .\n[{'aspect': 'atmosphere', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'modern', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very satisfied with simplicity of use , the streamlining of the google products , and the considerable battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery satisfied with simplicity of use , the streamlining of the google products , and the considerable battery life .\n->", + "output": "{\"text\": \"very satisfied with simplicity of use , the streamlining of the google products , and the considerable battery life .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'google products', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'considerable', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n->all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n[{'aspect': 'web browsing', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'NULL'}]\ntext: battery life is superb\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is superb\n->", + "output": "{\"text\": \"battery life is superb\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'superb', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only real complain is the same one everyone else has with this model and that is the battery life could be better .\n->the only real complain is the same one everyone else has with this model and that is the battery life could be better .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The entertainment was great they have shows that go on through out the dinner .\n->The entertainment was great they have shows that go on through out the dinner .\n[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: picked this up as something light and easy to carry around for working on personal coding projects while riding the bus .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npicked this up as something light and easy to carry around for working on personal coding projects while riding the bus .\n->", + "output": "{\"text\": \"picked this up as something light and easy to carry around for working on personal coding projects while riding the bus .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Rao 's has the best service and atmosphere in NYC .\n->Rao 's has the best service and atmosphere in NYC .\n[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: raymond the bartender rocks !\n->raymond the bartender rocks !\n[{'aspect': 'raymond', 'opinion': 'rocks', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: he ' s very happy with it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhe ' s very happy with it .\n->", + "output": "{\"text\": \"he ' s very happy with it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was there on sat . for my birthday and we had an excellent time .\n->i was there on sat . for my birthday and we had an excellent time .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i have also noticed the pro seems to run hotter and have a noticeably shorter battery life .\n->i have also noticed the pro seems to run hotter and have a noticeably shorter battery life .\n[{'aspect': 'pro', 'opinion': 'hotter', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'shorter', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: great little computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat little computer .\n->", + "output": "{\"text\": \"great little computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did not try the caviar but i tried their salmon and crab salad ( they are all good )\n->i did not try the caviar but i tried their salmon and crab salad ( they are all good )\n[{'aspect': 'salmon', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crab salad', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n->Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n[{'aspect': 'sushi place', 'opinion': 'Not the greatest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi place', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: good screen definition .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood screen definition .\n->", + "output": "{\"text\": \"good screen definition .\", \"labels\": \"[{'aspect': 'screen definition', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n->immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: and where does patis go wrong ; no where .\n->and where does patis go wrong ; no where .\n[{'aspect': 'patis', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i was worried about the battery life because of the reviews .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was worried about the battery life because of the reviews .\n->", + "output": "{\"text\": \"i was worried about the battery life because of the reviews .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'worried', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have been to Casimir over 5 times and I have always had a great time there .\n->I have been to Casimir over 5 times and I have always had a great time there .\n[{'aspect': 'Casimir', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n ' t like .\n->the food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n ' t like .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'prixe fixe tasting menu', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'prixe fixe tasting menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: however , i haven ' t had any disappoint with the battery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , i haven ' t had any disappoint with the battery .\n->", + "output": "{\"text\": \"however , i haven ' t had any disappoint with the battery .\", \"labels\": \"[{'aspect': 'battery', 'opinion': \"' t had any disappoint\", 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - it does slow down noticeably if you ' re doing too much at once .\n->- it does slow down noticeably if you ' re doing too much at once .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: ( coughlenovocough ) this is a beast .\n->( coughlenovocough ) this is a beast .\n[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: works great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks great .\n->", + "output": "{\"text\": \"works great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not only is the cuisine the best around , the service has always been attentive and charming .\n->Not only is the cuisine the best around , the service has always been attentive and charming .\n[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is the worst computer i have ever owned .\n->this is the worst computer i have ever owned .\n[{'aspect': 'computer', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: daughter school uses chrome for everything .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndaughter school uses chrome for everything .\n->", + "output": "{\"text\": \"daughter school uses chrome for everything .\", \"labels\": \"[{'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: average to good thai food , but terrible delivery .\n->average to good thai food , but terrible delivery .\n[{'aspect': 'thai food', 'opinion': 'average to good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great taste\n->great taste\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: granddaughter so loves it said it ' s the best christmas present ever\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngranddaughter so loves it said it ' s the best christmas present ever\n->", + "output": "{\"text\": \"granddaughter so loves it said it ' s the best christmas present ever\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only beverage we did receive was water in dirty glasses !\n->the only beverage we did receive was water in dirty glasses !\n[{'aspect': 'NULL', 'opinion': 'dirty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: LOVE THIS PLACE .\n->LOVE THIS PLACE .\n[{'aspect': 'PLACE', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: perfect ` ` computer ` ` for my young child .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nperfect ` ` computer ` ` for my young child .\n->", + "output": "{\"text\": \"perfect ` ` computer ` ` for my young child .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do not recommend .\n->i do not recommend .\n[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: skip dessert .\n->skip dessert .\n[{'aspect': 'dessert', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: so far no problems except i tried to print something from my email and am having trouble linking to my printer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far no problems except i tried to print something from my email and am having trouble linking to my printer .\n->", + "output": "{\"text\": \"so far no problems except i tried to print something from my email and am having trouble linking to my printer .\", \"labels\": \"[{'aspect': 'printer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i hope it is fixed this time .\n->i hope it is fixed this time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n->the hot dogs are top notch , and they ' re slamwich is amazing !\n[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: we are loving this chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe are loving this chromebook !\n->", + "output": "{\"text\": \"we are loving this chromebook !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were seated outside and the waiter spilled red wine and hot tea on myself and my date .\n->we were seated outside and the waiter spilled red wine and hot tea on myself and my date .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n->build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the os doesn ' t leave menu bar at the top for copying in programs for studies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe os doesn ' t leave menu bar at the top for copying in programs for studies .\n->", + "output": "{\"text\": \"the os doesn ' t leave menu bar at the top for copying in programs for studies .\", \"labels\": \"[{'aspect': 'os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i pray it stays open forever .\n->i pray it stays open forever .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we were very disappointed .\n->we were very disappointed .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: this is a great notebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great notebook .\n->", + "output": "{\"text\": \"this is a great notebook .\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: otherwise computer seems okay .\n->otherwise computer seems okay .\n[{'aspect': 'computer', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I love the atmorphere @ peep !\n->I love the atmorphere @ peep !\n[{'aspect': 'atmorphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: good quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood quality .\n->", + "output": "{\"text\": \"good quality .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it look like a professional laptop but overrall get this laptop you wo n ' t regret it , no negativity about it\n->it look like a professional laptop but overrall get this laptop you wo n ' t regret it , no negativity about it\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n->I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n[{'aspect': 'lamb chop', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: priced well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npriced well .\n->", + "output": "{\"text\": \"priced well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely the best chromebook out there .\n->definitely the best chromebook out there .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n->Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n[{'aspect': 'lunch', 'opinion': 'busier', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'seldom crowded', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: but you ca n ' t beat the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut you ca n ' t beat the price .\n->", + "output": "{\"text\": \"but you ca n ' t beat the price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The best pad thai i 've ever had .\n->The best pad thai i 've ever had .\n[{'aspect': 'pad thai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: And the staff is also young , energeic and hot ! ! ! !\n->And the staff is also young , energeic and hot ! ! ! !\n[{'aspect': 'staff', 'opinion': 'young', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'energeic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love this little chrome book .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this little chrome book .\n->", + "output": "{\"text\": \"i love this little chrome book .\", \"labels\": \"[{'aspect': 'chrome book', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The takeout menu says to keep an eye out for an expanded menu offering more italian dishes , I ca n't wait !\n->The takeout menu says to keep an eye out for an expanded menu offering more italian dishes , I ca n't wait !\n[{'aspect': 'menu', 'opinion': 'expanded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'italian dishes', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n->From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n[{'aspect': 'food', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s so easy to travel with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s so easy to travel with .\n->", + "output": "{\"text\": \"it ' s so easy to travel with .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: rao ' s has the best service and atmosphere in nyc .\n->rao ' s has the best service and atmosphere in nyc .\n[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the aluminum body looks great , but feels cheap and thin .\n->the aluminum body looks great , but feels cheap and thin .\n[{'aspect': 'aluminum body', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'aluminum body', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'aluminum body', 'opinion': 'thin', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: ca n ' t use itunes and stuff like that but it ' s ok still great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nca n ' t use itunes and stuff like that but it ' s ok still great .\n->", + "output": "{\"text\": \"ca n ' t use itunes and stuff like that but it ' s ok still great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great price - i always buy the warranty .\n->great price - i always buy the warranty .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'warranty', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}]\nExample:\ntext: so far great machine .\n->so far great machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: my daughter ordered the chromebook with her graduation money and she loves it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy daughter ordered the chromebook with her graduation money and she loves it .\n->", + "output": "{\"text\": \"my daughter ordered the chromebook with her graduation money and she loves it .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The restaurant is rather small but we were lucky to get a table quickly .\n->The restaurant is rather small but we were lucky to get a table quickly .\n[{'aspect': 'table', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: also battery life is max 8 hours , not 10 .\n->also battery life is max 8 hours , not 10 .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: this little computer is very fast and does a great job .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little computer is very fast and does a great job .\n->", + "output": "{\"text\": \"this little computer is very fast and does a great job .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was excellent - friendly and attentive .\n->The service was excellent - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: backlit keyboard is extremely viewable and comfortable .\n->backlit keyboard is extremely viewable and comfortable .\n[{'aspect': 'backlit keyboard', 'opinion': 'viewable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: very pleased with this computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery pleased with this computer .\n->", + "output": "{\"text\": \"very pleased with this computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the customer / warranty service that i received was first class and i am very impressed by asus for this reason alone .\n->the customer / warranty service that i received was first class and i am very impressed by asus for this reason alone .\n[{'aspect': 'customer / warranty service', 'opinion': 'first class', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: We have never had any problems with charging the meal or the tip , and the food was delivered quickly , but we live only a few minutes walk from them .\n->We have never had any problems with charging the meal or the tip , and the food was delivered quickly , but we live only a few minutes walk from them .\n[{'aspect': 'meal', 'opinion': 'never had any problems', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delivered quickly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tip', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: package arrived faster than the estimated arrival .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npackage arrived faster than the estimated arrival .\n->", + "output": "{\"text\": \"package arrived faster than the estimated arrival .\", \"labels\": \"[{'aspect': 'package', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n->well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n->While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n[{'aspect': 'room', 'opinion': 'not particularly comfortable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the computer runs great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer runs great .\n->", + "output": "{\"text\": \"the computer runs great .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not imagine better indian food in all of the city .\n->i can not imagine better indian food in all of the city .\n[{'aspect': 'indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it began to shut down and restart all on it ' s own - continuously .\n->it began to shut down and restart all on it ' s own - continuously .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: 1 month update : chromebook is still working great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n1 month update : chromebook is still working great .\n->", + "output": "{\"text\": \"1 month update : chromebook is still working great .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it runs most of those apps and games beautifully and when combined with a ` ` logitech gamepad f310 ` ` you can play the games with a game controller !\n->it runs most of those apps and games beautifully and when combined with a ` ` logitech gamepad f310 ` ` you can play the games with a game controller !\n[{'aspect': 'apps', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: blackboard works fine for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nblackboard works fine for me .\n->", + "output": "{\"text\": \"blackboard works fine for me .\", \"labels\": \"[{'aspect': 'blackboard', 'opinion': 'fine', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nothing better than buying a snapple for $ 3.25 too .\n->Nothing better than buying a snapple for $ 3.25 too .\n[{'aspect': 'snapple', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: its is small and lite and the battery dose last long .\n->its is small and lite and the battery dose last long .\n[{'aspect': 'NULL', 'opinion': 'small', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'lite', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: speaker is only good for watching a movie in a quiet room .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeaker is only good for watching a movie in a quiet room .\n->", + "output": "{\"text\": \"speaker is only good for watching a movie in a quiet room .\", \"labels\": \"[{'aspect': 'speaker', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I really liked this place .\n->I really liked this place .\n[{'aspect': 'place', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: : p ) , and the machine is definitely zippy .\n->: p ) , and the machine is definitely zippy .\n[{'aspect': 'machine', 'opinion': 'zippy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i really like my chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really like my chromebook .\n->", + "output": "{\"text\": \"i really like my chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n->However , their popularity has yet to slow down , and I still find myself drawn to their ambiance and delectable reputation .\n[{'aspect': 'ambiance', 'opinion': 'drawn', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: And they have these home made potato chips at the bar that are the most delicious things in the world !\n->And they have these home made potato chips at the bar that are the most delicious things in the world !\n[{'aspect': 'potato chips', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: everything works great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything works great .\n->", + "output": "{\"text\": \"everything works great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen does not wobble as there is a good amount of friction holding it where you put it .\n->the screen does not wobble as there is a good amount of friction holding it where you put it .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n->I do suggest to ask to be seated upstairs if you are looking to be a little cozy .\n[{'aspect': 'upstairs', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat chromebook .\n->", + "output": "{\"text\": \"great chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Two words : Free wine .\n->Two words : Free wine .\n[{'aspect': 'wine', 'opinion': 'Free', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: ( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n->( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n[{'aspect': 'serve', 'opinion': 'impresses', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'serve', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: has all the top features and runs fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhas all the top features and runs fast .\n->", + "output": "{\"text\": \"has all the top features and runs fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is always very crowded and popular .\n->This place is always very crowded and popular .\n[{'aspect': 'place', 'opinion': 'crowded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'popular', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I REALLY ENJOYED THE SHOWS PUT ON BY THE ACTORS .\n->I REALLY ENJOYED THE SHOWS PUT ON BY THE ACTORS .\n[{'aspect': 'SHOWS', 'opinion': 'ENJOYED', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ACTORS', 'opinion': 'ENJOYED', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they do a ton of online homework and this is perfect for them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey do a ton of online homework and this is perfect for them .\n->", + "output": "{\"text\": \"they do a ton of online homework and this is perfect for them .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: from the spectacular caviar to the hospitable waitstaff , i felt like royalty and enjoyed every second of it .\n->from the spectacular caviar to the hospitable waitstaff , i felt like royalty and enjoyed every second of it .\n[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n->maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: this does exactly what i need , writing on google docs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis does exactly what i need , writing on google docs .\n->", + "output": "{\"text\": \"this does exactly what i need , writing on google docs .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portion sizes here are huge , and the sushi is good .\n->The portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: am i just unlucky , or was this a bad batch ?\n->am i just unlucky , or was this a bad batch ?\n[{'aspect': 'NULL', 'opinion': 'unlucky', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: only thing is i am not sure if there is a delete key , something i use a lot\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly thing is i am not sure if there is a delete key , something i use a lot\n->", + "output": "{\"text\": \"only thing is i am not sure if there is a delete key , something i use a lot\", \"labels\": \"[{'aspect': 'delete key', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when we sat , we got great and fast service .\n->when we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: one thing keeps it from getting a five - star rave from me .\n->one thing keeps it from getting a five - star rave from me .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: this is a great laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great laptop .\n->", + "output": "{\"text\": \"this is a great laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: power turning on failed .\n->power turning on failed .\n[{'aspect': 'power', 'opinion': 'failed', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the staff is no nonsense .\n->the staff is no nonsense .\n[{'aspect': 'staff', 'opinion': 'no nonsense', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: so easy to use and is not that slow like some say works just fine for casual use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso easy to use and is not that slow like some say works just fine for casual use .\n->", + "output": "{\"text\": \"so easy to use and is not that slow like some say works just fine for casual use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'not that slow', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n->However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is great and they have a good selection of wines at reasonable prices .\n->The food is great and they have a good selection of wines at reasonable prices .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i got this for my mother in law and she could not be happier with how it works .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got this for my mother in law and she could not be happier with how it works .\n->", + "output": "{\"text\": \"i got this for my mother in law and she could not be happier with how it works .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not be happier', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only thing more wonderful than the food ( which is exceptional ) is the service .\n->The only thing more wonderful than the food ( which is exceptional ) is the service .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is good , the tablet mode is nice , and the keyboard has a good feel .\n->the screen is good , the tablet mode is nice , and the keyboard has a good feel .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: it was free with my phone , so the price was phenomenal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was free with my phone , so the price was phenomenal .\n->", + "output": "{\"text\": \"it was free with my phone , so the price was phenomenal .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n->i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n[{'aspect': 'word on line', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n->portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: solid chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsolid chromebook .\n->", + "output": "{\"text\": \"solid chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really like this chromebook .\n->i really like this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the track pad is one of the best i have seen for a non - apple touch pad .\n->the track pad is one of the best i have seen for a non - apple touch pad .\n[{'aspect': 'track pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: i like the ease of connecting to the internet wi - fi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like the ease of connecting to the internet wi - fi .\n->", + "output": "{\"text\": \"i like the ease of connecting to the internet wi - fi .\", \"labels\": \"[{'aspect': 'wi - fi', 'opinion': 'ease', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in the summer months , the back garden area is really nice .\n->in the summer months , the back garden area is really nice .\n[{'aspect': 'back garden area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: nicely sized , thin and portable , the works .\n->nicely sized , thin and portable , the works .\n[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: such a good investment , its so useful and works perfectly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuch a good investment , its so useful and works perfectly .\n->", + "output": "{\"text\": \"such a good investment , its so useful and works perfectly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My fav was the sassy lassi ...\n->My fav was the sassy lassi ...\n[{'aspect': 'sassy lassi', 'opinion': 'fav', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what ' s not to like , it ' s an amazing machine .\n->what ' s not to like , it ' s an amazing machine .\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i woudlnt recommend more than 10 active tabs which slows down some of the functionality but otherwise runs smoothly for students\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni woudlnt recommend more than 10 active tabs which slows down some of the functionality but otherwise runs smoothly for students\n->", + "output": "{\"text\": \"i woudlnt recommend more than 10 active tabs which slows down some of the functionality but otherwise runs smoothly for students\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: anyways , i am a bit sad that the ssd is a bit hidden and with very low memory , especially since i launched it and had to deal with half of it being full of bloatware ( btw , anyone who is looking for playing games on it , remember that the ssd will be faster and better at handling them , not the hard drive , all your programs and games should be installed to the ssd , all your files to the hard drive for those who don ' t know ) of course , with a little luck , that is fixable .\n->anyways , i am a bit sad that the ssd is a bit hidden and with very low memory , especially since i launched it and had to deal with half of it being full of bloatware ( btw , anyone who is looking for playing games on it , remember that the ssd will be faster and better at handling them , not the hard drive , all your programs and games should be installed to the ssd , all your files to the hard drive for those who don ' t know ) of course , with a little luck , that is fixable .\n[{'aspect': 'ssd', 'opinion': 'luck', 'polarity': 'positive', 'category': 'HARD_DISC#DESIGN_FEATURES'}, {'aspect': 'ssd', 'opinion': 'fixable', 'polarity': 'positive', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\nExample:\ntext: lobster was good , nothing spectacular .\n->lobster was good , nothing spectacular .\n[{'aspect': 'lobster', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'nothing spectacular', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: this device is mainly used for web browsing and pages load quickly , animations are swift and not laggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis device is mainly used for web browsing and pages load quickly , animations are swift and not laggy .\n->", + "output": "{\"text\": \"this device is mainly used for web browsing and pages load quickly , animations are swift and not laggy .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'swift', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'not laggy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this laptop so far .\n->i love this laptop so far .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: buy this thing if you want a cheap , snappy web browser on - the - go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuy this thing if you want a cheap , snappy web browser on - the - go .\n->", + "output": "{\"text\": \"buy this thing if you want a cheap , snappy web browser on - the - go .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my biggest gripe with this that makes me incredibly frustrated is that google hangouts is unreliable on this .\n->my biggest gripe with this that makes me incredibly frustrated is that google hangouts is unreliable on this .\n[{'aspect': 'google hangouts', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google hangouts', 'opinion': 'frustrated', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The puke green walls leave a lot to be desired , but the food is very good .\n->The puke green walls leave a lot to be desired , but the food is very good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'walls', 'opinion': 'desired', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i am beyond impressed with this little machine , i would absolutly buy this again !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am beyond impressed with this little machine , i would absolutly buy this again !\n->", + "output": "{\"text\": \"i am beyond impressed with this little machine , i would absolutly buy this again !\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought 2 , one stopped working after 9 months , was sent for ` ` repair ` ` it wasn ' t came right back with the same issue .\n->i bought 2 , one stopped working after 9 months , was sent for ` ` repair ` ` it wasn ' t came right back with the same issue .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: it was totally overpriced - fish and chips was about $ 15 . . . .\n->it was totally overpriced - fish and chips was about $ 15 . . . .\n[{'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'fish and chips', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: battery works well , i get nearly 4 to 5 hours easily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery works well , i get nearly 4 to 5 hours easily .\n->", + "output": "{\"text\": \"battery works well , i get nearly 4 to 5 hours easily .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'well', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is fast and friendly .\n->service is fast and friendly .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: on that scale , it ' s a world - beater .\n->on that scale , it ' s a world - beater .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: this item is excellent and not bad for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis item is excellent and not bad for the price .\n->", + "output": "{\"text\": \"this item is excellent and not bad for the price .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'item', 'opinion': 'not bad', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: comfortable keyboard !\n->comfortable keyboard !\n[{'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n->Excellent atmosphere , delicious dishes good and friendly service .\n[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i just got mine the other day , & am still getting used to it , but at the moment i couldn ' t be happier , it will do everything i want it to do , & i couldn ' t care less about the things it won ' t do .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just got mine the other day , & am still getting used to it , but at the moment i couldn ' t be happier , it will do everything i want it to do , & i couldn ' t care less about the things it won ' t do .\n->", + "output": "{\"text\": \"i just got mine the other day , & am still getting used to it , but at the moment i couldn ' t be happier , it will do everything i want it to do , & i couldn ' t care less about the things it won ' t do .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': \"' t be happier\", 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the flip and touchscreen aspects work fine , no problems .\n->the flip and touchscreen aspects work fine , no problems .\n[{'aspect': 'flip', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: A restaurant that does n't try to do anything except serve great food with great service in a pleasant atmosphere .\n->A restaurant that does n't try to do anything except serve great food with great service in a pleasant atmosphere .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i would recommend this machine to anyone who wants an inexpensive web - content device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would recommend this machine to anyone who wants an inexpensive web - content device .\n->", + "output": "{\"text\": \"i would recommend this machine to anyone who wants an inexpensive web - content device .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the flavors are amazing and the value is phenomenal .\n->the flavors are amazing and the value is phenomenal .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: second where the heck is my other 8 gigs of ram ?\n->second where the heck is my other 8 gigs of ram ?\n[{'aspect': '8 gigs of ram', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\ntext: choose this one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchoose this one .\n->", + "output": "{\"text\": \"choose this one .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my friends , both female and male , are going to by this set up for their travel needs but for me it is lacking .\n->my friends , both female and male , are going to by this set up for their travel needs but for me it is lacking .\n[{'aspect': 'set up', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: quick startup and has a nice display which is matte .\n->quick startup and has a nice display which is matte .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: the overall durability is a bit suspect but with the price tag , it is essentially a 1 - 2 year investment for school .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe overall durability is a bit suspect but with the price tag , it is essentially a 1 - 2 year investment for school .\n->", + "output": "{\"text\": \"the overall durability is a bit suspect but with the price tag , it is essentially a 1 - 2 year investment for school .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'suspect', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our waiter and all of the people helping him were attentive and genuine .\n->Our waiter and all of the people helping him were attentive and genuine .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'genuine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Indoor was very cozy and cute .\n->Indoor was very cozy and cute .\n[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\ntext: chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n->", + "output": "{\"text\": \"chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: More important , the sushi rivals the best in Tokyo .\n->More important , the sushi rivals the best in Tokyo .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i had lobster bisque it has 2 oz . of maine lobster in it .\n->i had lobster bisque it has 2 oz . of maine lobster in it .\n[{'aspect': 'lobster bisque', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: works well for writing novels which is what i bought it for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks well for writing novels which is what i bought it for .\n->", + "output": "{\"text\": \"works well for writing novels which is what i bought it for .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we have gone for dinner only a few times but the same great quality and service is given .\n->we have gone for dinner only a few times but the same great quality and service is given .\n[{'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n->The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spaghetti with Scallops and Shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it printed easily to our wireless printer too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit printed easily to our wireless printer too !\n->", + "output": "{\"text\": \"it printed easily to our wireless printer too !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easily', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the trackpad is the ultimate difference - maker for me .\n->the trackpad is the ultimate difference - maker for me .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: zero ambiance to boot .\n->zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'zero', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: kinda small but good quality !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkinda small but good quality !\n->", + "output": "{\"text\": \"kinda small but good quality !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the batrey charges very quickly , you will need 1 hour for a full charge .\n->the batrey charges very quickly , you will need 1 hour for a full charge .\n[{'aspect': 'batrey charges', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is very easy to use the set up was awesome .\n->it is very easy to use the set up was awesome .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}, {'aspect': 'set up', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: she loves it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe loves it .\n->", + "output": "{\"text\": \"she loves it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n->We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n[{'aspect': 'scallops', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s very easy to set up and use\n->it ' s very easy to set up and use\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: love this took place of my laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove this took place of my laptop .\n->", + "output": "{\"text\": \"love this took place of my laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n->The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n[{'aspect': 'seating', 'opinion': 'drafty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'tight', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n->We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love my laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love my laptop !\n->", + "output": "{\"text\": \"i love my laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not super confident this asus unit will last half that long .\n->not super confident this asus unit will last half that long .\n[{'aspect': 'asus unit', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: food is excellent .\n->food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: great laptop for a good price too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat laptop for a good price too !\n->", + "output": "{\"text\": \"great laptop for a good price too !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lenovo paid for my shipping to the repair facility since i ' d had it such a short time , and they fixed and returned it in a reasonable time period .\n->lenovo paid for my shipping to the repair facility since i ' d had it such a short time , and they fixed and returned it in a reasonable time period .\n[{'aspect': 'lenovo', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\nExample:\ntext: Overall , the best bagel in town .\n->Overall , the best bagel in town .\n[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great buy , love the smaller size too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat buy , love the smaller size too !\n->", + "output": "{\"text\": \"great buy , love the smaller size too !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i expected quite a bit more from such an expensive menu .\n->i expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n->the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n[{'aspect': 'intel rst driver', 'opinion': 'old', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: this little computer is awesome and it was so inexpensive for what you get !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little computer is awesome and it was so inexpensive for what you get !\n->", + "output": "{\"text\": \"this little computer is awesome and it was so inexpensive for what you get !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n->received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n[{'aspect': 'cpu', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: the screen is nice .\n->the screen is nice .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: very happy with this purchase\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery happy with this purchase\n->", + "output": "{\"text\": \"very happy with this purchase\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n->The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n[{'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: now it kind of feels a little bit like windows but still different .\n->now it kind of feels a little bit like windows but still different .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}]\ntext: delivered quickly , easy set up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndelivered quickly , easy set up .\n->", + "output": "{\"text\": \"delivered quickly , easy set up .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 2 ) slow start up and performance given\n->2 ) slow start up and performance given\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The soup is pretty good too .\n->The soup is pretty good too .\n[{'aspect': 'soup', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: surprisingly fast for such a small chrome book .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsurprisingly fast for such a small chrome book .\n->", + "output": "{\"text\": \"surprisingly fast for such a small chrome book .\", \"labels\": \"[{'aspect': 'chrome book', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stick with the chicken , beef , and lamb dishes .\n->stick with the chicken , beef , and lamb dishes .\n[{'aspect': 'chicken', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb dishes', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We had Pam 's special fried fish and it was amazing .\n->We had Pam 's special fried fish and it was amazing .\n[{'aspect': \"Pam 's special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: worked well when i had it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworked well when i had it .\n->", + "output": "{\"text\": \"worked well when i had it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Turned out there was full service upstairs and sat down .\n->Turned out there was full service upstairs and sat down .\n[{'aspect': 'service', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n->also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n[{'aspect': 'service', 'opinion': 'place', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'the', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'just', 'opinion': \"' re\", 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: love the mobility of this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the mobility of this .\n->", + "output": "{\"text\": \"love the mobility of this .\", \"labels\": \"[{'aspect': 'mobility', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try sushimi cucumber roll .\n->try sushimi cucumber roll .\n[{'aspect': 'sushimi cucumber roll', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the battery on this computer is not very good i feel like i always have to have it plugged in .\n->the battery on this computer is not very good i feel like i always have to have it plugged in .\n[{'aspect': 'battery', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neasy to use .\n->", + "output": "{\"text\": \"easy to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: moderate prices .\n->moderate prices .\n[{'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: mac is life , but i take one star away for the price .\n->mac is life , but i take one star away for the price .\n[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: i bought this for a linux machine and it does that just great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this for a linux machine and it does that just great .\n->", + "output": "{\"text\": \"i bought this for a linux machine and it does that just great .\", \"labels\": \"[{'aspect': 'linux machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n->thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n->The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: another thing , it is much thinner than expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nanother thing , it is much thinner than expected .\n->", + "output": "{\"text\": \"another thing , it is much thinner than expected .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: im actually wondering if there is an issue with the speakers , it ' s so bad .\n->im actually wondering if there is an issue with the speakers , it ' s so bad .\n[{'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: my desktop runs all my video editing software for my bigger and most demanding projects and my laptop is great for editing video and running pc games in places other than my home ( like school ! )\n->my desktop runs all my video editing software for my bigger and most demanding projects and my laptop is great for editing video and running pc games in places other than my home ( like school ! )\n[{'aspect': 'desktop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: nice little notebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice little notebook !\n->", + "output": "{\"text\": \"nice little notebook !\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n->one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n->i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: i love this one !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this one !\n->", + "output": "{\"text\": \"i love this one !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very good to use in korea .\n->it is very good to use in korea .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the speakers on this model are really nice as well .\n->the speakers on this model are really nice as well .\n[{'aspect': 'speakers', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: i don ' t do much more than google searches , so the lower end model works great for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t do much more than google searches , so the lower end model works great for me .\n->", + "output": "{\"text\": \"i don ' t do much more than google searches , so the lower end model works great for me .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n->i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n[{'aspect': 'device', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: The table next to us asked if he crushed the grapes himself when their long overdue bottle of wine finally arrived .\n->The table next to us asked if he crushed the grapes himself when their long overdue bottle of wine finally arrived .\n[{'aspect': 'bottle of wine', 'opinion': 'long overdue', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i am very happy with this item .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very happy with this item .\n->", + "output": "{\"text\": \"i am very happy with this item .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n->they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: The wine selection ( by the glass and bottle ) is wonderful and I always recommend that friends make a reservation if they 're going to be in town .\n->The wine selection ( by the glass and bottle ) is wonderful and I always recommend that friends make a reservation if they 're going to be in town .\n[{'aspect': 'wine selection', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'reservation', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: long battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlong battery life .\n->", + "output": "{\"text\": \"long battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard has a nice feel to it .\n->keyboard has a nice feel to it .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: i like the ambience , it ' s very dark and original .\n->i like the ambience , it ' s very dark and original .\n[{'aspect': 'ambience', 'opinion': 'like', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'dark', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'original', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: lenovo has not disappointed with their products .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlenovo has not disappointed with their products .\n->", + "output": "{\"text\": \"lenovo has not disappointed with their products .\", \"labels\": \"[{'aspect': 'lenovo', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'products', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food here is rather good , but only if you like to wait for it .\n->The food here is rather good , but only if you like to wait for it .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n->even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'working unit', 'opinion': 'outweighs', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it ' s a solid and quality built laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a solid and quality built laptop .\n->", + "output": "{\"text\": \"it ' s a solid and quality built laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'quality built', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n->The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n[{'aspect': 'anti-pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the computer runs great .\n->the computer runs great .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: overall for the price range , it ' s a fantastic laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall for the price range , it ' s a fantastic laptop .\n->", + "output": "{\"text\": \"overall for the price range , it ' s a fantastic laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place itself is beautiful the bar scene seems to be happening .\n->the place itself is beautiful the bar scene seems to be happening .\n[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'bar scene', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: we had a very nice time .\n->we had a very nice time .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: 2 ) the battery was pretty limited .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n2 ) the battery was pretty limited .\n->", + "output": "{\"text\": \"2 ) the battery was pretty limited .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'limited', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n->After complaining about the chicken dish , the manager came over to tell us that , no one had ever complained before , and that we just did n't know what the dish was supposed to taste like .\n[{'aspect': 'chicken dish', 'opinion': 'complaining', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n->the place is small and intimate and you may feel a little crowded , but the service is excellent and it ' s great for friends out , a romantic date , or a special occassion .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: the computer has a solid processor and enough memory to handle pretty much anything the average user willl throw at it , short of graphics - heavy gaming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer has a solid processor and enough memory to handle pretty much anything the average user willl throw at it , short of graphics - heavy gaming .\n->", + "output": "{\"text\": \"the computer has a solid processor and enough memory to handle pretty much anything the average user willl throw at it , short of graphics - heavy gaming .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza is delicious and the proprietor is one of the nicest in nyc .\n->the pizza is delicious and the proprietor is one of the nicest in nyc .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i will never buy another asus after this experience .\n->i will never buy another asus after this experience .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: that said : if you can deal with the short battery life , you ' re not going to find a better machine at this price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat said : if you can deal with the short battery life , you ' re not going to find a better machine at this price .\n->", + "output": "{\"text\": \"that said : if you can deal with the short battery life , you ' re not going to find a better machine at this price .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not a good quality laptop .\n->not a good quality laptop .\n[{'aspect': 'laptop', 'opinion': 'not a good', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: ( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n->( and I have eaten my share ) Which impresses me for having such a large amount of people to serve .\n[{'aspect': 'serve', 'opinion': 'impresses', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'serve', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the case is solid and attractive , the keyboard responsive and comfortable to use ( nice touch with the dedicated number keypad too ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe case is solid and attractive , the keyboard responsive and comfortable to use ( nice touch with the dedicated number keypad too ! )\n->", + "output": "{\"text\": \"the case is solid and attractive , the keyboard responsive and comfortable to use ( nice touch with the dedicated number keypad too ! )\", \"labels\": \"[{'aspect': 'case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'case', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'number keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is can became on e of the ny italian food fare institutions .\n->this is can became on e of the ny italian food fare institutions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it is an okay laptop and nothing more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is an okay laptop and nothing more .\n->", + "output": "{\"text\": \"it is an okay laptop and nothing more .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n->the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n[{'aspect': 'charging port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: beautiful resolution\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeautiful resolution\n->", + "output": "{\"text\": \"beautiful resolution\", \"labels\": \"[{'aspect': 'resolution', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: inexpensive , unassuming , great time !\n->inexpensive , unassuming , great time !\n[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it was nice when it was working .\n->it was nice when it was working .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: bigger screen\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbigger screen\n->", + "output": "{\"text\": \"bigger screen\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'bigger', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the service was a bit slow .\n->but the service was a bit slow .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: nice keyboard\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice keyboard\n->", + "output": "{\"text\": \"nice keyboard\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n->Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: definitely a steal at the price i bought this for .\n->definitely a steal at the price i bought this for .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the charge cord is very short , about 1 / 2 the size of a regular charging cord\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe charge cord is very short , about 1 / 2 the size of a regular charging cord\n->", + "output": "{\"text\": \"the charge cord is very short , about 1 / 2 the size of a regular charging cord\", \"labels\": \"[{'aspect': 'charge cord', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ' ll be there for every anniversary , birthday , valentines day . . .\n->you ' ll be there for every anniversary , birthday , valentines day . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the battery seriously lasts as long as i need it , and i ' m one to forget to charge it .\n->the battery seriously lasts as long as i need it , and i ' m one to forget to charge it .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the battery capacity is 3 hours on a full charge\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery capacity is 3 hours on a full charge\n->", + "output": "{\"text\": \"the battery capacity is 3 hours on a full charge\", \"labels\": \"[{'aspect': 'battery capacity', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so kudos to acer for the keyboard !\n->so kudos to acer for the keyboard !\n[{'aspect': 'keyboard', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'acer', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: but basic stuff needs to be properly engineered and designed , and this machine had two huge problems right out of the gate .\n->but basic stuff needs to be properly engineered and designed , and this machine had two huge problems right out of the gate .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the small battery capacity is the number one reason i would not buy this product and would recommend with that caveat being disclosed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe small battery capacity is the number one reason i would not buy this product and would recommend with that caveat being disclosed .\n->", + "output": "{\"text\": \"the small battery capacity is the number one reason i would not buy this product and would recommend with that caveat being disclosed .\", \"labels\": \"[{'aspect': 'battery capacity', 'opinion': 'small', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been beyond pleased with this laptop purchase over the last 6 months of use .\n->i have been beyond pleased with this laptop purchase over the last 6 months of use .\n[{'aspect': 'laptop', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: chrome extensions are great productivity tools to boot - they help me squeeze the most out of everyday .\n->chrome extensions are great productivity tools to boot - they help me squeeze the most out of everyday .\n[{'aspect': 'chrome extensions', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: great item !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat item !\n->", + "output": "{\"text\": \"great item !\", \"labels\": \"[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s absolutely silent .\n->it ' s absolutely silent .\n[{'aspect': 'NULL', 'opinion': 'silent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: service was quick .\n->service was quick .\n[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: laptop was new , in perfect condition and works like a charm .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop was new , in perfect condition and works like a charm .\n->", + "output": "{\"text\": \"laptop was new , in perfect condition and works like a charm .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am happy i did the food was awsome .\n->i am happy i did the food was awsome .\n[{'aspect': 'food', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Toons has recently been redone , so it 's now a very attractive space .\n->Toons has recently been redone , so it 's now a very attractive space .\n[{'aspect': 'space', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: mem , hdd , screed , dvd drive are all easily accessible and removable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmem , hdd , screed , dvd drive are all easily accessible and removable .\n->", + "output": "{\"text\": \"mem , hdd , screed , dvd drive are all easily accessible and removable .\", \"labels\": \"[{'aspect': 'mem', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'mem', 'opinion': 'removable', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'hdd', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'hdd', 'opinion': 'removable', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'screed', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screed', 'opinion': 'removable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'removable', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was the only thing good about this restaurant .\n->The service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: No free drink .\n->No free drink .\n[{'aspect': 'drink', 'opinion': 'No free', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: 720p screen that ' s not very bright .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n720p screen that ' s not very bright .\n->", + "output": "{\"text\": \"720p screen that ' s not very bright .\", \"labels\": \"[{'aspect': '720p screen', 'opinion': 'not very bright', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n->chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n[{'aspect': 'chromebooks', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n->everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n[{'aspect': 'atmosphere', 'opinion': 'raved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rooms', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'views', 'opinion': 'incomparable', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: short charging cable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshort charging cable .\n->", + "output": "{\"text\": \"short charging cable .\", \"labels\": \"[{'aspect': 'charging cable', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unhygienic\n->unhygienic\n[{'aspect': 'NULL', 'opinion': 'unhygienic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: an unexpected benefit for me has been the iphone / mbp integration .\n->an unexpected benefit for me has been the iphone / mbp integration .\n[{'aspect': 'iphone / mbp integration', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i ' m giving this five stars considering the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m giving this five stars considering the price .\n->", + "output": "{\"text\": \"i ' m giving this five stars considering the price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: terrible , terrible management - deserves to be shut - down .\n->terrible , terrible management - deserves to be shut - down .\n[{'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: lives up to the hype\n->lives up to the hype\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it can also run modded skyrim with no issues , which is nice bonus for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit can also run modded skyrim with no issues , which is nice bonus for me .\n->", + "output": "{\"text\": \"it can also run modded skyrim with no issues , which is nice bonus for me .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n->the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n[{'aspect': '1080p screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': '1080p screen', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'hinge', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: We love the food , drinks , and atmosphere !\n->We love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n->", + "output": "{\"text\": \"all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n->the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n[{'aspect': 'laptop', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'spec', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: great value for money , the notebook is as good as new .\n->great value for money , the notebook is as good as new .\n[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'notebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: lots of space , fast , and it will last a long time with ita top shelf technology .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlots of space , fast , and it will last a long time with ita top shelf technology .\n->", + "output": "{\"text\": \"lots of space , fast , and it will last a long time with ita top shelf technology .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is working really well .\n->it is working really well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i really liked that this chromebook came with 64gb of space but i really don ' t see how someone may fill that up unless they opt to sync their google drive offline or something .\n->i really liked that this chromebook came with 64gb of space but i really don ' t see how someone may fill that up unless they opt to sync their google drive offline or something .\n[{'aspect': 'chromebook', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: but overall a good laptop for productivity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut overall a good laptop for productivity .\n->", + "output": "{\"text\": \"but overall a good laptop for productivity .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food and the prices are very reasonable .\n->Great food and the prices are very reasonable .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Try the mediterranean salad , it is a true experience for your taste buds ! !\n->Try the mediterranean salad , it is a true experience for your taste buds ! !\n[{'aspect': 'mediterranean salad', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: good laptop thank you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood laptop thank you .\n->", + "output": "{\"text\": \"good laptop thank you .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is phenomenal again .\n->battery life is phenomenal again .\n[{'aspect': 'battery life', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is terrific , as is the value .\n->it is terrific , as is the value .\n[{'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: right out of the box , this computer is really slow , but two simple steps easily fix that issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nright out of the box , this computer is really slow , but two simple steps easily fix that issue .\n->", + "output": "{\"text\": \"right out of the box , this computer is really slow , but two simple steps easily fix that issue .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my husband and i have been sold on this from the first visit .\n->my husband and i have been sold on this from the first visit .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i ' m not picky - but it was actually gross .\n->i ' m not picky - but it was actually gross .\n[{'aspect': 'NULL', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: overall , its a decent machine for the money , and for $ 35 more for a cheap ssd , it really works great for a low buck budget friendly laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , its a decent machine for the money , and for $ 35 more for a cheap ssd , it really works great for a low buck budget friendly laptop .\n->", + "output": "{\"text\": \"overall , its a decent machine for the money , and for $ 35 more for a cheap ssd , it really works great for a low buck budget friendly laptop .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n->If you do n't mind pre-sliced low quality fish , unfriendly staff and a sushi chef that looks like he is miserable then this is your place .\n[{'aspect': 'fish', 'opinion': 'low quality', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'unfriendly', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi chef', 'opinion': 'miserable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: love the price reduction and the lenova one year warranty .\n->love the price reduction and the lenova one year warranty .\n[{'aspect': 'lenova one year warranty', 'opinion': 'love', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: my girlfriend works from home with it and has no problems at all to do online classes with it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy girlfriend works from home with it and has no problems at all to do online classes with it .\n->", + "output": "{\"text\": \"my girlfriend works from home with it and has no problems at all to do online classes with it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes had to do several times , but thought it might be an idiosyncrady of this model .\n->sometimes had to do several times , but thought it might be an idiosyncrady of this model .\n[{'aspect': 'model', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: note that this laptop is only 45 daysish old .\n->note that this laptop is only 45 daysish old .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: decent laptop for home use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndecent laptop for home use .\n->", + "output": "{\"text\": \"decent laptop for home use .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re considering a switch from windows based pcs to apple computers , i ' d probably recommend that you look at what you ' re going to use it for .\n->if you ' re considering a switch from windows based pcs to apple computers , i ' d probably recommend that you look at what you ' re going to use it for .\n[{'aspect': 'apple computers', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the price is right too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe price is right too .\n->", + "output": "{\"text\": \"the price is right too .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'right', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Oh , do n't even let me start with how expensive the bills were !\n->Oh , do n't even let me start with how expensive the bills were !\n[{'aspect': 'bills', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n->in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great value , i wanted a laptop for my personal home use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat value , i wanted a laptop for my personal home use .\n->", + "output": "{\"text\": \"great value , i wanted a laptop for my personal home use .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n->everytime i decide to try another place on the ues , i get angry that i did n ' t just go to zucchero pomodori .\n[{'aspect': 'zucchero pomodori', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Finally a reliable Chinese restaurant !\n->Finally a reliable Chinese restaurant !\n[{'aspect': 'Chinese restaurant', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n->", + "output": "{\"text\": \"this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'computer replacement', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was attentive , yet discreet .\n->The service was attentive , yet discreet .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'discreet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: its battery life is really good , and the led lights are nice .\n->its battery life is really good , and the led lights are nice .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'led lights', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: great product and price\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat product and price\n->", + "output": "{\"text\": \"great product and price\", \"labels\": \"[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was also very good .\n->Service was also very good .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The buffet had a nice selection .\n->The buffet had a nice selection .\n[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this laptop is great for a lot of modern games .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is great for a lot of modern games .\n->", + "output": "{\"text\": \"this laptop is great for a lot of modern games .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n->Excellent atmosphere , delicious dishes good and friendly service .\n[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Pizza was a little soggy .\n->Pizza was a little soggy .\n[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: bought this for web surfing at home .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought this for web surfing at home .\n->", + "output": "{\"text\": \"bought this for web surfing at home .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n->i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n[{'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the ssd is not work after 4 month\n->the ssd is not work after 4 month\n[{'aspect': 'ssd', 'opinion': 'not work', 'polarity': 'negative', 'category': 'HARD_DISC#QUALITY'}]\ntext: price is good\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprice is good\n->", + "output": "{\"text\": \"price is good\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n->downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n[{'aspect': 'downloading android apps', 'opinion': 'easy', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: waitstaff are very friendly .\n->waitstaff are very friendly .\n[{'aspect': 'waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the large screen and integral numeric pad are exactly what i need to easily process documents .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe large screen and integral numeric pad are exactly what i need to easily process documents .\n->", + "output": "{\"text\": \"the large screen and integral numeric pad are exactly what i need to easily process documents .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'integral numeric pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my only other complaint is trackpad sensitivity .\n->my only other complaint is trackpad sensitivity .\n[{'aspect': 'trackpad sensitivity', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: she then put the check down without asking if we were done , and came to check on the bill every two minutes , even though we were one of three occupied tables .\n->she then put the check down without asking if we were done , and came to check on the bill every two minutes , even though we were one of three occupied tables .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: highly recommend , great value , use computer mainly for online business and is quick and easy to use\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly recommend , great value , use computer mainly for online business and is quick and easy to use\n->", + "output": "{\"text\": \"highly recommend , great value , use computer mainly for online business and is quick and easy to use\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n->some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n[{'aspect': 'NULL', 'opinion': 'respectable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'builds', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard dock', 'opinion': 'superior', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: LOVE THIS PLACE .\n->LOVE THIS PLACE .\n[{'aspect': 'PLACE', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ordered this computer to use in college and also for gaming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nordered this computer to use in college and also for gaming .\n->", + "output": "{\"text\": \"ordered this computer to use in college and also for gaming .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: When we sat , we got great and fast service .\n->When we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the price very reasonable .\n->the price very reasonable .\n[{'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n->", + "output": "{\"text\": \"although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most of the booths allow you to sit next to eachother without looking like ' that ' couple .\n->most of the booths allow you to sit next to eachother without looking like ' that ' couple .\n[{'aspect': 'booths', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: works well .\n->works well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the computer looks nice , and works good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer looks nice , and works good .\n->", + "output": "{\"text\": \"the computer looks nice , and works good .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagel was huge .\n->The bagel was huge .\n[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The staff was knowledgeable and full of personality .\n->The staff was knowledgeable and full of personality .\n[{'aspect': 'staff', 'opinion': 'knowledgeable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love love love this laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove love love this laptop !\n->", + "output": "{\"text\": \"love love love this laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchscreen functions very well , both in laptop and tablet mode , and the trackpad and keyboard are enjoyable to use .\n->the touchscreen functions very well , both in laptop and tablet mode , and the trackpad and keyboard are enjoyable to use .\n[{'aspect': 'touchscreen', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: first one i received , the space bar got stuck and returned it for a replacement .\n->first one i received , the space bar got stuck and returned it for a replacement .\n[{'aspect': 'space bar', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: what a tragic mistake buying a computer with windows 10 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat a tragic mistake buying a computer with windows 10 !\n->", + "output": "{\"text\": \"what a tragic mistake buying a computer with windows 10 !\", \"labels\": \"[{'aspect': 'computer with windows 10', 'opinion': 'tragic', 'polarity': 'negative', 'category': 'OS#GENERAL'}, {'aspect': 'computer with windows 10', 'opinion': 'tragic', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n->i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n[{'aspect': 'device', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: the only beverage we did receive was water in dirty glasses !\n->the only beverage we did receive was water in dirty glasses !\n[{'aspect': 'NULL', 'opinion': 'dirty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: great laptop great price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat laptop great price .\n->", + "output": "{\"text\": \"great laptop great price .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wifi card recently died after 14 months .\n->the wifi card recently died after 14 months .\n[{'aspect': 'wifi card', 'opinion': 'died', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i loved it and would highly recommend .\n->i loved it and would highly recommend .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: great customer service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat customer service .\n->", + "output": "{\"text\": \"great customer service .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the spicy tuna roll is huge and probably the best that i ' ve had at this price range .\n->the spicy tuna roll is huge and probably the best that i ' ve had at this price range .\n[{'aspect': 'spicy tuna roll', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'spicy tuna roll', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spicy tuna roll', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i have never been so disgusted by both food an service .\n->i have never been so disgusted by both food an service .\n[{'aspect': 'food', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: good computer good memory\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood computer good memory\n->", + "output": "{\"text\": \"good computer good memory\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n->Ess-A-Bagel ( either by Sty-town or midtown ) is by far the best bagel in NY .\n[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - the backlit keyboard looks nice .\n->- the backlit keyboard looks nice .\n[{'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: amazing laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazing laptop !\n->", + "output": "{\"text\": \"amazing laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will see if samsung honors their warrnty , if so , i will probably change / update this review .\n->i will see if samsung honors their warrnty , if so , i will probably change / update this review .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n->even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'working unit', 'opinion': 'outweighs', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this laptop is the best of both worlds\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is the best of both worlds\n->", + "output": "{\"text\": \"this laptop is the best of both worlds\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Overall , excellent restaurant !\n->Overall , excellent restaurant !\n[{'aspect': 'restaurant', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after the 4th time i asked again and the waiter than said after our dinner .\n->after the 4th time i asked again and the waiter than said after our dinner .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: not bad for the a basic betty or plain jane laptop\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot bad for the a basic betty or plain jane laptop\n->", + "output": "{\"text\": \"not bad for the a basic betty or plain jane laptop\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'not bad', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n->The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The $ 300 bill was a bit steep , but the experience was great .\n->The $ 300 bill was a bit steep , but the experience was great .\n[{'aspect': 'bill', 'opinion': 'steep', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i only wish this laptop had a removable battery other than that it ' s great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni only wish this laptop had a removable battery other than that it ' s great .\n->", + "output": "{\"text\": \"i only wish this laptop had a removable battery other than that it ' s great .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would go back .\n->i would go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the build quality is cheap .\n->the build quality is cheap .\n[{'aspect': 'build quality', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: ( not amazing graphics at those settings )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( not amazing graphics at those settings )\n->", + "output": "{\"text\": \"( not amazing graphics at those settings )\", \"labels\": \"[{'aspect': 'graphics', 'opinion': 'not amazing', 'polarity': 'neutral', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: an excellent service\n->an excellent service\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: red dragon roll - my favorite thing to eat , of any food group - hands down\n->red dragon roll - my favorite thing to eat , of any food group - hands down\n[{'aspect': 'red dragon roll', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: solid inexpensive computer for our 10 year old\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsolid inexpensive computer for our 10 year old\n->", + "output": "{\"text\": \"solid inexpensive computer for our 10 year old\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was very disappointed with this restaurant .\n->i was very disappointed with this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n->small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: very happy with my purchase , fast delivery , package well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery happy with my purchase , fast delivery , package well .\n->", + "output": "{\"text\": \"very happy with my purchase , fast delivery , package well .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'package', 'opinion': 'well', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Spreads and toppings are great - though a bit pricey .\n->Spreads and toppings are great - though a bit pricey .\n[{'aspect': 'Spreads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Spreads', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: samsung rma ' d the device to replace the screen .\n->samsung rma ' d the device to replace the screen .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: great little laptop for the money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat little laptop for the money .\n->", + "output": "{\"text\": \"great little laptop for the money .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n->In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: good drink .\n->good drink .\n[{'aspect': 'drink', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: good laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood laptop .\n->", + "output": "{\"text\": \"good laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n->by the time we left our wallets were empty and so were our stomachs and we missed the show we were supposed to see following our dinner , which would have been acceptable if we got to enjoy the experience of good food and belly dancers !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: despite the minor case marks , i can heartily recommend this chromebook based on features , design , and operation .\n->despite the minor case marks , i can heartily recommend this chromebook based on features , design , and operation .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: you just have to deal with a low battery and that ' s all\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou just have to deal with a low battery and that ' s all\n->", + "output": "{\"text\": \"you just have to deal with a low battery and that ' s all\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: netflix and hulu seem to be working for the most part so far , but amazon prime and xfinity stream are both having issues .\n->netflix and hulu seem to be working for the most part so far , but amazon prime and xfinity stream are both having issues .\n[{'aspect': 'netflix', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'hulu seem', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'amazon prime', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'xfinity stream', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i find it to be a little large when used in tablet mode .\n->i find it to be a little large when used in tablet mode .\n[{'aspect': 'tablet mode', 'opinion': 'large', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: it is a little slow at times though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a little slow at times though .\n->", + "output": "{\"text\": \"it is a little slow at times though .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pro is by far the best .\n->the pro is by far the best .\n[{'aspect': 'pro', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n->it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n[{'aspect': 'customer service and support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\ntext: great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n->", + "output": "{\"text\": \"great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no . . .\n->the waitress moved our table practically into the bathroom and when we asked to cancel our dinner orders because we did not want to eat sitting on the toilet , we were told no . . .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n->The menu is very limited - i think we counted 4 or 5 entrees .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this has worked great to overcome that problem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis has worked great to overcome that problem .\n->", + "output": "{\"text\": \"this has worked great to overcome that problem .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n->Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n[{'aspect': 'candle-light', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'closely situated', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the notebook is decent .\n->the notebook is decent .\n[{'aspect': 'notebook', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great amount of memory for a small business .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat amount of memory for a small business .\n->", + "output": "{\"text\": \"great amount of memory for a small business .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'great', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff was the friendliest that have seen in new york .\n->the staff was the friendliest that have seen in new york .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Judging from previous posts this used to be a good place , but not any longer .\n->Judging from previous posts this used to be a good place , but not any longer .\n[{'aspect': 'place', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: we are very happy with the lenovo laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe are very happy with the lenovo laptop .\n->", + "output": "{\"text\": \"we are very happy with the lenovo laptop .\", \"labels\": \"[{'aspect': 'lenovo laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard and os takes some getting used to .\n->the keyboard and os takes some getting used to .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\nExample:\ntext: My wife had the fried shrimp which are huge and loved it .\n->My wife had the fried shrimp which are huge and loved it .\n[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s very easy to set up and use\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s very easy to set up and use\n->", + "output": "{\"text\": \"it ' s very easy to set up and use\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not a good quality laptop .\n->not a good quality laptop .\n[{'aspect': 'laptop', 'opinion': 'not a good', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: Our son loves pizza and we have a certified Neapolitan pizzaria in our home city ( Seattle ) , we liked this nearly as much - and the differences were more about personal preference than any reflection on either restaurant .\n->Our son loves pizza and we have a certified Neapolitan pizzaria in our home city ( Seattle ) , we liked this nearly as much - and the differences were more about personal preference than any reflection on either restaurant .\n[{'aspect': 'pizza', 'opinion': 'loves', 'polarity': 'positive', 'category': 'NULL'}]\ntext: once i got it set up it has been very nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonce i got it set up it has been very nice .\n->", + "output": "{\"text\": \"once i got it set up it has been very nice .\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazing fresh dogs but best of all endless toppings ! ! !\n->amazing fresh dogs but best of all endless toppings ! ! !\n[{'aspect': 'dogs', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dogs', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'toppings', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'toppings', 'opinion': 'endless', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: The fried rice is amazing here .\n->The fried rice is amazing here .\n[{'aspect': 'fried rice', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: noisy hdr , better with ssd ) works quickly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnoisy hdr , better with ssd ) works quickly .\n->", + "output": "{\"text\": \"noisy hdr , better with ssd ) works quickly .\", \"labels\": \"[{'aspect': 'hdr', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}, {'aspect': 'ssd', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also ordered the change mojito , which was out of this world .\n->i also ordered the change mojito , which was out of this world .\n[{'aspect': 'change mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: so far so good , good quality , plenty fast enough for streaming and browsing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far so good , good quality , plenty fast enough for streaming and browsing .\n->", + "output": "{\"text\": \"so far so good , good quality , plenty fast enough for streaming and browsing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was well prepared and the service impecable .\n->The food was well prepared and the service impecable .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: sometimes i get bad food and bad service , sometimes i get good good and bad service .\n->sometimes i get bad food and bad service , sometimes i get good good and bad service .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'good', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: my dad says it works extremely well !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy dad says it works extremely well !\n->", + "output": "{\"text\": \"my dad says it works extremely well !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would never wait for a table to eat , it just is not THAT great .\n->I would never wait for a table to eat , it just is not THAT great .\n[{'aspect': 'table', 'opinion': 'never wait', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n->I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n[{'aspect': 'meal', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'nice', 'polarity': 'negative', 'category': 'NULL'}]\ntext: love this computer !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove this computer !\n->", + "output": "{\"text\": \"love this computer !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had this laptop repaired within the first 6 months of owning it .\n->i had this laptop repaired within the first 6 months of owning it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Try green curry with vegetables .\n->Try green curry with vegetables .\n[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love my new lenovo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove my new lenovo .\n->", + "output": "{\"text\": \"love my new lenovo .\", \"labels\": \"[{'aspect': 'lenovo', 'opinion': 'love', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n->The service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hot sauce', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n->Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'raw vegatables in side orders', 'opinion': 'wondered about freshmess', 'polarity': 'negative', 'category': 'NULL'}]\ntext: pretty fast processor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npretty fast processor .\n->", + "output": "{\"text\": \"pretty fast processor .\", \"labels\": \"[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: occasionally it will get sort of slow and then i ' ll go to settings and clear the image cache and that takes care of it .\n->occasionally it will get sort of slow and then i ' ll go to settings and clear the image cache and that takes care of it .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: less than 90 days and the screen stopped working .\n->less than 90 days and the screen stopped working .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: excellent lapto , just as they show it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent lapto , just as they show it .\n->", + "output": "{\"text\": \"excellent lapto , just as they show it .\", \"labels\": \"[{'aspect': 'lapto', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: - boot time , sleep time and wake time are crazy fast .\n->- boot time , sleep time and wake time are crazy fast .\n[{'aspect': 'boot time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'boot time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\ntext: very good laptop for gamers and editing videos , love it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good laptop for gamers and editing videos , love it\n->", + "output": "{\"text\": \"very good laptop for gamers and editing videos , love it\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - track pad is accurate\n->- track pad is accurate\n[{'aspect': 'track pad', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Be sure to try the seasonal , and always delicious , specials .\n->Be sure to try the seasonal , and always delicious , specials .\n[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is very good to use in korea .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is very good to use in korea .\n->", + "output": "{\"text\": \"it is very good to use in korea .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food !\n->great food !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: This is the BEST Shabu-Shabu Restaurant in the Try-State Area .\n->This is the BEST Shabu-Shabu Restaurant in the Try-State Area .\n[{'aspect': 'Shabu-Shabu Restaurant', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\ntext: good price , good quality and good service in korea .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood price , good quality and good service in korea .\n->", + "output": "{\"text\": \"good price , good quality and good service in korea .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would buy again , especially at this price .\n->i would buy again , especially at this price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n->on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: love it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove it !\n->", + "output": "{\"text\": \"love it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the 15 ` ` screen is big and nice , this will make my examinations go much faster .\n->the 15 ` ` screen is big and nice , this will make my examinations go much faster .\n[{'aspect': 'NULL', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'faster', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: nice laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice laptop !\n->", + "output": "{\"text\": \"nice laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n->this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n[{'aspect': 'trattoria', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'trattoria', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: they both pick up oils and such pretty easily .\n->they both pick up oils and such pretty easily .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the best laptop for its price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe best laptop for its price .\n->", + "output": "{\"text\": \"the best laptop for its price .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: check this place out !\n->check this place out !\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I highly recommend to anyone to give this place a try .\n->I highly recommend to anyone to give this place a try .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i recommend it , definitely\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recommend it , definitely\n->", + "output": "{\"text\": \"i recommend it , definitely\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portions are small but being that the food was so good makes up for that .\n->The portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n->quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n[{'aspect': 'NULL', 'opinion': 'tranquility', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: it ' s exactly as i wanted it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s exactly as i wanted it\n->", + "output": "{\"text\": \"it ' s exactly as i wanted it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n->received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it 's the only place you can get yummy authentic japanese comfort food .\n->it 's the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i like it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like it\n->", + "output": "{\"text\": \"i like it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the actual laptop is very much darker and blue .\n->the actual laptop is very much darker and blue .\n[{'aspect': 'actual laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n->received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\ntext: this is the most well priced laptop for its spec\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the most well priced laptop for its spec\n->", + "output": "{\"text\": \"this is the most well priced laptop for its spec\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'spec', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing i moderately enjoyed was their grilled chicken special with edamame puree .\n->the only thing i moderately enjoyed was their grilled chicken special with edamame puree .\n[{'aspect': 'grilled chicken special with edamame puree', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: but , it was packed well and arrived with no damage whatsoever !\n->but , it was packed well and arrived with no damage whatsoever !\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\ntext: very good laptop\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good laptop\n->", + "output": "{\"text\": \"very good laptop\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was ok .\n->food was ok .\n[{'aspect': 'food', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Please try the Filet Mignon , its just the most tender piece ever .\n->Please try the Filet Mignon , its just the most tender piece ever .\n[{'aspect': 'Filet Mignon', 'opinion': 'tender', 'polarity': 'positive', 'category': 'NULL'}]\ntext: lenovo computers are of chinese manufacture and thus you will not know if yours is one of the pla ' s cyber - spies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlenovo computers are of chinese manufacture and thus you will not know if yours is one of the pla ' s cyber - spies .\n->", + "output": "{\"text\": \"lenovo computers are of chinese manufacture and thus you will not know if yours is one of the pla ' s cyber - spies .\", \"labels\": \"[{'aspect': 'lenovo computers', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good quality .\n->good quality .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i ' m not sure when this feature was introduced , but it is very welcome .\n->i ' m not sure when this feature was introduced , but it is very welcome .\n[{'aspect': 'feature', 'opinion': 'welcome', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: battery life short\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life short\n->", + "output": "{\"text\": \"battery life short\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all in all , i would return - as it was a beautiful restaurant - but i hope the staff pays more attention to the little details in the future .\n->all in all , i would return - as it was a beautiful restaurant - but i hope the staff pays more attention to the little details in the future .\n[{'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n->i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n[{'aspect': 'usb - c charger', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n->", + "output": "{\"text\": \"i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a chrome book it is excellent , but android support is unsatisfying .\n->as a chrome book it is excellent , but android support is unsatisfying .\n[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'android support', 'opinion': 'unsatisfying', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: was installing an update and the computer went black .\n->was installing an update and the computer went black .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: only issue is that the graphics aren ' t quite as good as i expected , but i didn ' t buy this to use as a gaming pc so i ' m not overly concerned about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly issue is that the graphics aren ' t quite as good as i expected , but i didn ' t buy this to use as a gaming pc so i ' m not overly concerned about it .\n->", + "output": "{\"text\": \"only issue is that the graphics aren ' t quite as good as i expected , but i didn ' t buy this to use as a gaming pc so i ' m not overly concerned about it .\", \"labels\": \"[{'aspect': 'graphics', 'opinion': \"' t quite as good as\", 'polarity': 'neutral', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i must say i am surprised by the bad reviews of the restaurant earlier in the year , though .\n->i must say i am surprised by the bad reviews of the restaurant earlier in the year , though .\n[{'aspect': 'restaurant', 'opinion': 'bad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: noodle pudding is exactly the type of service and food i enjoy .\n->noodle pudding is exactly the type of service and food i enjoy .\n[{'aspect': 'service', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this is a great laptop for web browsing , skype , and more simple games .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great laptop for web browsing , skype , and more simple games .\n->", + "output": "{\"text\": \"this is a great laptop for web browsing , skype , and more simple games .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .\n->the large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .\n[{'aspect': 'bruschettas', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'paninis', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'tramezzinis', 'opinion': 'large selection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n->We ate out in the back patio , which is worth it as it 's cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\ntext: photoshop also runs very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nphotoshop also runs very well .\n->", + "output": "{\"text\": \"photoshop also runs very well .\", \"labels\": \"[{'aspect': 'photoshop', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works like it is made to run linux .\n->it works like it is made to run linux .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it started to get slow a week ago .\n->it started to get slow a week ago .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i would definitely recommend this laptop to anyone who is looking for a cheap and nice computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would definitely recommend this laptop to anyone who is looking for a cheap and nice computer .\n->", + "output": "{\"text\": \"i would definitely recommend this laptop to anyone who is looking for a cheap and nice computer .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n->graphic issues : after opening any page or window with a black or dark color and i go back to a more bright or white page it takes at least 5 good second to gain the full luminosity\n[{'aspect': 'graphic', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\nExample:\ntext: Okay service .\n->Okay service .\n[{'aspect': 'service', 'opinion': 'Okay', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove it .\n->", + "output": "{\"text\": \"love it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s somewhere you can eat and be happy .\n->it ' s somewhere you can eat and be happy .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: everyone was smiling so that made me feel welcome .\n->everyone was smiling so that made me feel welcome .\n[{'aspect': 'NULL', 'opinion': 'welcome', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: very happy with it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery happy with it .\n->", + "output": "{\"text\": \"very happy with it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only real upgrade for the new one , before adding on options is faster memory .\n->the only real upgrade for the new one , before adding on options is faster memory .\n[{'aspect': 'memory', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n->looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\ntext: excellent computer , better than expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent computer , better than expected .\n->", + "output": "{\"text\": \"excellent computer , better than expected .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: youtube works well on here .\n->youtube works well on here .\n[{'aspect': 'youtube', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: then tonight ( april 29th ) my daughter says it ' s not charging .\n->then tonight ( april 29th ) my daughter says it ' s not charging .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: great service with amazon on fulfilling my order .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat service with amazon on fulfilling my order .\n->", + "output": "{\"text\": \"great service with amazon on fulfilling my order .\", \"labels\": \"[{'aspect': 'service with amazon', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery life is terrible .\n->the battery life is terrible .\n[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: touch screen and zoom is great .\n->touch screen and zoom is great .\n[{'aspect': 'touch screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'zoom', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\ntext: i am truly enjoying my laptop after one month .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am truly enjoying my laptop after one month .\n->", + "output": "{\"text\": \"i am truly enjoying my laptop after one month .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n->The portions are HUGE , so it might be good to order three things to split ( rather than one appetizer and entree per person ) for two people .\n[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Food and service was okay .\n->Food and service was okay .\n[{'aspect': 'Food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i use it for streaming with the elgato device and it doesn ' t miss a beat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use it for streaming with the elgato device and it doesn ' t miss a beat .\n->", + "output": "{\"text\": \"i use it for streaming with the elgato device and it doesn ' t miss a beat .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: next , is that the track pad is insanely wobbly .\n->next , is that the track pad is insanely wobbly .\n[{'aspect': 'track pad', 'opinion': 'wobbly', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The server was really cool and served us our food and drinks with a smile .\n->The server was really cool and served us our food and drinks with a smile .\n[{'aspect': 'server', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you can ' t beat the price for what you are getting with this computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can ' t beat the price for what you are getting with this computer .\n->", + "output": "{\"text\": \"you can ' t beat the price for what you are getting with this computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the cable functions just fine .\n->the cable functions just fine .\n[{'aspect': 'cable', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: While the $ 20 entree range is not overly expensive , in New York City , there is definitely better food in that range , and so Sapphire , despite it 's lovely atmosphere , will most likely not be a restaurant to which I will return .\n->While the $ 20 entree range is not overly expensive , in New York City , there is definitely better food in that range , and so Sapphire , despite it 's lovely atmosphere , will most likely not be a restaurant to which I will return .\n[{'aspect': 'food', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entree range', 'opinion': 'not overly expensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love the graphics when i replay my videos or watch other streamers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the graphics when i replay my videos or watch other streamers .\n->", + "output": "{\"text\": \"love the graphics when i replay my videos or watch other streamers .\", \"labels\": \"[{'aspect': 'graphics', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n->it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: what this tells me is that the hdmi port on my chromebook is defective .\n->what this tells me is that the hdmi port on my chromebook is defective .\n[{'aspect': 'hdmi port on my chromebook', 'opinion': 'defective', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: if you need a decent computer that runs quality this is it , especially if you are starting out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you need a decent computer that runs quality this is it , especially if you are starting out .\n->", + "output": "{\"text\": \"if you need a decent computer that runs quality this is it , especially if you are starting out .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n->received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n[{'aspect': 'cpu', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: and because of the constant usage of higher brightness , the battery does drain faster .\n->and because of the constant usage of higher brightness , the battery does drain faster .\n[{'aspect': 'battery', 'opinion': 'faster', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the fans did not turn on loudly if at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fans did not turn on loudly if at all .\n->", + "output": "{\"text\": \"the fans did not turn on loudly if at all .\", \"labels\": \"[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even my indian friend could n ' t believe how good and tasty everything was .\n->even my indian friend could n ' t believe how good and tasty everything was .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the 302 would be just about the perfect chromebook if it had smaller bezels , though .\n->the 302 would be just about the perfect chromebook if it had smaller bezels , though .\n[{'aspect': '302', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: purchased as a mothers day gift but i ' ve come to respect the quality and performance of lenovo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npurchased as a mothers day gift but i ' ve come to respect the quality and performance of lenovo .\n->", + "output": "{\"text\": \"purchased as a mothers day gift but i ' ve come to respect the quality and performance of lenovo .\", \"labels\": \"[{'aspect': 'lenovo', 'opinion': 'respect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'lenovo', 'opinion': 'respect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the real problem i had with this place was the complete lack of service .\n->the real problem i had with this place was the complete lack of service .\n[{'aspect': 'service', 'opinion': 'lack of', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n->certain apps ( especially flash based apps ) will get the machine very hot .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n->", + "output": "{\"text\": \"i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 1st , i was shocked at how easy it was to set up .\n->1st , i was shocked at how easy it was to set up .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: i have been coming here for years and have nothing but good things to say about the service and the great staff at la lanterna .\n->i have been coming here for years and have nothing but good things to say about the service and the great staff at la lanterna .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: fast delivery , thanks to amazon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast delivery , thanks to amazon .\n->", + "output": "{\"text\": \"fast delivery , thanks to amazon .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n->Raga stands out with an interesting fusion of French and Indian cooking .\n[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Such nice people working here - but I have to review the food .\n->Such nice people working here - but I have to review the food .\n[{'aspect': 'people', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: excellent price , i bought it for a beginner in art design field .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent price , i bought it for a beginner in art design field .\n->", + "output": "{\"text\": \"excellent price , i bought it for a beginner in art design field .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then it would not boot up .\n->then it would not boot up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: outside of that , the keyboard is solid , the back lighting was not a selling point to me .\n->outside of that , the keyboard is solid , the back lighting was not a selling point to me .\n[{'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'back lighting', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: it ' s been about 3 weeks since my purchase of my lenova laptop and figure it ' s time to give my all important review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s been about 3 weeks since my purchase of my lenova laptop and figure it ' s time to give my all important review .\n->", + "output": "{\"text\": \"it ' s been about 3 weeks since my purchase of my lenova laptop and figure it ' s time to give my all important review .\", \"labels\": \"[{'aspect': 'lenova laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s pretty fast even with heavy use and multiple applications running at once .\n->it ' s pretty fast even with heavy use and multiple applications running at once .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'secret back room', 'opinion': 'Check out', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love the price reduction and the lenova one year warranty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the price reduction and the lenova one year warranty .\n->", + "output": "{\"text\": \"love the price reduction and the lenova one year warranty .\", \"labels\": \"[{'aspect': 'lenova one year warranty', 'opinion': 'love', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was using it and the screen started to flicker and then went completely dim .\n->i was using it and the screen started to flicker and then went completely dim .\n[{'aspect': 'screen', 'opinion': 'flicker', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n->immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: pros : nice size , clear screen , quick on start up , very functional and easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npros : nice size , clear screen , quick on start up , very functional and easy to use .\n->", + "output": "{\"text\": \"pros : nice size , clear screen , quick on start up , very functional and easy to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n->My wife and I also enjoyed the spinach , the Shanghai low mein , and other attractions .\n[{'aspect': 'spinach', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shanghai low mein', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - ssd ( solid state drive ) is not easily upgradable .\n->- ssd ( solid state drive ) is not easily upgradable .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\ntext: it ' s also very lightweight , so it ' s easy to carry around .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s also very lightweight , so it ' s easy to carry around .\n->", + "output": "{\"text\": \"it ' s also very lightweight , so it ' s easy to carry around .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: No food snobs allowed , this place is for people who appreciate good food .\n->No food snobs allowed , this place is for people who appreciate good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Someone else recommended the dessert - we also left that .\n->Someone else recommended the dessert - we also left that .\n[{'aspect': 'dessert', 'opinion': 'recommended', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i love the fact that it can extend and be flat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love the fact that it can extend and be flat .\n->", + "output": "{\"text\": \"i love the fact that it can extend and be flat .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Normally that would be improper , however they were all delicious and my host did not complain .\n->Normally that would be improper , however they were all delicious and my host did not complain .\n[{'aspect': 'host', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Ambiance is barely romantic but management tries .\n->Ambiance is barely romantic but management tries .\n[{'aspect': 'Ambiance', 'opinion': 'barely romantic', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'management', 'opinion': 'tries', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the keyboard is a nice size and the pads clicks on touch and not stiff or hard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is a nice size and the pads clicks on touch and not stiff or hard .\n->", + "output": "{\"text\": \"the keyboard is a nice size and the pads clicks on touch and not stiff or hard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Indoor was very cozy and cute .\n->Indoor was very cozy and cute .\n[{'aspect': 'Indoor', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Indoor', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: as many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .\n->as many other reviewers noticed , your order is often slow to arrive - this is particularly true in the evening but is not a problem during lunch time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: it also has a lot of space and memory .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit also has a lot of space and memory .\n->", + "output": "{\"text\": \"it also has a lot of space and memory .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there are no negatives to speak of .\n->there are no negatives to speak of .\n[{'aspect': 'NULL', 'opinion': 'no negatives', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: display is ok not great .\n->display is ok not great .\n[{'aspect': 'display', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\ntext: cons : i wished it had a backlight on the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncons : i wished it had a backlight on the keyboard .\n->", + "output": "{\"text\": \"cons : i wished it had a backlight on the keyboard .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: great indian food and the service is incredible .\n->great indian food and the service is incredible .\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the battery life sucks as it starts to die after 3 - 4 hours of use ( no gaming ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life sucks as it starts to die after 3 - 4 hours of use ( no gaming ) .\n->", + "output": "{\"text\": \"the battery life sucks as it starts to die after 3 - 4 hours of use ( no gaming ) .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was fully charged and i only turned it on a total of 3 times before the screen went blank .\n->it was fully charged and i only turned it on a total of 3 times before the screen went blank .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'the screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n->i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\ntext: lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n->", + "output": "{\"text\": \"lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\", \"labels\": \"[{'aspect': 'touch pad', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n->very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n[{'aspect': 'sound volume', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: the thing that made me return it was the trackpad .\n->the thing that made me return it was the trackpad .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: after reading a lot of the reviews on here , i was unsure about laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter reading a lot of the reviews on here , i was unsure about laptop .\n->", + "output": "{\"text\": \"after reading a lot of the reviews on here , i was unsure about laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was authentic .\n->The food was authentic .\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was able to download all of my games in a quick amount of time too .\n->i was able to download all of my games in a quick amount of time too .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: only con would be that display is not that bright , although i would say at the brightest setting is probably where it should be .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly con would be that display is not that bright , although i would say at the brightest setting is probably where it should be .\n->", + "output": "{\"text\": \"only con would be that display is not that bright , although i would say at the brightest setting is probably where it should be .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'con', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n->the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n[{'aspect': 'touch screen', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: package arrived faster than the estimated arrival .\n->package arrived faster than the estimated arrival .\n[{'aspect': 'package', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\ntext: also audio is pretty decent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso audio is pretty decent .\n->", + "output": "{\"text\": \"also audio is pretty decent .\", \"labels\": \"[{'aspect': 'audio', 'opinion': 'decent', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: losing the function keys for a toucher was a deal breaker .\n->losing the function keys for a toucher was a deal breaker .\n[{'aspect': 'function keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n->the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'plus', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: lenovo should put a better battery in it , and should make a retrofit available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlenovo should put a better battery in it , and should make a retrofit available .\n->", + "output": "{\"text\": \"lenovo should put a better battery in it , and should make a retrofit available .\", \"labels\": \"[{'aspect': 'better', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is the best chromebook that i have ever used .\n->it is the best chromebook that i have ever used .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n->the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: but if you keep it plugged in it ' s great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut if you keep it plugged in it ' s great !\n->", + "output": "{\"text\": \"but if you keep it plugged in it ' s great !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was pretty tradional but it was hot and good with large portions .\n->The food was pretty tradional but it was hot and good with large portions .\n[{'aspect': 'food', 'opinion': 'tradional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: so delicious ! ! ! ! ! !\n->so delicious ! ! ! ! ! !\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: lenovo paid for my shipping to the repair facility since i ' d had it such a short time , and they fixed and returned it in a reasonable time period .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlenovo paid for my shipping to the repair facility since i ' d had it such a short time , and they fixed and returned it in a reasonable time period .\n->", + "output": "{\"text\": \"lenovo paid for my shipping to the repair facility since i ' d had it such a short time , and they fixed and returned it in a reasonable time period .\", \"labels\": \"[{'aspect': 'lenovo', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The waitress suggested glasses of wine that went very well with the food .\n->The waitress suggested glasses of wine that went very well with the food .\n[{'aspect': 'glasses of wine', 'opinion': 'went very well with the food', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: only thing is i am not sure if there is a delete key , something i use a lot\n->only thing is i am not sure if there is a delete key , something i use a lot\n[{'aspect': 'delete key', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave not tried customer service so no comment , but this is a nice sub $ 400 machine .\n->", + "output": "{\"text\": \"have not tried customer service so no comment , but this is a nice sub $ 400 machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have recommended the place to friends , always gets good response .\n->have recommended the place to friends , always gets good response .\n[{'aspect': 'place', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the build quality is great .\n->the build quality is great .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: i love this laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this laptop !\n->", + "output": "{\"text\": \"i love this laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great , service is ok .\n->The food is great , service is ok .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: it is fantastic for the things that i need a computer for .\n->it is fantastic for the things that i need a computer for .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it works like it is made to run linux .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit works like it is made to run linux .\n->", + "output": "{\"text\": \"it works like it is made to run linux .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touch screen broke four months after i purchased it .\n->the touch screen broke four months after i purchased it .\n[{'aspect': 'touch screen', 'opinion': 'broke', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: for the price i think it ' s just fine .\n->for the price i think it ' s just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the only real complain is the same one everyone else has with this model and that is the battery life could be better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only real complain is the same one everyone else has with this model and that is the battery life could be better .\n->", + "output": "{\"text\": \"the only real complain is the same one everyone else has with this model and that is the battery life could be better .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this made me realize that arm processors are not ready for desktop - class browsing .\n->this made me realize that arm processors are not ready for desktop - class browsing .\n[{'aspect': 'arm processors', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\nExample:\ntext: its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n->its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it does great and has lots of cool stuff\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit does great and has lots of cool stuff\n->", + "output": "{\"text\": \"it does great and has lots of cool stuff\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n->Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the laptop died after just one month .\n->the laptop died after just one month .\n[{'aspect': 'laptop', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: excellent product and experience with the purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent product and experience with the purchase .\n->", + "output": "{\"text\": \"excellent product and experience with the purchase .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was quick .\n->Service was quick .\n[{'aspect': 'Service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if all you are looking for is a reliable laptop to write papers on or to browse the web , this is good .\n->if all you are looking for is a reliable laptop to write papers on or to browse the web , this is good .\n[{'aspect': 'laptop', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: but the worse webcam i ' ve seen in a while and the battery dies very fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the worse webcam i ' ve seen in a while and the battery dies very fast .\n->", + "output": "{\"text\": \"but the worse webcam i ' ve seen in a while and the battery dies very fast .\", \"labels\": \"[{'aspect': 'webcam', 'opinion': 'worse', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}, {'aspect': 'battery', 'opinion': 'fast', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it arrived as promised and was exactly as described .\n->it arrived as promised and was exactly as described .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\nExample:\ntext: i highly recommend it .\n->i highly recommend it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it was easy to set up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was easy to set up .\n->", + "output": "{\"text\": \"it was easy to set up .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n->first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n[{'aspect': 'keyboard', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: highly recommend this laptop for mobile workers .\n->highly recommend this laptop for mobile workers .\n[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: it is a very good laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a very good laptop .\n->", + "output": "{\"text\": \"it is a very good laptop .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n->All in all , this midtown gem instantly became one of my favorite sushi restaurants in the city .\n[{'aspect': 'sushi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the only issue i ' m having is battery life .\n->the only issue i ' m having is battery life .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: laptop meets spec for what it was purchased to do but disappointed it was shipped with a charger not compatible with location it was shipped to ( needed to buy an adapter to convert from 2 pin to 3 pin plug )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop meets spec for what it was purchased to do but disappointed it was shipped with a charger not compatible with location it was shipped to ( needed to buy an adapter to convert from 2 pin to 3 pin plug )\n->", + "output": "{\"text\": \"laptop meets spec for what it was purchased to do but disappointed it was shipped with a charger not compatible with location it was shipped to ( needed to buy an adapter to convert from 2 pin to 3 pin plug )\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ' re not gon na find a deal like this too often .\n->you ' re not gon na find a deal like this too often .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the waitress , seems to be more concerned of looking good than actually waitressing .\n->the waitress , seems to be more concerned of looking good than actually waitressing .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it has everything he wanted and needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has everything he wanted and needs .\n->", + "output": "{\"text\": \"it has everything he wanted and needs .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works well for internet browsing and e - mail but i was hoping for much more .\n->it works well for internet browsing and e - mail but i was hoping for much more .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: we have always liked lenovo laptops .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe have always liked lenovo laptops .\n->", + "output": "{\"text\": \"we have always liked lenovo laptops .\", \"labels\": \"[{'aspect': 'lenovo laptops', 'opinion': 'liked', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n->Ive been to many Thai restaurants in Manhattan before , and Toons is by far the best Thai food Ive had ( except for my mom 's of course ) .\n[{'aspect': 'Thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love it so far very nice product and it do what it is set out to do\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove it so far very nice product and it do what it is set out to do\n->", + "output": "{\"text\": \"love it so far very nice product and it do what it is set out to do\", \"labels\": \"[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for each course we waited over 1 / 2 hour to 45 minutes and were never offered a drink .\n->for each course we waited over 1 / 2 hour to 45 minutes and were never offered a drink .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it is great quality .\n->it is great quality .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\n->", + "output": "{\"text\": \"i got this lenovo ideapad 320 ( the amd a12 7th generation version ) and it ' s absolutely incredible !\", \"labels\": \"[{'aspect': 'lenovo ideapad 320', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n->as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: they have great rolls , the triple color and norwegetan rolls , are awesome and filling .\n->they have great rolls , the triple color and norwegetan rolls , are awesome and filling .\n[{'aspect': 'rolls', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'triple color and norwegetan rolls', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'triple color and norwegetan rolls', 'opinion': 'filling', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: really fast also lenovo ' s customer service excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreally fast also lenovo ' s customer service excellent .\n->", + "output": "{\"text\": \"really fast also lenovo ' s customer service excellent .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: toons has recently been redone , so it ' s now a very attractive space .\n->toons has recently been redone , so it ' s now a very attractive space .\n[{'aspect': 'toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: not sure if this is simply a deal accident , but my ssd failed within 4 months .\n->not sure if this is simply a deal accident , but my ssd failed within 4 months .\n[{'aspect': 'ssd', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: cons hd display is n ' t the greatest\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncons hd display is n ' t the greatest\n->", + "output": "{\"text\": \"cons hd display is n ' t the greatest\", \"labels\": \"[{'aspect': 'hd display', 'opinion': 'cons', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'hd display', 'opinion': \"n ' t the greatest\", 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is phenomenal again .\n->battery life is phenomenal again .\n[{'aspect': 'battery life', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: so again , the battery is surprisingly great .\n->so again , the battery is surprisingly great .\n[{'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: battery last 2 1 / 2 hours\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery last 2 1 / 2 hours\n->", + "output": "{\"text\": \"battery last 2 1 / 2 hours\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We thought that this place is using too much of MSG cooking in the foods .\n->We thought that this place is using too much of MSG cooking in the foods .\n[{'aspect': 'MSG cooking', 'opinion': 'too much', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n->Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'regular', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n->", + "output": "{\"text\": \"i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'boot speed', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'OS#GENERAL'}, {'aspect': 'cooling fan', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer is nice , blah blah it has nice features but it stops working after a few months .\n->the computer is nice , blah blah it has nice features but it stops working after a few months .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n->i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: i don ' t like the fact that the battery go low very fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t like the fact that the battery go low very fast .\n->", + "output": "{\"text\": \"i don ' t like the fact that the battery go low very fast .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'fast', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n->replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: feel like at max brightness it just isn ' t enough .\n->feel like at max brightness it just isn ' t enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: it has way more than i will ever need because all i do is check my email and facebook , but it is crazy fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has way more than i will ever need because all i do is check my email and facebook , but it is crazy fast .\n->", + "output": "{\"text\": \"it has way more than i will ever need because all i do is check my email and facebook , but it is crazy fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: laptop screen goes blank after four weeks minimally used .\n->laptop screen goes blank after four weeks minimally used .\n[{'aspect': 'laptop screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve enjoyed 99 % of the dishes we ' ve ordered with the only exceptions being the occasional too - authentic - for - me dish ( i ' m a daring eater but not that daring ) .\n->i ' ve enjoyed 99 % of the dishes we ' ve ordered with the only exceptions being the occasional too - authentic - for - me dish ( i ' m a daring eater but not that daring ) .\n[{'aspect': 'dishes', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dish', 'opinion': 'too - authentic - for - me', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i don ' t really have anywhere to rest my hand when i use it because it ' s so large .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t really have anywhere to rest my hand when i use it because it ' s so large .\n->", + "output": "{\"text\": \"i don ' t really have anywhere to rest my hand when i use it because it ' s so large .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'large', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer runs great .\n->the computer runs great .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n->the only things u could really taste are the very salty soy sauce ( even its low sodium ) , the vinegar-soaked rice , and the scallion on top of the fish .\n[{'aspect': 'soy sauce', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'vinegar-soaked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: otherwise i really do love it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \notherwise i really do love it\n->", + "output": "{\"text\": \"otherwise i really do love it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n->in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n[{'aspect': 'fingerprints', 'opinion': 'dislike', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}]\nExample:\ntext: We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n->We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: plenty of memory and storage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplenty of memory and storage .\n->", + "output": "{\"text\": \"plenty of memory and storage .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n->i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the bestt !\n->the bestt !\n[{'aspect': 'NULL', 'opinion': 'bestt', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: love this laptop\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove this laptop\n->", + "output": "{\"text\": \"love this laptop\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: planet thailand has always been a hit with me , i go there usually for the sushi , which is great , the thai food is excellent too .\n->planet thailand has always been a hit with me , i go there usually for the sushi , which is great , the thai food is excellent too .\n[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'planet thailand', 'opinion': 'hit', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n->Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n[{'aspect': 'prices', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i am very happy with this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very happy with this laptop .\n->", + "output": "{\"text\": \"i am very happy with this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n->i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n[{'aspect': 'keyboard', 'opinion': 'worried', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n->The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n[{'aspect': 'fillings', 'opinion': 'unconventional', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dosa batter', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen is just right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is just right .\n->", + "output": "{\"text\": \"the screen is just right .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'right', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i waited for a while before writing a review about this product .\n->i waited for a while before writing a review about this product .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: best dining experience in the west village !\n->best dining experience in the west village !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i bought this for my daughter for school and she loves it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this for my daughter for school and she loves it .\n->", + "output": "{\"text\": \"i bought this for my daughter for school and she loves it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the original hdd in this laptop has some speed limitations for load up .\n->the original hdd in this laptop has some speed limitations for load up .\n[{'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: We went to eat at the Jekyll and Hyde restaurant on Friday night and really enjoyed the fun atmosphere and good food .\n->We went to eat at the Jekyll and Hyde restaurant on Friday night and really enjoyed the fun atmosphere and good food .\n[{'aspect': 'atmosphere', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the specs are good on this for a good cheap low end gaming machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe specs are good on this for a good cheap low end gaming machine .\n->", + "output": "{\"text\": \"the specs are good on this for a good cheap low end gaming machine .\", \"labels\": \"[{'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would definitely recommend SEA if you like thai cuisine !\n->I would definitely recommend SEA if you like thai cuisine !\n[{'aspect': 'thai cuisine', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service - friendly and attentive .\n->service - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: so before you get angry do your homework on why the laptop may be acting strange .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso before you get angry do your homework on why the laptop may be acting strange .\n->", + "output": "{\"text\": \"so before you get angry do your homework on why the laptop may be acting strange .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'angry', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chrome os is pretty simplistic and easy to learn .\n->chrome os is pretty simplistic and easy to learn .\n[{'aspect': 'chrome os', 'opinion': 'simplistic', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'chrome os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\nExample:\ntext: - battery life is pretty amazing at 10 - 11hrs\n->- battery life is pretty amazing at 10 - 11hrs\n[{'aspect': 'battery life', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: great computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat computer .\n->", + "output": "{\"text\": \"great computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do not buy it like me , a samsung fan .\n->do not buy it like me , a samsung fan .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Great service , great food .\n->Great service , great food .\n[{'aspect': 'service', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: fast , sturdy with a beautiful display\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast , sturdy with a beautiful display\n->", + "output": "{\"text\": \"fast , sturdy with a beautiful display\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * excellent form factor , extremely portable while remaining a serious pro computer\n->* excellent form factor , extremely portable while remaining a serious pro computer\n[{'aspect': 'pro computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro computer', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: one thing keeps it from getting a five - star rave from me .\n->one thing keeps it from getting a five - star rave from me .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: 1 , bloatware from hell and 2 , a very cheap crappy 5400rpm hdd .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n1 , bloatware from hell and 2 , a very cheap crappy 5400rpm hdd .\n->", + "output": "{\"text\": \"1 , bloatware from hell and 2 , a very cheap crappy 5400rpm hdd .\", \"labels\": \"[{'aspect': 'bloatware', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': '5400rpm hdd', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'HARD_DISC#PRICE'}, {'aspect': '5400rpm hdd', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'HARD_DISC#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was the friendliest that have seen in New York .\n->The staff was the friendliest that have seen in New York .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: suan is a great place that i often take my friends ( classmates ) too .\n->suan is a great place that i often take my friends ( classmates ) too .\n[{'aspect': 'suan', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: multitasking is pretty good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmultitasking is pretty good .\n->", + "output": "{\"text\": \"multitasking is pretty good .\", \"labels\": \"[{'aspect': 'multitasking', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The palak paneer was standard , and I was not a fan of the malai kofta .\n->The palak paneer was standard , and I was not a fan of the malai kofta .\n[{'aspect': 'palak paneer', 'opinion': 'standard', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The chicken parm was edible but had canned tomato sauce and boxed pasta and the chicken with portobello mushrooms consisted of dry , inedible chicken with terrible sauce .\n->The chicken parm was edible but had canned tomato sauce and boxed pasta and the chicken with portobello mushrooms consisted of dry , inedible chicken with terrible sauce .\n[{'aspect': 'chicken', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tomato sauce', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: battery is okay .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery is okay .\n->", + "output": "{\"text\": \"battery is okay .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They sell special sushi , everything have a topping , sauce and etc .\n->They sell special sushi , everything have a topping , sauce and etc .\n[{'aspect': 'sushi', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Also good for client lunch meetings , esp .\n->Also good for client lunch meetings , esp .\n[{'aspect': 'lunch meetings', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: keyboard is pretty damn good and the track pad is fair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard is pretty damn good and the track pad is fair .\n->", + "output": "{\"text\": \"keyboard is pretty damn good and the track pad is fair .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'track pad', 'opinion': 'fair', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my third and last chromebook .\n->this is my third and last chromebook .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n->when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n[{'aspect': 'NULL', 'opinion': 'okay', 'polarity': 'positive', 'category': 'FANS&COOLING#GENERAL'}]\ntext: fan noise could be a bit quieter when the cpu is being taxed but not annoyingly loud either , under lite use the pc is silent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfan noise could be a bit quieter when the cpu is being taxed but not annoyingly loud either , under lite use the pc is silent .\n->", + "output": "{\"text\": \"fan noise could be a bit quieter when the cpu is being taxed but not annoyingly loud either , under lite use the pc is silent .\", \"labels\": \"[{'aspect': 'fan', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s easy find and delete pics and files you ' ve downloaded .\n->it ' s easy find and delete pics and files you ' ve downloaded .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Beef noodle soup is good as well .\n->Beef noodle soup is good as well .\n[{'aspect': 'Beef noodle soup', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: has ac wireless so you can see the 5ghz on your network , blazing fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhas ac wireless so you can see the 5ghz on your network , blazing fast .\n->", + "output": "{\"text\": \"has ac wireless so you can see the 5ghz on your network , blazing fast .\", \"labels\": \"[{'aspect': 'ac wireless', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n->the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n[{'aspect': '1080p screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': '1080p screen', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'hinge', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: at this price range it is a fine screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat this price range it is a fine screen .\n->", + "output": "{\"text\": \"at this price range it is a fine screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place blew me away ... by far my new favorite restaurant on the uppereast side .\n->This place blew me away ... by far my new favorite restaurant on the uppereast side .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it was as they advertised .\n->it was as they advertised .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great laptop if you are willing to put in an ssd and reinstall windows .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat laptop if you are willing to put in an ssd and reinstall windows .\n->", + "output": "{\"text\": \"great laptop if you are willing to put in an ssd and reinstall windows .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was shocked that my friends wanted to stay after the waitress said , ` ` can i help you ' ' and ` ` how many are in your party . ' '\n->i was shocked that my friends wanted to stay after the waitress said , ` ` can i help you ' ' and ` ` how many are in your party . ' '\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: very very very very weak battery and the play store is a joke , this thing still needs a lot of work to become a serious product .\n->very very very very weak battery and the play store is a joke , this thing still needs a lot of work to become a serious product .\n[{'aspect': 'battery', 'opinion': 'weak', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'play store', 'opinion': 'joke', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it is very easy to use the set up was awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is very easy to use the set up was awesome .\n->", + "output": "{\"text\": \"it is very easy to use the set up was awesome .\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}, {'aspect': 'set up', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we all felt it was worth it .\n->we all felt it was worth it .\n[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: if i could give 0 stars i would do so for this place .\n->if i could give 0 stars i would do so for this place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n->", + "output": "{\"text\": \"that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not imagine you not rushing out to eat there .\n->i can not imagine you not rushing out to eat there .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: easily the worst stir - fried squid i ' ve ever tasted .\n->easily the worst stir - fried squid i ' ve ever tasted .\n[{'aspect': 'stir - fried squid', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i have had no issues with it since i bought it i would highly recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had no issues with it since i bought it i would highly recommend it .\n->", + "output": "{\"text\": \"i have had no issues with it since i bought it i would highly recommend it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the have a great cocktail with citrus vodka and lemon and lime juice and mint leaves that is to die for !\n->the have a great cocktail with citrus vodka and lemon and lime juice and mint leaves that is to die for !\n[{'aspect': 'cocktail with citrus vodka and lemon and lime juice and mint leaves', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n->they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i loved how fast it was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved how fast it was .\n->", + "output": "{\"text\": \"i loved how fast it was .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n->I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n[{'aspect': 'Edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good screen quality for reading , fairly fast i3 processor , and decent battery life .\n->good screen quality for reading , fairly fast i3 processor , and decent battery life .\n[{'aspect': 'screen quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'i3 processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'decent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: also can not just ` ` add ` ` memory ddms : only one socket means you must remove the 8gb and purchace a 16gb ( ~ $ 180 further investment ) ; battery not gon na last several hrs , looking at ~ 2 - 4 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso can not just ` ` add ` ` memory ddms : only one socket means you must remove the 8gb and purchace a 16gb ( ~ $ 180 further investment ) ; battery not gon na last several hrs , looking at ~ 2 - 4 .\n->", + "output": "{\"text\": \"also can not just ` ` add ` ` memory ddms : only one socket means you must remove the 8gb and purchace a 16gb ( ~ $ 180 further investment ) ; battery not gon na last several hrs , looking at ~ 2 - 4 .\", \"labels\": \"[{'aspect': 'memory ddms', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 1 , the touchscreen stopped working after 6 months for finger presses .\n->1 , the touchscreen stopped working after 6 months for finger presses .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n->the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i love this laptop so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this laptop so far .\n->", + "output": "{\"text\": \"i love this laptop so far .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n->Wait staff is blantently unappreciative of your business but its the best pie on the UWS !\n[{'aspect': 'Wait staff', 'opinion': 'unappreciative', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a large is $ 20 , and toppings are about $ 3 each .\n->a large is $ 20 , and toppings are about $ 3 each .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'toppings', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: the start up process was very simple and relatively quick .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe start up process was very simple and relatively quick .\n->", + "output": "{\"text\": \"the start up process was very simple and relatively quick .\", \"labels\": \"[{'aspect': 'start up', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: backlit keyboard is great ; feels sturdy ; fast processing .\n->backlit keyboard is great ; feels sturdy ; fast processing .\n[{'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n->My suggestion is to eat family style because you 'll want to try the other dishes .\n[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i was able to download all of my games in a quick amount of time too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was able to download all of my games in a quick amount of time too .\n->", + "output": "{\"text\": \"i was able to download all of my games in a quick amount of time too .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Went here last night - nice decor , good service , but the food was surprisingly excellent .\n->Went here last night - nice decor , good service , but the food was surprisingly excellent .\n[{'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: feel totally comfortable with it , and will never go back to a pc .\n->feel totally comfortable with it , and will never go back to a pc .\n[{'aspect': 'NULL', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the laptop is the perfect size , it ' s not too heavy and it ' s slim .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop is the perfect size , it ' s not too heavy and it ' s slim .\n->", + "output": "{\"text\": \"the laptop is the perfect size , it ' s not too heavy and it ' s slim .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food has been consistant for years and it never lets you down .\n->The food has been consistant for years and it never lets you down .\n[{'aspect': 'food', 'opinion': 'consistant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n->i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n[{'aspect': 'performs', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: have had mine for about 3 - 4 wks and have had no trouble .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave had mine for about 3 - 4 wks and have had no trouble .\n->", + "output": "{\"text\": \"have had mine for about 3 - 4 wks and have had no trouble .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: less than three minutes passed before i found myself doubled over the toilet .\n->less than three minutes passed before i found myself doubled over the toilet .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n->this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n[{'aspect': 'trattoria', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'trattoria', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the tech and the quantities ( ram , etc . )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe tech and the quantities ( ram , etc . )\n->", + "output": "{\"text\": \"the tech and the quantities ( ram , etc . )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n->we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'delight', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: we were greeted promptly by the waiter who was very nice and cordial .\n->we were greeted promptly by the waiter who was very nice and cordial .\n[{'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'cordial', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i love my laptop but the battery life is the worst i ' ve ever had on a laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love my laptop but the battery life is the worst i ' ve ever had on a laptop .\n->", + "output": "{\"text\": \"i love my laptop but the battery life is the worst i ' ve ever had on a laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'worst', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the view is spectacular , and the food is great .\n->the view is spectacular , and the food is great .\n[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n->i bought this for a light user and it seems like the chrome os is going to work well for her - simple and quick .\n[{'aspect': 'chrome os', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'simple', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: it ' s still loading and working pretty fast and you ca n ' t beat the storage for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s still loading and working pretty fast and you ca n ' t beat the storage for the price .\n->", + "output": "{\"text\": \"it ' s still loading and working pretty fast and you ca n ' t beat the storage for the price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 1 ) the delete button is right next to the power button\n->1 ) the delete button is right next to the power button\n[{'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n->i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'software', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\ntext: overall i ' m happy with it for what i use it for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall i ' m happy with it for what i use it for .\n->", + "output": "{\"text\": \"overall i ' m happy with it for what i use it for .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer is nice , blah blah it has nice features but it stops working after a few months .\n->the computer is nice , blah blah it has nice features but it stops working after a few months .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: even with the volume turned up all the way , the sound is very low which means that you have a poor soundstage .\n->even with the volume turned up all the way , the sound is very low which means that you have a poor soundstage .\n[{'aspect': 'sound', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: if your main priority is a long lasting battery this is n ' t for you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif your main priority is a long lasting battery this is n ' t for you .\n->", + "output": "{\"text\": \"if your main priority is a long lasting battery this is n ' t for you .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price i think it ' s just fine .\n->for the price i think it ' s just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n->service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'setting / atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: great price for a nice laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat price for a nice laptop .\n->", + "output": "{\"text\": \"great price for a nice laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the decor is very simple but comfortable .\n->the decor is very simple but comfortable .\n[{'aspect': 'decor', 'opinion': 'simple', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n->the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: bought this for my daughter ' s senior year of college and she ' s very happy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought this for my daughter ' s senior year of college and she ' s very happy .\n->", + "output": "{\"text\": \"bought this for my daughter ' s senior year of college and she ' s very happy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff is no nonsense .\n->the staff is no nonsense .\n[{'aspect': 'staff', 'opinion': 'no nonsense', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the steelsteries keyboard feels great and looks awesome , but the keyboard surface of the laptop can get warm while sitting idle or gaming even when i have a fan pushing cold air underneath the laptop .\n->the steelsteries keyboard feels great and looks awesome , but the keyboard surface of the laptop can get warm while sitting idle or gaming even when i have a fan pushing cold air underneath the laptop .\n[{'aspect': 'steelsteries keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'steelsteries keyboard', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: it is a very nice computer that serves his needs very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a very nice computer that serves his needs very well .\n->", + "output": "{\"text\": \"it is a very nice computer that serves his needs very well .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is tasty and portion sizes are appropriate .\n->the food is tasty and portion sizes are appropriate .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: \u2022 occasionally hangs for 10 - 30 seconds with no response from keyboard or trackpad\n->\u2022 occasionally hangs for 10 - 30 seconds with no response from keyboard or trackpad\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: 8g is enough to do most daily activities , i use nvidia geforce now to play much higher end games like destiny 2 , r6 and what not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n8g is enough to do most daily activities , i use nvidia geforce now to play much higher end games like destiny 2 , r6 and what not .\n->", + "output": "{\"text\": \"8g is enough to do most daily activities , i use nvidia geforce now to play much higher end games like destiny 2 , r6 and what not .\", \"labels\": \"[{'aspect': 'nvidia geforce', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'GRAPHICS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicious bagels , especially when right out of the oven .\n->delicious bagels , especially when right out of the oven .\n[{'aspect': 'bagels', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this is by far my favorite place in the neighborhood .\n->this is by far my favorite place in the neighborhood .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the only cons are the battery and the brightness of the screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only cons are the battery and the brightness of the screen .\n->", + "output": "{\"text\": \"the only cons are the battery and the brightness of the screen .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'cons', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'cons', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s to die for !\n->it ' s to die for !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n->I would highly recommend this place to anyone looking for a casual atmosphere that whisks you away to the left bank of the river Seine .\n[{'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is a brand i look for that i feel i can trust .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a brand i look for that i feel i can trust .\n->", + "output": "{\"text\": \"this is a brand i look for that i feel i can trust .\", \"labels\": \"[{'aspect': 'brand', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never got an explanation as to what was going on .\n->never got an explanation as to what was going on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n->the atmosphere is nothing special , but it feels like a sushi establishment in tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: works great and looks great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks great and looks great .\n->", + "output": "{\"text\": \"works great and looks great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: would have been 5 stars if the keyboard was back light and the finger print reader had linux drivers .\n->would have been 5 stars if the keyboard was back light and the finger print reader had linux drivers .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'finger print reader', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: Sure , the setting is nice .\n->Sure , the setting is nice .\n[{'aspect': 'setting', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great buy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat buy .\n->", + "output": "{\"text\": \"great buy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu choices are similar but the taste lacked more flavor than it looked .\n->The menu choices are similar but the taste lacked more flavor than it looked .\n[{'aspect': 'taste', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu choices', 'opinion': 'similar', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n->even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'working unit', 'opinion': 'outweighs', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the laptop works well , i have no complaints .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop works well , i have no complaints .\n->", + "output": "{\"text\": \"the laptop works well , i have no complaints .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought an acer computer that did not work .\n->bought an acer computer that did not work .\n[{'aspect': 'acer computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: in the summer months , the back garden area is really nice .\n->in the summer months , the back garden area is really nice .\n[{'aspect': 'back garden area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the only draw back with this pc is the battery life which lasts about 3 hrs before needing to be charged .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only draw back with this pc is the battery life which lasts about 3 hrs before needing to be charged .\n->", + "output": "{\"text\": \"the only draw back with this pc is the battery life which lasts about 3 hrs before needing to be charged .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'draw back', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The well mannered , pleasant staff that Tony has in his employ .\n->The well mannered , pleasant staff that Tony has in his employ .\n[{'aspect': 'staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you wan na spend a couple hundred for a laptop this is deffanently worth it .\n->if you wan na spend a couple hundred for a laptop this is deffanently worth it .\n[{'aspect': 'laptop', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: nice little computer for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice little computer for the price .\n->", + "output": "{\"text\": \"nice little computer for the price .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n->chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n[{'aspect': 'chrome', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is working really well .\n->it is working really well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n->", + "output": "{\"text\": \"but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'worst', 'polarity': 'negative', 'category': 'OS#GENERAL'}, {'aspect': 'windows 10', 'opinion': 'awful', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had the mango chicken and i ca n't go on to tell you how delicious that was and the presentation was beautiful .\n->I had the mango chicken and i ca n't go on to tell you how delicious that was and the presentation was beautiful .\n[{'aspect': 'mango chicken', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'presentation', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very disappointed and i have n ' t even had the product for 12 hours .\n->very disappointed and i have n ' t even had the product for 12 hours .\n[{'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i hate windows 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni hate windows 10 .\n->", + "output": "{\"text\": \"i hate windows 10 .\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'hate', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is very good for it 's price , better than most fried dumplings I 've had .\n->The food is very good for it 's price , better than most fried dumplings I 've had .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried dumplings', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the computer literally blue screened on the second day because system 32 was corrupt .\n->the computer literally blue screened on the second day because system 32 was corrupt .\n[{'aspect': 'system 32', 'opinion': 'corrupt', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: liking it a lot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nliking it a lot .\n->", + "output": "{\"text\": \"liking it a lot .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'liking', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , waiters try to push more food on you , like suggest things as if they are complimentary when they actually cost $ .\n->also , waiters try to push more food on you , like suggest things as if they are complimentary when they actually cost $ .\n[{'aspect': 'waiters', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the large screen and integral numeric pad are exactly what i need to easily process documents .\n->the large screen and integral numeric pad are exactly what i need to easily process documents .\n[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'integral numeric pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: haven ' t had it long so only 4 stars but if it holds up it ' s worth 5 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhaven ' t had it long so only 4 stars but if it holds up it ' s worth 5 .\n->", + "output": "{\"text\": \"haven ' t had it long so only 4 stars but if it holds up it ' s worth 5 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n->i have been using samsung chromebooks for 5 years , my 2012 chromebook was finally hard to charge , but it might have had something to do with having been dropped several times , and still running great , but hard to charge .\n[{'aspect': '2012 chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: first of all , this is a physically beautiful machine .\n->first of all , this is a physically beautiful machine .\n[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: has nice features for the price and nice video for streaming movies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhas nice features for the price and nice video for streaming movies .\n->", + "output": "{\"text\": \"has nice features for the price and nice video for streaming movies .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'video', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: La Rosa waltzes in , and I think they are doing it the best .\n->La Rosa waltzes in , and I think they are doing it the best .\n[{'aspect': 'La Rosa', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was very good - prompt , attentive and non - intrusive .\n->service was very good - prompt , attentive and non - intrusive .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'non - intrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: bought this for my ten year old for school and creating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought this for my ten year old for school and creating .\n->", + "output": "{\"text\": \"bought this for my ten year old for school and creating .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i updated it to high sierra and it ' s running smoothly so far .\n->i updated it to high sierra and it ' s running smoothly so far .\n[{'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: none was made so i hung up .\n->none was made so i hung up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: she loves it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe loves it !\n->", + "output": "{\"text\": \"she loves it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we had a great time at the jekyll and hyde pub last night .\n->we had a great time at the jekyll and hyde pub last night .\n[{'aspect': 'jekyll and hyde pub', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: but the worse webcam i ' ve seen in a while and the battery dies very fast .\n->but the worse webcam i ' ve seen in a while and the battery dies very fast .\n[{'aspect': 'webcam', 'opinion': 'worse', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}, {'aspect': 'battery', 'opinion': 'fast', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i would buy this for myself as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would buy this for myself as well .\n->", + "output": "{\"text\": \"i would buy this for myself as well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ive researched this and it is very common and apple says it ' s normal .\n->ive researched this and it is very common and apple says it ' s normal .\n[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: they have a huge selection of different cream cheeses and all of their salads are great .\n->they have a huge selection of different cream cheeses and all of their salads are great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: i wish the camera was a little better , but it ' s great otherwise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wish the camera was a little better , but it ' s great otherwise .\n->", + "output": "{\"text\": \"i wish the camera was a little better , but it ' s great otherwise .\", \"labels\": \"[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would defiantly come back here again as one of my top choices .\n->i would defiantly come back here again as one of my top choices .\n[{'aspect': 'NULL', 'opinion': 'top choices', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The sushi was awful !\n->The sushi was awful !\n[{'aspect': 'sushi', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n->", + "output": "{\"text\": \"i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the most well priced laptop for its spec\n->this is the most well priced laptop for its spec\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'spec', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the charging issue i can live with as well , even though it is annoying .\n->the charging issue i can live with as well , even though it is annoying .\n[{'aspect': 'charging', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: battery life sucks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life sucks .\n->", + "output": "{\"text\": \"battery life sucks .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do n ' t be fooled by crowds of people .\n->do n ' t be fooled by crowds of people .\n[{'aspect': 'NULL', 'opinion': 'fooled', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: I found it on a cold night , the perfect spot to warm up .\n->I found it on a cold night , the perfect spot to warm up .\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the camera sucks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe camera sucks .\n->", + "output": "{\"text\": \"the camera sucks .\", \"labels\": \"[{'aspect': 'camera', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchscreen is great and the backlit keyboard is fantastic .\n->the touchscreen is great and the backlit keyboard is fantastic .\n[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'backlit keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: I can not imagine a friendlier staff working in a restaurant .\n->I can not imagine a friendlier staff working in a restaurant .\n[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is nice for the price and has good speed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is nice for the price and has good speed .\n->", + "output": "{\"text\": \"this is nice for the price and has good speed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Saturday , Nov. 6th I had a group from work come in with about 35 people and the staff was amazing to accomodate us .\n->Saturday , Nov. 6th I had a group from work come in with about 35 people and the staff was amazing to accomodate us .\n[{'aspect': 'staff', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was due to upgrade and this product seemed perfect for me .\n->i was due to upgrade and this product seemed perfect for me .\n[{'aspect': 'product', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this laptop exceeds my expectations for a mid - price laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop exceeds my expectations for a mid - price laptop .\n->", + "output": "{\"text\": \"this laptop exceeds my expectations for a mid - price laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Dim Sum was so-so , but not spectacular .\n->The Dim Sum was so-so , but not spectacular .\n[{'aspect': 'Dim Sum', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Dim Sum', 'opinion': 'not spectacular', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n->the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n[{'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\ntext: the only problem is that certain streaming video applications freeze up in windows 10 and i can not remember how i resolved the problem on my old laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only problem is that certain streaming video applications freeze up in windows 10 and i can not remember how i resolved the problem on my old laptop .\n->", + "output": "{\"text\": \"the only problem is that certain streaming video applications freeze up in windows 10 and i can not remember how i resolved the problem on my old laptop .\", \"labels\": \"[{'aspect': 'streaming video applications', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a decent laptop no thanks to asus support .\n->this is a decent laptop no thanks to asus support .\n[{'aspect': 'laptop', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i would highly recommend this product if you want to get into music production like myself .\n->i would highly recommend this product if you want to get into music production like myself .\n[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the customer support rep at amazon was of no help , though he did try .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe customer support rep at amazon was of no help , though he did try .\n->", + "output": "{\"text\": \"the customer support rep at amazon was of no help , though he did try .\", \"labels\": \"[{'aspect': 'customer support rep', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , after having the computer for about 4 months it suddenly died one day and would not turn on .\n->however , after having the computer for about 4 months it suddenly died one day and would not turn on .\n[{'aspect': 'computer', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n->android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n[{'aspect': 'android', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i use this laptop for work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use this laptop for work .\n->", + "output": "{\"text\": \"i use this laptop for work .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: went on a 3 day oyster binge , with fish bringing up the closing , and i am so glad this was the place it o trip ended , because it was so great !\n->went on a 3 day oyster binge , with fish bringing up the closing , and i am so glad this was the place it o trip ended , because it was so great !\n[{'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n->I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n[{'aspect': 'pastrami on challah sandwich', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: otherwise , it ' s a good computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \notherwise , it ' s a good computer .\n->", + "output": "{\"text\": \"otherwise , it ' s a good computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we wo n ' t go to this place again for a good meal .\n->we wo n ' t go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: great deal on a great computer !\n->great deal on a great computer !\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it ' s pretty fast even with heavy use and multiple applications running at once .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s pretty fast even with heavy use and multiple applications running at once .\n->", + "output": "{\"text\": \"it ' s pretty fast even with heavy use and multiple applications running at once .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Somehow working the italian charm with constant mille grazie does not constitute proper service .\n->Somehow working the italian charm with constant mille grazie does not constitute proper service .\n[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: laptop in perfect condition .\n->laptop in perfect condition .\n[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: love the laptop ; great quality ; sent as expected , on time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the laptop ; great quality ; sent as expected , on time .\n->", + "output": "{\"text\": \"love the laptop ; great quality ; sent as expected , on time .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Acceptable prices .\n->Acceptable prices .\n[{'aspect': 'prices', 'opinion': 'Acceptable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this establishment is the real deal .\n->this establishment is the real deal .\n[{'aspect': 'establishment', 'opinion': 'real deal', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it is far and away the best i ' ve ever had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is far and away the best i ' ve ever had .\n->", + "output": "{\"text\": \"it is far and away the best i ' ve ever had .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n->Great wine selection , Gigondas is worth the price , and the house champagne is a great value .\n[{'aspect': 'wine selection', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Gigondas', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'house champagne', 'opinion': 'great value', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'worth', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n->Very popular style Izakaya ( Sake and small portion of sake-friendly dishes ) .\n[{'aspect': 'dishes', 'opinion': 'sake-friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portion', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: fantastic computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfantastic computer .\n->", + "output": "{\"text\": \"fantastic computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is so cool and the service is prompt and curtious .\n->the place is so cool and the service is prompt and curtious .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: very happy with my purchase , fast delivery , package well .\n->very happy with my purchase , fast delivery , package well .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'package', 'opinion': 'well', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the programs run great with no lag time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe programs run great with no lag time .\n->", + "output": "{\"text\": \"the programs run great with no lag time .\", \"labels\": \"[{'aspect': 'programs', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if i could give 0 stars i would do so for this place .\n->if i could give 0 stars i would do so for this place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The sandwhiches are out of this world !\n->The sandwhiches are out of this world !\n[{'aspect': 'sandwhiches', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\ntext: battery lasts for quite sometime .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery lasts for quite sometime .\n->", + "output": "{\"text\": \"battery lasts for quite sometime .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n->The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'moist not dry', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n->I did n't expect to like Nosh as much as I did , but the pastrami on challah sandwich I had was otherworldly , the soups are like Mom 's , and the knishes give Yonah Schimmel 's a run for its money .\n[{'aspect': 'pastrami on challah sandwich', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s not real fast and it doesn ' t have a lot of storage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s not real fast and it doesn ' t have a lot of storage .\n->", + "output": "{\"text\": \"it ' s not real fast and it doesn ' t have a lot of storage .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: technical support was easy to reach , but not able to stop the problem i was having .\n->technical support was easy to reach , but not able to stop the problem i was having .\n[{'aspect': 'technical support', 'opinion': 'easy', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: one of my favorite places in manhattan .\n->one of my favorite places in manhattan .\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i don ' t ask much of a laptop so i am happy with it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t ask much of a laptop so i am happy with it .\n->", + "output": "{\"text\": \"i don ' t ask much of a laptop so i am happy with it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: although we were told 10 - 15 minutes and it was more like 45 minutes .\n->although we were told 10 - 15 minutes and it was more like 45 minutes .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the e15 has a bright , 1080p screen - text is extremely sharp .\n->the e15 has a bright , 1080p screen - text is extremely sharp .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: it ' s fast , connects quickly to wifi , and the screen is quite nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s fast , connects quickly to wifi , and the screen is quite nice .\n->", + "output": "{\"text\": \"it ' s fast , connects quickly to wifi , and the screen is quite nice .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambiance -- relaxed and stylish .\n->Ambiance -- relaxed and stylish .\n[{'aspect': 'Ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n->i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n[{'aspect': 'NULL', 'opinion': 'nasty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i liked the fact that lenovo came with the microsoft programs on it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni liked the fact that lenovo came with the microsoft programs on it .\n->", + "output": "{\"text\": \"i liked the fact that lenovo came with the microsoft programs on it .\", \"labels\": \"[{'aspect': 'lenovo', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing i think that could be better is the volume of the speakers .\n->the only thing i think that could be better is the volume of the speakers .\n[{'aspect': 'speakers', 'opinion': 'could be better', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: vanison was good but not amazing .\n->vanison was good but not amazing .\n[{'aspect': 'vanison', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'vanison', 'opinion': 'not amazing', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: the only problem that i have found about lenovo is that it comes with a program called migration , which is supposed to migrate your things from your old computer to your new computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only problem that i have found about lenovo is that it comes with a program called migration , which is supposed to migrate your things from your old computer to your new computer .\n->", + "output": "{\"text\": \"the only problem that i have found about lenovo is that it comes with a program called migration , which is supposed to migrate your things from your old computer to your new computer .\", \"labels\": \"[{'aspect': 'migration', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Somehow working the italian charm with constant mille grazie does not constitute proper service .\n->Somehow working the italian charm with constant mille grazie does not constitute proper service .\n[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: super fast boot .\n->super fast boot .\n[{'aspect': 'boot', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\ntext: no problems with this computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno problems with this computer .\n->", + "output": "{\"text\": \"no problems with this computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice screen and keyboard , touch pad is great .\n->nice screen and keyboard , touch pad is great .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: love the graphics when i replay my videos or watch other streamers .\n->love the graphics when i replay my videos or watch other streamers .\n[{'aspect': 'graphics', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: i enjoy that it has 10 key .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni enjoy that it has 10 key .\n->", + "output": "{\"text\": \"i enjoy that it has 10 key .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wo n ' t go back unless someone else is footing the bill .\n->i wo n ' t go back unless someone else is footing the bill .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: these are overpriced and you can get better just around the corner :\n->these are overpriced and you can get better just around the corner :\n[{'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: fast to start up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast to start up .\n->", + "output": "{\"text\": \"fast to start up .\", \"labels\": \"[{'aspect': 'start up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .\n->my chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .\n[{'aspect': 'chicken', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n->i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n[{'aspect': 'battery', 'opinion': 'better', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: for reference : ive had this laptop for about 4 months now for my first year at college .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor reference : ive had this laptop for about 4 months now for my first year at college .\n->", + "output": "{\"text\": \"for reference : ive had this laptop for about 4 months now for my first year at college .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n->all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n[{'aspect': 'desktop / application options', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: I liked the beer selection !\n->I liked the beer selection !\n[{'aspect': 'beer selection', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if all you are looking for is a reliable laptop to write papers on or to browse the web , this is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif all you are looking for is a reliable laptop to write papers on or to browse the web , this is good .\n->", + "output": "{\"text\": \"if all you are looking for is a reliable laptop to write papers on or to browse the web , this is good .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere was pretty nice but had a bit lacking , which it tries to make up for with a crazy scheme of mirrors .\n->the atmosphere was pretty nice but had a bit lacking , which it tries to make up for with a crazy scheme of mirrors .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'scheme of mirrors', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n->You get what you pay for and with that logic in mind , Spice is a great place to grab some cheap eats and drinks in a beautiful setting .\n[{'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: youtube works well on here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyoutube works well on here .\n->", + "output": "{\"text\": \"youtube works well on here .\", \"labels\": \"[{'aspect': 'youtube', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they even scoop it out nice ( for those on a diet ) not too much not to little .\n->they even scoop it out nice ( for those on a diet ) not too much not to little .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: one of the best hot dogs i have ever eaten .\n->one of the best hot dogs i have ever eaten .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: runs great - however , i just tried to upload my small adventure videos and the built in video player stops mid video about 10 seconds in - audio continues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nruns great - however , i just tried to upload my small adventure videos and the built in video player stops mid video about 10 seconds in - audio continues .\n->", + "output": "{\"text\": \"runs great - however , i just tried to upload my small adventure videos and the built in video player stops mid video about 10 seconds in - audio continues .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'video player', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n->the food was delicious ( i had a halibut special , my husband had steak ) , and the service was top - notch .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'halibut special', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'steak', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'top - notch', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: every time in new york i make it a point to visit restaurant saul on smith street .\n->every time in new york i make it a point to visit restaurant saul on smith street .\n[{'aspect': 'restaurant saul', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: battery life is horrible though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is horrible though .\n->", + "output": "{\"text\": \"battery life is horrible though .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is so cool and the service is prompt and curtious .\n->The place is so cool and the service is prompt and curtious .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: constantly got the blue screen , already tryed everything to fix it .\n->constantly got the blue screen , already tryed everything to fix it .\n[{'aspect': 'blue screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: easy to use and set up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neasy to use and set up .\n->", + "output": "{\"text\": \"easy to use and set up .\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: two stars because the current laptop i have works great .\n->two stars because the current laptop i have works great .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Authentic Pakistani food .\n->Authentic Pakistani food .\n[{'aspect': 'Pakistani food', 'opinion': 'Authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: sorry not a fan of windows 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsorry not a fan of windows 10 .\n->", + "output": "{\"text\": \"sorry not a fan of windows 10 .\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n->i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n[{'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n->it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: nice little laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice little laptop .\n->", + "output": "{\"text\": \"nice little laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent computer , better than expected .\n->excellent computer , better than expected .\n[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: not happy with this one .\n->not happy with this one .\n[{'aspect': 'NULL', 'opinion': 'not happy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: nice laptop for every day use , with some extra features .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice laptop for every day use , with some extra features .\n->", + "output": "{\"text\": \"nice laptop for every day use , with some extra features .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: no backlighting on the keyboard .\n->no backlighting on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: my only issue is that the fan is very noisy and gets stuck at times , causing worry about the laptop getting overheated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy only issue is that the fan is very noisy and gets stuck at times , causing worry about the laptop getting overheated .\n->", + "output": "{\"text\": \"my only issue is that the fan is very noisy and gets stuck at times , causing worry about the laptop getting overheated .\", \"labels\": \"[{'aspect': 'fan', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n->all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n[{'aspect': 'greek and cypriot dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'gyro', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: salads were fantastic .\n->salads were fantastic .\n[{'aspect': 'salads', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n->", + "output": "{\"text\": \"i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apple should be embarrassed .\n->apple should be embarrassed .\n[{'aspect': 'apple', 'opinion': 'embarrassed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: update : i have had this computer for about 3 months now , and it is full of problems .\n->update : i have had this computer for about 3 months now , and it is full of problems .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: really happy with this laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreally happy with this laptop !\n->", + "output": "{\"text\": \"really happy with this laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was bad , the food took to forever to come , we sat on the upper level .\n->The service was bad , the food took to forever to come , we sat on the upper level .\n[{'aspect': 'service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Love YUKA .\n->Love YUKA .\n[{'aspect': 'YUKA', 'opinion': 'Love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i just received this product about an hour or so ago .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just received this product about an hour or so ago .\n->", + "output": "{\"text\": \"i just received this product about an hour or so ago .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve found the touch screen is pretty handy .\n->i ' ve found the touch screen is pretty handy .\n[{'aspect': 'touch screen', 'opinion': 'handy', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: we , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n->we , there were four of us , arrived at noon - the place was empty - and the staff acted like we were imposing on them and they were very rude .\n[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i have yet to update the drives on it or try a game but so far its a good machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have yet to update the drives on it or try a game but so far its a good machine .\n->", + "output": "{\"text\": \"i have yet to update the drives on it or try a game but so far its a good machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n->One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n[{'aspect': 'menu', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after seven months , the usb - c ports stopped charging .\n->after seven months , the usb - c ports stopped charging .\n[{'aspect': 'usb - c ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: i recommend buying it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni recommend buying it .\n->", + "output": "{\"text\": \"i recommend buying it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Le Pere Pinard has a $ 15 pre-theater menu that is outstanding .\n->Le Pere Pinard has a $ 15 pre-theater menu that is outstanding .\n[{'aspect': 'pre-theater menu', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: none of this means the acer chromebook 14 is a bad chromebook ; on the contrary , it is an excellent chromebook !\n->none of this means the acer chromebook 14 is a bad chromebook ; on the contrary , it is an excellent chromebook !\n[{'aspect': 'acer chromebook 14', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: very nice i love it it work very well even i instal gta 5 it run but not enoght video card mb but run at all\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery nice i love it it work very well even i instal gta 5 it run but not enoght video card mb but run at all\n->", + "output": "{\"text\": \"very nice i love it it work very well even i instal gta 5 it run but not enoght video card mb but run at all\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'video card', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like cafe noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->i like cafe noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'cafe noir', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n->Meanwhile , the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil , gratis .\n[{'aspect': 'turnip soup with pureed basil', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very good purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good purchase .\n->", + "output": "{\"text\": \"very good purchase .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After we got our sashimi order , I could not believe how small the portions were !\n->After we got our sashimi order , I could not believe how small the portions were !\n[{'aspect': 'sashimi', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: avoid this place !\n->avoid this place !\n[{'aspect': 'place', 'opinion': 'avoid', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the price was great during prime days .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe price was great during prime days .\n->", + "output": "{\"text\": \"the price was great during prime days .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My roommate and I LOVE this place .\n->My roommate and I LOVE this place .\n[{'aspect': 'place', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n->we went around 9 : 30 on a friday and it had died down a bit by then so the service was great !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: works well so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks well so far .\n->", + "output": "{\"text\": \"works well so far .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i recieved the item i was amazed at the quality of it .\n->when i recieved the item i was amazed at the quality of it .\n[{'aspect': 'item', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: it is fast and lightweight .\n->it is fast and lightweight .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'it', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: price and product , outstanding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprice and product , outstanding .\n->", + "output": "{\"text\": \"price and product , outstanding .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s lightweight , sleek , and sexy as hell .\n->it ' s lightweight , sleek , and sexy as hell .\n[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sexy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n->Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n[{'aspect': 'tanks', 'opinion': 'sad-looking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tanks', 'opinion': 'clear', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'healthy-looking', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i like the size but dislike the battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like the size but dislike the battery life .\n->", + "output": "{\"text\": \"i like the size but dislike the battery life .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'dislike', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very disappointed in this machine .\n->very disappointed in this machine .\n[{'aspect': 'machine', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Great roofdeck , nice group of 30 somethings , but no music , kind of quiet .\n->Great roofdeck , nice group of 30 somethings , but no music , kind of quiet .\n[{'aspect': 'roofdeck', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'quiet', 'polarity': 'negative', 'category': 'NULL'}]\ntext: still learning but it ' s a good computer and a great deal\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstill learning but it ' s a good computer and a great deal\n->", + "output": "{\"text\": \"still learning but it ' s a good computer and a great deal\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked-to-perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: very odd operating system .\n->very odd operating system .\n[{'aspect': 'operating system', 'opinion': 'odd', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\ntext: it look like a professional laptop but overrall get this laptop you wo n ' t regret it , no negativity about it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit look like a professional laptop but overrall get this laptop you wo n ' t regret it , no negativity about it\n->", + "output": "{\"text\": \"it look like a professional laptop but overrall get this laptop you wo n ' t regret it , no negativity about it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n->Two people in our party felt like something else , and Volare immediately obliged with two great dishes that were not in their regular menu .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'regular', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: skip this restaurant , it ' s a big disappointment .\n->skip this restaurant , it ' s a big disappointment .\n[{'aspect': 'restaurant', 'opinion': 'skip', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: great computer for the price\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat computer for the price\n->", + "output": "{\"text\": \"great computer for the price\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is my first time writing a review for a restaurant because the food and service was excellent .\n->This is my first time writing a review for a restaurant because the food and service was excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is a good product to buy .\n->it is a good product to buy .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: works well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks well .\n->", + "output": "{\"text\": \"works well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bruschetta and panini 's are so yummy !\n->The bruschetta and panini 's are so yummy !\n[{'aspect': 'bruschetta', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'panini', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: A cool place to hang with your friends for a couple of healthy drinks and desserts .\n->A cool place to hang with your friends for a couple of healthy drinks and desserts .\n[{'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'healthy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great laptop , very quick and efficient for work and casual gaming , got over 200 fps for steam games .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat laptop , very quick and efficient for work and casual gaming , got over 200 fps for steam games .\n->", + "output": "{\"text\": \"great laptop , very quick and efficient for work and casual gaming , got over 200 fps for steam games .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'efficient', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n->Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Planet Thailand', 'opinion': 'hit', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: even my indian friend could n ' t believe how good and tasty everything was .\n->even my indian friend could n ' t believe how good and tasty everything was .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: great speed and storage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat speed and storage .\n->", + "output": "{\"text\": \"great speed and storage .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: yes , it ' s absolute garbage .\n->yes , it ' s absolute garbage .\n[{'aspect': 'NULL', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .\n->Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i ' ve had it since may and enjoy it\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had it since may and enjoy it\n->", + "output": "{\"text\": \"i ' ve had it since may and enjoy it\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i completely recommend casa la femme for any special occasion and to really impress your date .\n->i completely recommend casa la femme for any special occasion and to really impress your date .\n[{'aspect': 'casa la femme', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: I did n't complain , I liked the atmosphere so much .\n->I did n't complain , I liked the atmosphere so much .\n[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 1 , the touchscreen stopped working after 6 months for finger presses .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n1 , the touchscreen stopped working after 6 months for finger presses .\n->", + "output": "{\"text\": \"1 , the touchscreen stopped working after 6 months for finger presses .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the location , the prices are very reasonable .\n->For the location , the prices are very reasonable .\n[{'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'location', 'opinion': 'reasonable', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: this place is great .\n->this place is great .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: samsung keeps telling me that the serial number is invalid .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsamsung keeps telling me that the serial number is invalid .\n->", + "output": "{\"text\": \"samsung keeps telling me that the serial number is invalid .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: we were n ' t !\n->we were n ' t !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: samsung ' s warranty process was not working properly for this device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsamsung ' s warranty process was not working properly for this device .\n->", + "output": "{\"text\": \"samsung ' s warranty process was not working properly for this device .\", \"labels\": \"[{'aspect': \"samsung ' s warranty\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have not fully tested battery life but it seems to last about as long as advertised .\n->i have not fully tested battery life but it seems to last about as long as advertised .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n->The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n[{'aspect': 'waitstaff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'polite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: when i called they would not accept the serial number .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i called they would not accept the serial number .\n->", + "output": "{\"text\": \"when i called they would not accept the serial number .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: product worked great until it randomly stopped charging .\n->product worked great until it randomly stopped charging .\n[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: so far i absolutely love it .\n->so far i absolutely love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: don ' t buy it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndon ' t buy it .\n->", + "output": "{\"text\": \"don ' t buy it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections .\n->We were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections .\n[{'aspect': 'staff', 'opinion': 'enthusiastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'manager', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I have eaten at Saul , many times , the food is always consistently , outrageously good .\n->I have eaten at Saul , many times , the food is always consistently , outrageously good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: do not buy it like me , a samsung fan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not buy it like me , a samsung fan .\n->", + "output": "{\"text\": \"do not buy it like me , a samsung fan .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life short\n->battery life short\n[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: loses wifi connection every hour .\n->loses wifi connection every hour .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n->", + "output": "{\"text\": \"this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n->i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n[{'aspect': 'usb - c charger', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i took one look at the chicken and i was appalled .\n->i took one look at the chicken and i was appalled .\n[{'aspect': 'chicken', 'opinion': 'appalled', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: i got a replacement unit and it had the same issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got a replacement unit and it had the same issue .\n->", + "output": "{\"text\": \"i got a replacement unit and it had the same issue .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then suddenly it needs a software update which made my laptop crash .\n->then suddenly it needs a software update which made my laptop crash .\n[{'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: samsung has not been helpful in getting this fixed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsamsung has not been helpful in getting this fixed .\n->", + "output": "{\"text\": \"samsung has not been helpful in getting this fixed .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'not been helpful', 'polarity': 'negative', 'category': 'COMPANY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you are in need of a reliable laptop that is lightweight , fast , and convertible , i highly recommend the asus c302 !\n->if you are in need of a reliable laptop that is lightweight , fast , and convertible , i highly recommend the asus c302 !\n[{'aspect': 'asus c302', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus c302', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus c302', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus c302', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus c302', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'laptop', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n->the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n[{'aspect': 'startup time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: it makes the entire device unusable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit makes the entire device unusable .\n->", + "output": "{\"text\": \"it makes the entire device unusable .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n->it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n->Not the greatest sushi place , but excellent for a $ 19.95 all you can eat .\n[{'aspect': 'sushi place', 'opinion': 'Not the greatest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi place', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: ridiculous for something so expensive , some internet research shows numerous people having this issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nridiculous for something so expensive , some internet research shows numerous people having this issue .\n->", + "output": "{\"text\": \"ridiculous for something so expensive , some internet research shows numerous people having this issue .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'ridiculous', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Mermaid Inn is an overall good restaurant with really good seafood .\n->Mermaid Inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Mermaid Inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Despite the fact that the space is large , they 've overcrowded the floor with tables .\n->Despite the fact that the space is large , they 've overcrowded the floor with tables .\n[{'aspect': 'space', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'overcrowded', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\n->", + "output": "{\"text\": \"i like everything about the device , but the beautiful touchscreen ended up malfunctioning about 6 - months after purchase .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was average or above including some surprising tasty dishes .\n->the food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Besides having the table we had been promised given to other restaurant patrons twice before we were actually seated , we were served dishes we had n't ordered three times , received one of our orders 20 minutes after the rest of the table had been served ( and that order was undercooked ) , and charged $ 45 more than we should have been on our bill .\n->Besides having the table we had been promised given to other restaurant patrons twice before we were actually seated , we were served dishes we had n't ordered three times , received one of our orders 20 minutes after the rest of the table had been served ( and that order was undercooked ) , and charged $ 45 more than we should have been on our bill .\n[{'aspect': 'dishes', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: samsung rma ' d the device to replace the screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsamsung rma ' d the device to replace the screen .\n->", + "output": "{\"text\": \"samsung rma ' d the device to replace the screen .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: awesome\n->awesome\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i had a huge pastrami sandwich on a roll .\n->i had a huge pastrami sandwich on a roll .\n[{'aspect': 'pastrami sandwich on a roll', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the solution i am trying for now is to disable the touch screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe solution i am trying for now is to disable the touch screen .\n->", + "output": "{\"text\": \"the solution i am trying for now is to disable the touch screen .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We love the food , drinks , and atmosphere !\n->We love the food , drinks , and atmosphere !\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: keyboard is fantastic .\n->keyboard is fantastic .\n[{'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: hot / dead pixels on screen after 4 months use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhot / dead pixels on screen after 4 months use .\n->", + "output": "{\"text\": \"hot / dead pixels on screen after 4 months use .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer can not repair whatever disc issues it has .\n->the computer can not repair whatever disc issues it has .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: keyboard is fantastic .\n->keyboard is fantastic .\n[{'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the pro worked great until now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pro worked great until now .\n->", + "output": "{\"text\": \"the pro worked great until now .\", \"labels\": \"[{'aspect': 'pro', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speakers are not great , but bluetooth connection to an external speaker is standard these days and it ' s how we watch movies .\n->the speakers are not great , but bluetooth connection to an external speaker is standard these days and it ' s how we watch movies .\n[{'aspect': 'bluetooth connection', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n->i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n[{'aspect': 'machine', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: now there are pixels on the screen not working , and they are multiplying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow there are pixels on the screen not working , and they are multiplying .\n->", + "output": "{\"text\": \"now there are pixels on the screen not working , and they are multiplying .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: try everything for that matter , it is all good .\n->try everything for that matter , it is all good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the pixels are currently stuck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pixels are currently stuck .\n->", + "output": "{\"text\": \"the pixels are currently stuck .\", \"labels\": \"[{'aspect': 'pixels', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n->so now i ' m stuck with this dog but i ' m going to do all i can to get out the word that asus support is not the customer ' s friend : it ' s the customer ' s cross to bear .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: traditional french decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n->traditional french decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n[{'aspect': 'traditional french decour', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'hall', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: i will see if samsung honors their warrnty , if so , i will probably change / update this review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will see if samsung honors their warrnty , if so , i will probably change / update this review .\n->", + "output": "{\"text\": \"i will see if samsung honors their warrnty , if so , i will probably change / update this review .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worked as it should .\n->worked as it should .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n->the boths are not as small as some of the reviews make them out to look they ' re perfect for 2 people .\n[{'aspect': 'boths', 'opinion': 'not as small', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'boths', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the chromebook is an amazing product with one huge glaring flaw - you can not use the sd card with any android apps , nor use android apps with wifi tethering or anything reaching ` ` outside the sandbox ` ` .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook is an amazing product with one huge glaring flaw - you can not use the sd card with any android apps , nor use android apps with wifi tethering or anything reaching ` ` outside the sandbox ` ` .\n->", + "output": "{\"text\": \"the chromebook is an amazing product with one huge glaring flaw - you can not use the sd card with any android apps , nor use android apps with wifi tethering or anything reaching ` ` outside the sandbox ` ` .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sd card', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the replacement i got was much better , but still too slow for my expectations .\n->the replacement i got was much better , but still too slow for my expectations .\n[{'aspect': 'replacement', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: Good , fast service .\n->Good , fast service .\n[{'aspect': 'service', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\ntext: oops , i didn ' t anticipate android is crippled on here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noops , i didn ' t anticipate android is crippled on here .\n->", + "output": "{\"text\": \"oops , i didn ' t anticipate android is crippled on here .\", \"labels\": \"[{'aspect': 'android', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use it for gaming and it runs rocket league at max graphics and it looks amazing !\n->i use it for gaming and it runs rocket league at max graphics and it looks amazing !\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: I love and I know gourmet food by excellence !\n->I love and I know gourmet food by excellence !\n[{'aspect': 'gourmet food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'gourmet food', 'opinion': 'excellence', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you are wanting a beautiful screen that beats all other tablets for color range and matches samsung ' s galaxy tab s3 ( top android tablet ) , but if you absolutely must have a keyboard , don ' t buy this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are wanting a beautiful screen that beats all other tablets for color range and matches samsung ' s galaxy tab s3 ( top android tablet ) , but if you absolutely must have a keyboard , don ' t buy this .\n->", + "output": "{\"text\": \"if you are wanting a beautiful screen that beats all other tablets for color range and matches samsung ' s galaxy tab s3 ( top android tablet ) , but if you absolutely must have a keyboard , don ' t buy this .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worked as it should .\n->worked as it should .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: we did arrive late for our reservation so i can not complain too much about the wait for a table .\n->we did arrive late for our reservation so i can not complain too much about the wait for a table .\n[{'aspect': 'wait', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: i am terribly disappointed with obviously a poor qc by samsung .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am terribly disappointed with obviously a poor qc by samsung .\n->", + "output": "{\"text\": \"i am terribly disappointed with obviously a poor qc by samsung .\", \"labels\": \"[{'aspect': 'qc', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}, {'aspect': 'qc', 'opinion': 'poor', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n->Unfortunately , the food is outstanding , but everything else about this restaurant is the pits .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n->i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: unfortunately , on day 2 since i received the chromebook pro , when i flipped the lid to turn it into a tablet / tent mode , screen started flickering all over the place .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunfortunately , on day 2 since i received the chromebook pro , when i flipped the lid to turn it into a tablet / tent mode , screen started flickering all over the place .\n->", + "output": "{\"text\": \"unfortunately , on day 2 since i received the chromebook pro , when i flipped the lid to turn it into a tablet / tent mode , screen started flickering all over the place .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n->nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n[{'aspect': 'mac os x', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n->chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n[{'aspect': 'chromebooks', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreturning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n->", + "output": "{\"text\": \"returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this one !\n->i love this one !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Great Indian food and the service is incredible .\n->Great Indian food and the service is incredible .\n[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\ntext: to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n->", + "output": "{\"text\": \"to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'doubtful', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: easy to use and set up .\n->easy to use and set up .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: The service was a bit slow , but they were very friendly .\n->The service was a bit slow , but they were very friendly .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'negative', 'category': 'NULL'}]\ntext: no matter where on earth , you can get your apple product repaired .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno matter where on earth , you can get your apple product repaired .\n->", + "output": "{\"text\": \"no matter where on earth , you can get your apple product repaired .\", \"labels\": \"[{'aspect': 'apple product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really like this chromebook .\n->i really like this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the core m3 allows this system to get fast and to stay quiet .\n->the core m3 allows this system to get fast and to stay quiet .\n[{'aspect': 'core m3', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'core m3', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: in short , i would be very cautious as to ordering this product given the obvious history of quality control issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin short , i would be very cautious as to ordering this product given the obvious history of quality control issues .\n->", + "output": "{\"text\": \"in short , i would be very cautious as to ordering this product given the obvious history of quality control issues .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'cautious', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had it since may and enjoy it\n->i ' ve had it since may and enjoy it\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Grilled whole fish wonderful , great spicing .\n->Grilled whole fish wonderful , great spicing .\n[{'aspect': 'fish', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n->", + "output": "{\"text\": \"wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer lives up to its expectations .\n->this computer lives up to its expectations .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: how is this palce still open ?\n->how is this palce still open ?\n[{'aspect': 'palce', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: first and foremost , lets talk about the infamous keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst and foremost , lets talk about the infamous keyboard .\n->", + "output": "{\"text\": \"first and foremost , lets talk about the infamous keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'infamous', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: baluchi ' s has solid food and a nice decor at reasonable prices .\n->baluchi ' s has solid food and a nice decor at reasonable prices .\n[{'aspect': \"baluchi ' s\", 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: nice chromebook .\n->nice chromebook .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: yes , it ' s absolute garbage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyes , it ' s absolute garbage .\n->", + "output": "{\"text\": \"yes , it ' s absolute garbage .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: their designs are made for phones and on this huge screen , they are palpitated .\n->their designs are made for phones and on this huge screen , they are palpitated .\n[{'aspect': 'designs', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: * excellent form factor , extremely portable while remaining a serious pro computer\n->* excellent form factor , extremely portable while remaining a serious pro computer\n[{'aspect': 'pro computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro computer', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: i ' m surprised people are settling for claiming ` ` it ' s good enough ` ` or ` ` gets the job done ` ` when every time i use it , i ' m constantly reminded of how i should have just sprang for the pixelbook when it was on sale for $ 699 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m surprised people are settling for claiming ` ` it ' s good enough ` ` or ` ` gets the job done ` ` when every time i use it , i ' m constantly reminded of how i should have just sprang for the pixelbook when it was on sale for $ 699 .\n->", + "output": "{\"text\": \"i ' m surprised people are settling for claiming ` ` it ' s good enough ` ` or ` ` gets the job done ` ` when every time i use it , i ' m constantly reminded of how i should have just sprang for the pixelbook when it was on sale for $ 699 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: once i got it set up it has been very nice .\n->once i got it set up it has been very nice .\n[{'aspect': 'set up', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: camera is sd but not a problem .\n->camera is sd but not a problem .\n[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\ntext: i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\n->", + "output": "{\"text\": \"i ' ve owned / used dozens upon dozens of keyboards and there is absolutely no questions this one is the worst .\", \"labels\": \"[{'aspect': 'keyboards', 'opinion': 'worst', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n->i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n[{'aspect': 'NULL', 'opinion': 'biased', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard is really nice - .\n->the keyboard is really nice - .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: my 2nd issues is with it ' s performance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy 2nd issues is with it ' s performance .\n->", + "output": "{\"text\": \"my 2nd issues is with it ' s performance .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The ambience is authentic and relaxing and we have always received attentive and prompt service .\n->The ambience is authentic and relaxing and we have always received attentive and prompt service .\n[{'aspect': 'ambience', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n->despite the confusing mirrors this will likely be my go - to for modern japanese food for the foreseeable future .\n[{'aspect': 'modern japanese food', 'opinion': 'go - to for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mirrors', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: even in the early rounds the frames are heavily compromised and the entire game feels very sluggish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven in the early rounds the frames are heavily compromised and the entire game feels very sluggish .\n->", + "output": "{\"text\": \"even in the early rounds the frames are heavily compromised and the entire game feels very sluggish .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'sluggish', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n->for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: * battery - i can not really speak to the battery life yet , but as a power user with lots of tabs and several apps open , it is giving me a days use 5 + hours of use so far .\n->* battery - i can not really speak to the battery life yet , but as a power user with lots of tabs and several apps open , it is giving me a days use 5 + hours of use so far .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i ' ll give the chromebook pro an extra star for its best asset , the screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ll give the chromebook pro an extra star for its best asset , the screen .\n->", + "output": "{\"text\": \"i ' ll give the chromebook pro an extra star for its best asset , the screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'best', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' d say the only drawback might be the speakers .\n->i ' d say the only drawback might be the speakers .\n[{'aspect': 'speakers', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it does indeed look excellent !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit does indeed look excellent !\n->", + "output": "{\"text\": \"it does indeed look excellent !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: too bad i had paid an extra $ 2 for the stone bowl .\n->too bad i had paid an extra $ 2 for the stone bowl .\n[{'aspect': 'stone bowl', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: I had been a regular due to the consistently good food and ease of getting a table .\n->I had been a regular due to the consistently good food and ease of getting a table .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'ease', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s also worth noting my unit came with a floating trackpad , with initial play before the actually click .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s also worth noting my unit came with a floating trackpad , with initial play before the actually click .\n->", + "output": "{\"text\": \"it ' s also worth noting my unit came with a floating trackpad , with initial play before the actually click .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The best Chicken pad tai , I 've ever had .\n->The best Chicken pad tai , I 've ever had .\n[{'aspect': 'Chicken pad tai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The staff offers impeccable service .\n->The staff offers impeccable service .\n[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you are indifferent about the screen , do not buy this !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are indifferent about the screen , do not buy this !\n->", + "output": "{\"text\": \"if you are indifferent about the screen , do not buy this !\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n->There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n[{'aspect': 'Blue Point oysters', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: they would help me only if i bought an ongoing support contract with them .\n->they would help me only if i bought an ongoing support contract with them .\n[{'aspect': 'support contract', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: i just got this chromebook last week and since then i ' ve had to do a hard reboot once and it has reset itself to factory settings about 5 times .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just got this chromebook last week and since then i ' ve had to do a hard reboot once and it has reset itself to factory settings about 5 times .\n->", + "output": "{\"text\": \"i just got this chromebook last week and since then i ' ve had to do a hard reboot once and it has reset itself to factory settings about 5 times .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n->During the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: I have eaten at Saul , many times , the food is always consistently , outrageously good .\n->I have eaten at Saul , many times , the food is always consistently , outrageously good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: there is a power button on the side which i have accidentally hit quite a few times .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is a power button on the side which i have accidentally hit quite a few times .\n->", + "output": "{\"text\": \"there is a power button on the side which i have accidentally hit quite a few times .\", \"labels\": \"[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the filet mignon dish was superb !\n->the filet mignon dish was superb !\n[{'aspect': 'filet mignon dish', 'opinion': 'superb', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i haven ' t abused this at all , and i noticed a small crack in the screen today and it ' s starting to spider out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni haven ' t abused this at all , and i noticed a small crack in the screen today and it ' s starting to spider out .\n->", + "output": "{\"text\": \"i haven ' t abused this at all , and i noticed a small crack in the screen today and it ' s starting to spider out .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Another plus is most of the entrees are approx .\n->Another plus is most of the entrees are approx .\n[{'aspect': 'entrees', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n->original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n[{'aspect': 'screen', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\ntext: so $ 500 bucks down the drain as i ' m sure that isn ' t covered by any warranties .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso $ 500 bucks down the drain as i ' m sure that isn ' t covered by any warranties .\n->", + "output": "{\"text\": \"so $ 500 bucks down the drain as i ' m sure that isn ' t covered by any warranties .\", \"labels\": \"[{'aspect': 'warranties', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'WARRANTY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: was able to get past setting up the log in info , but then once you log in the screen continuously goes black and comes back on and goes black and comes back on ; continuous cycle .\n->was able to get past setting up the log in info , but then once you log in the screen continuously goes black and comes back on and goes black and comes back on ; continuous cycle .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n->lastly , the touch pad , even after lowering the sensitivity , it ' s still pretty sensitive and the click pads are stiff , so it doesn ' t tap on clicks , sometimes i have to click on it hard enough , so that ' s a bit annoying .\n[{'aspect': 'touch pad', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: with it being a touch screen it surprised me that it cracked and i have no idea how .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith it being a touch screen it surprised me that it cracked and i have no idea how .\n->", + "output": "{\"text\": \"with it being a touch screen it surprised me that it cracked and i have no idea how .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'surprised', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the portions are small but being that the food was so good makes up for that .\n->the portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: when in use , the lower screen is flickering .\n->when in use , the lower screen is flickering .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: my screen stayed black more than it was on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy screen stayed black more than it was on .\n->", + "output": "{\"text\": \"my screen stayed black more than it was on .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he likes it\n->he likes it\n[{'aspect': 'NULL', 'opinion': 'likes', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: their pad penang is delicious and everything else is fantastic .\n->their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: samsung needs to fix that issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsamsung needs to fix that issue .\n->", + "output": "{\"text\": \"samsung needs to fix that issue .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n->the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: wonderful at holiday time .\n->wonderful at holiday time .\n[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: possible note , it was very light .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npossible note , it was very light .\n->", + "output": "{\"text\": \"possible note , it was very light .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n->My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n[{'aspect': 'cheese', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: overall , it works well and is easy to use .\n->overall , it works well and is easy to use .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: while this thing is gorgeous and the perfect size for what i was looking for , my initial display died after not even 12 hours of use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile this thing is gorgeous and the perfect size for what i was looking for , my initial display died after not even 12 hours of use .\n->", + "output": "{\"text\": \"while this thing is gorgeous and the perfect size for what i was looking for , my initial display died after not even 12 hours of use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really needed this product for work and had big plans for using it ' s features / versatility .\n->i really needed this product for work and had big plans for using it ' s features / versatility .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n->at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n[{'aspect': 'model', 'opinion': 'afraid', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'amazon', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: except , you know , when it decided to become unusable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcept , you know , when it decided to become unusable .\n->", + "output": "{\"text\": \"except , you know , when it decided to become unusable .\", \"labels\": \"[{'aspect': 'it', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n->it may be a bit packed on weekends , but the vibe is good and it is the best french food you will find in the area .\n[{'aspect': 'vibe', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'french food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The food and staff always surprise me with the new heights they are taken to .\n->The food and staff always surprise me with the new heights they are taken to .\n[{'aspect': 'food', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i just wanted to make it known my personal experiences with the device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just wanted to make it known my personal experiences with the device .\n->", + "output": "{\"text\": \"i just wanted to make it known my personal experiences with the device .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we will definitely go back .\n->we will definitely go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: fantastic place .\n->fantastic place .\n[{'aspect': 'place', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: awesome display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nawesome display .\n->", + "output": "{\"text\": \"awesome display .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is great .\n->the build quality is great .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: guess what , i waited for twenty minutes before she came over and when she finally did , she says , ` ` oh well , i wish you would have said something earlier ' ' no apology , nothing .\n->guess what , i waited for twenty minutes before she came over and when she finally did , she says , ` ` oh well , i wish you would have said something earlier ' ' no apology , nothing .\n[{'aspect': 'NULL', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: fast boot and reliable os .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast boot and reliable os .\n->", + "output": "{\"text\": \"fast boot and reliable os .\", \"labels\": \"[{'aspect': 'boot', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'boot', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the scallion pancakes and fried dumplings were nothing out of the ordinary .\n->the scallion pancakes and fried dumplings were nothing out of the ordinary .\n[{'aspect': 'scallion pancakes', 'opinion': 'ordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'fried dumplings', 'opinion': 'nothing out of the ordinary', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The bagels are fabulous .\n->The bagels are fabulous .\n[{'aspect': 'bagels', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: chassis could be studier and keyboard and trackpad very average and lack of backlight is a deal breaker for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchassis could be studier and keyboard and trackpad very average and lack of backlight is a deal breaker for me .\n->", + "output": "{\"text\": \"chassis could be studier and keyboard and trackpad very average and lack of backlight is a deal breaker for me .\", \"labels\": \"[{'aspect': 'chassis', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'average', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'trackpad', 'opinion': 'average', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'backlight', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was great as well .\n->The service was great as well .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: such a disappointment . . .\n->such a disappointment . . .\n[{'aspect': 'NULL', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: mouse track pad is non standard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmouse track pad is non standard .\n->", + "output": "{\"text\": \"mouse track pad is non standard .\", \"labels\": \"[{'aspect': 'mouse track pad', 'opinion': 'non standard', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n->my personal favorite is an everything bagel with lox spread , but all the bagles are unbeliavably good .\n[{'aspect': 'bagel with lox spread', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagles', 'opinion': 'unbeliavably good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n->this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n[{'aspect': 'computer', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it ' s hard to navigate and i had to buy a usb mouse and a usb connector .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s hard to navigate and i had to buy a usb mouse and a usb connector .\n->", + "output": "{\"text\": \"it ' s hard to navigate and i had to buy a usb mouse and a usb connector .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the overall device is slim and lightweight .\n->the overall device is slim and lightweight .\n[{'aspect': 'device', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the food tasted very good .\n->the food tasted very good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i say this device is worth no more than $ 300 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni say this device is worth no more than $ 300 .\n->", + "output": "{\"text\": \"i say this device is worth no more than $ 300 .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'worth', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not impressed with the food .\n->Not impressed with the food .\n[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: there was a small wait , but shorter than i expected .\n->there was a small wait , but shorter than i expected .\n[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\n->", + "output": "{\"text\": \"it is very limited and the competition for an actual usable operating system that can do a lot more than just browse the internet , are available .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'limited', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'operating system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i guess the computer is quite okay for the price they are asking for it .\n->i guess the computer is quite okay for the price they are asking for it .\n[{'aspect': 'computer', 'opinion': 'okay', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Went here last night - nice decor , good service , but the food was surprisingly excellent .\n->Went here last night - nice decor , good service , but the food was surprisingly excellent .\n[{'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the quality is is meh .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe quality is is meh .\n->", + "output": "{\"text\": \"the quality is is meh .\", \"labels\": \"[{'aspect': 'the quality', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You must try the shrimp appetizers .\n->You must try the shrimp appetizers .\n[{'aspect': 'shrimp appetizers', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the new computer failed again with the same error .\n->the new computer failed again with the same error .\n[{'aspect': 'computer', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: its cheap plastic and honestly , the keyboard its really bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits cheap plastic and honestly , the keyboard its really bad .\n->", + "output": "{\"text\": \"its cheap plastic and honestly , the keyboard its really bad .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the service is horrid !\n->but the service is horrid !\n[{'aspect': 'service', 'opinion': 'horrid', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: my daughter ordered the chromebook with her graduation money and she loves it .\n->my daughter ordered the chromebook with her graduation money and she loves it .\n[{'aspect': 'chromebook', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it simply feels like a cheap samsung tablet with a keyboard attached .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit simply feels like a cheap samsung tablet with a keyboard attached .\n->", + "output": "{\"text\": \"it simply feels like a cheap samsung tablet with a keyboard attached .\", \"labels\": \"[{'aspect': 'samsung tablet', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good experience\n->good experience\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Great for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening .\n->Great for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening .\n[{'aspect': 'waiter', 'opinion': 'SUPER HAPPY', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the playstore its immature .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe playstore its immature .\n->", + "output": "{\"text\": \"the playstore its immature .\", \"labels\": \"[{'aspect': 'playstore', 'opinion': 'immature', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: purchased as a mothers day gift but i ' ve come to respect the quality and performance of lenovo .\n->purchased as a mothers day gift but i ' ve come to respect the quality and performance of lenovo .\n[{'aspect': 'lenovo', 'opinion': 'respect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'lenovo', 'opinion': 'respect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: their designs are made for phones and on this huge screen , they are palpitated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntheir designs are made for phones and on this huge screen , they are palpitated .\n->", + "output": "{\"text\": \"their designs are made for phones and on this huge screen , they are palpitated .\", \"labels\": \"[{'aspect': 'designs', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i have been to roth ' s twice and both times were very disappointing .\n->i have been to roth ' s twice and both times were very disappointing .\n[{'aspect': \"roth ' s\", 'opinion': 'disappointing .', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: this is quite honest , a very expensive chrome browser .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is quite honest , a very expensive chrome browser .\n->", + "output": "{\"text\": \"this is quite honest , a very expensive chrome browser .\", \"labels\": \"[{'aspect': 'chrome browser', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was the first place we ate on our first trip to new york , and it will be the last place we stop as we head out of town on our next trip to new york .\n->it was the first place we ate on our first trip to new york , and it will be the last place we stop as we head out of town on our next trip to new york .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n->I must say it 's a little pricey for the food because it was not as spectacular as the view .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i do not recommend it at this price point .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do not recommend it at this price point .\n->", + "output": "{\"text\": \"i do not recommend it at this price point .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food always tastes fresh and served promptly .\n->The food always tastes fresh and served promptly .\n[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'served', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The waitress was very patient with us and the food is phenomenal !\n->The waitress was very patient with us and the food is phenomenal !\n[{'aspect': 'waitress', 'opinion': 'patient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i would however spend $ 300 on this device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would however spend $ 300 on this device .\n->", + "output": "{\"text\": \"i would however spend $ 300 on this device .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There was a small wait , but shorter than I expected .\n->There was a small wait , but shorter than I expected .\n[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n->Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n[{'aspect': 'fruit of the oil', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'sweetness', 'polarity': 'positive', 'category': 'NULL'}]\ntext: its current price is not worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits current price is not worth it .\n->", + "output": "{\"text\": \"its current price is not worth it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n->One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n[{'aspect': 'menu', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n->this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n[{'aspect': 'computer', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: after a couple of weeks i started experiencing web browsing issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter a couple of weeks i started experiencing web browsing issues .\n->", + "output": "{\"text\": \"after a couple of weeks i started experiencing web browsing issues .\", \"labels\": \"[{'aspect': 'web browsing', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n->Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n[{'aspect': 'sake list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was worried about the battery life because of the reviews .\n->i was worried about the battery life because of the reviews .\n[{'aspect': 'battery life', 'opinion': 'worried', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: first occasionally and later to a point where the laptop became unusable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst occasionally and later to a point where the laptop became unusable .\n->", + "output": "{\"text\": \"first occasionally and later to a point where the laptop became unusable .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best restaurant in brooklyn\n->best restaurant in brooklyn\n[{'aspect': 'restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the problem with it is that it freezes from time to time .\n->the problem with it is that it freezes from time to time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: after some googling i realized that the wifi issue was related to bluetooth being on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter some googling i realized that the wifi issue was related to bluetooth being on .\n->", + "output": "{\"text\": \"after some googling i realized that the wifi issue was related to bluetooth being on .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wait staff is very courteous and accomodating .\n->the wait staff is very courteous and accomodating .\n[{'aspect': 'wait staff', 'opinion': 'courteous', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i can ' t really testify to its battery - life as i have not used it to the point where the battery is totally dissipated .\n->i can ' t really testify to its battery - life as i have not used it to the point where the battery is totally dissipated .\n[{'aspect': 'battery - life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n->", + "output": "{\"text\": \"stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\", \"labels\": \"[{'aspect': 'stylus', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'stylus', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great neighborhood joint .\n->great neighborhood joint .\n[{'aspect': 'joint', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it is expensive but well worth the money .\n->it is expensive but well worth the money .\n[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: 10 minutes after finishing this review started having a new issue : touch pad started acting out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n10 minutes after finishing this review started having a new issue : touch pad started acting out .\n->", + "output": "{\"text\": \"10 minutes after finishing this review started having a new issue : touch pad started acting out .\", \"labels\": \"[{'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I look forward to eating here again\n->I look forward to eating here again\n[{'aspect': 'eating', 'opinion': 'look forward', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Decor is charming .\n->Decor is charming .\n[{'aspect': 'Decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you know , i wanted to love this machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou know , i wanted to love this machine .\n->", + "output": "{\"text\": \"you know , i wanted to love this machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n->But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n[{'aspect': 'atmosphere', 'opinion': 'delightfully', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Service is great , takeout is good too .\n->Service is great , takeout is good too .\n[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'takeout', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i went in to this purchase assuming that the availability of android apps from the google play store would make this unit into a true windows alternative .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni went in to this purchase assuming that the availability of android apps from the google play store would make this unit into a true windows alternative .\n->", + "output": "{\"text\": \"i went in to this purchase assuming that the availability of android apps from the google play store would make this unit into a true windows alternative .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: photoshop also runs very well .\n->photoshop also runs very well .\n[{'aspect': 'photoshop', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Saturday , Nov. 6th I had a group from work come in with about 35 people and the staff was amazing to accomodate us .\n->Saturday , Nov. 6th I had a group from work come in with about 35 people and the staff was amazing to accomodate us .\n[{'aspect': 'staff', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: before i get to that , let me first confirm that the keyboard is indeed appallingly bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbefore i get to that , let me first confirm that the keyboard is indeed appallingly bad .\n->", + "output": "{\"text\": \"before i get to that , let me first confirm that the keyboard is indeed appallingly bad .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for an office , you can give everyone a chromebook and their environment becomes available on login and disappears when they sign - out , and the machines can also be remotely managed if you ' re a google apps customer .\n->for an office , you can give everyone a chromebook and their environment becomes available on login and disappears when they sign - out , and the machines can also be remotely managed if you ' re a google apps customer .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: The pizza was pretty good and huge .\n->The pizza was pretty good and huge .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ' what a bunch of whiners , ' i concluded about all the people who slammed the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n' what a bunch of whiners , ' i concluded about all the people who slammed the keyboard .\n->", + "output": "{\"text\": \"' what a bunch of whiners , ' i concluded about all the people who slammed the keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The best pad thai i 've ever had .\n->The best pad thai i 've ever had .\n[{'aspect': 'pad thai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is a good product at an affordable price .\n->it is a good product at an affordable price .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: so one star off for the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso one star off for the keyboard .\n->", + "output": "{\"text\": \"so one star off for the keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what was even worse was the customer service .\n->what was even worse was the customer service .\n[{'aspect': 'customer service', 'opinion': 'worse', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n->even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nneedless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n->", + "output": "{\"text\": \"needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n->This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n[{'aspect': 'Jazz', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: there is really no excuse why it can ' t have one .\n->there is really no excuse why it can ' t have one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: none of their android versions were what i would call usable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnone of their android versions were what i would call usable .\n->", + "output": "{\"text\": \"none of their android versions were what i would call usable .\", \"labels\": \"[{'aspect': 'android versions', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the jukebox plays everything from italian opera to the strokes .\n->the jukebox plays everything from italian opera to the strokes .\n[{'aspect': 'jukebox', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: after logging in to the replacement , the screen flashes black every fives seconds and restart the chrome browser .\n->after logging in to the replacement , the screen flashes black every fives seconds and restart the chrome browser .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: in short , this unit is a chromebook with a really nice display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin short , this unit is a chromebook with a really nice display .\n->", + "output": "{\"text\": \"in short , this unit is a chromebook with a really nice display .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first time user for windows 10 and it ' s pretty good .\n->first time user for windows 10 and it ' s pretty good .\n[{'aspect': 'windows 10', 'opinion': 'good', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: everyone was smiling so that made me feel welcome .\n->everyone was smiling so that made me feel welcome .\n[{'aspect': 'NULL', 'opinion': 'welcome', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: if you want to spend 5 or 6 hundred bucks for that , rock on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you want to spend 5 or 6 hundred bucks for that , rock on .\n->", + "output": "{\"text\": \"if you want to spend 5 or 6 hundred bucks for that , rock on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - keyboard isn ' t back - lit\n->- keyboard isn ' t back - lit\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i love how quick this thing is .\n->i love how quick this thing is .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: unfortunately , my chromebook that i purchased through best buy was dead on arrival .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunfortunately , my chromebook that i purchased through best buy was dead on arrival .\n->", + "output": "{\"text\": \"unfortunately , my chromebook that i purchased through best buy was dead on arrival .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n->i really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n[{'aspect': 'scallops', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mahi mahi ( on saffron risotto', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the problem is that it never charged .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe problem is that it never charged .\n->", + "output": "{\"text\": \"the problem is that it never charged .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google is amazing .\n->google is amazing .\n[{'aspect': 'google', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: Service and food is what any one would expect when spending that type of money .\n->Service and food is what any one would expect when spending that type of money .\n[{'aspect': 'Service', 'opinion': 'expect', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'expect', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the cable functions just fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe cable functions just fine .\n->", + "output": "{\"text\": \"the cable functions just fine .\", \"labels\": \"[{'aspect': 'cable', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Dessert is a joke ... dont bother\n->Dessert is a joke ... dont bother\n[{'aspect': 'Dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this device would be a good choice if it weren ' t so poorly constructed .\n->this device would be a good choice if it weren ' t so poorly constructed .\n[{'aspect': 'device', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'poorly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: the chromebook is simply dead from what i can tell .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook is simply dead from what i can tell .\n->", + "output": "{\"text\": \"the chromebook is simply dead from what i can tell .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has and does everything it should .\n->it has and does everything it should .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: And the prices were way to high for what you get .\n->And the prices were way to high for what you get .\n[{'aspect': 'prices', 'opinion': 'high', 'polarity': 'negative', 'category': 'NULL'}]\ntext: as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n->", + "output": "{\"text\": \"as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\", \"labels\": \"[{'aspect': 'cable', 'opinion': 'active', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'power led', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but now , i ' m totally satisfied with this chromebook !\n->but now , i ' m totally satisfied with this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is one of the best comfort food places in the city .\n->this is one of the best comfort food places in the city .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'comfort', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: very disappointing , samsung !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery disappointing , samsung !\n->", + "output": "{\"text\": \"very disappointing , samsung !\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the c302 is heavier in size and appearance and also significantly better in all aspects .\n->the c302 is heavier in size and appearance and also significantly better in all aspects .\n[{'aspect': 'c302', 'opinion': 'heavier', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'c302', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: great item !\n->great item !\n[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i really needed this product for work and had big plans for using it ' s features / versatility .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really needed this product for work and had big plans for using it ' s features / versatility .\n->", + "output": "{\"text\": \"i really needed this product for work and had big plans for using it ' s features / versatility .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: suddenly the laptop goes to sleep and doesn ' t wake up .\n->suddenly the laptop goes to sleep and doesn ' t wake up .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: also on this model the ssd is not replaceable .\n->also on this model the ssd is not replaceable .\n[{'aspect': 'ssd', 'opinion': 'not replaceable', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: all i got was a fancy 2lb metal slab that can only be used to swat flies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall i got was a fancy 2lb metal slab that can only be used to swat flies .\n->", + "output": "{\"text\": \"all i got was a fancy 2lb metal slab that can only be used to swat flies .\", \"labels\": \"[{'aspect': 'metal slab', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this device would be a good choice if it weren ' t so poorly constructed .\n->this device would be a good choice if it weren ' t so poorly constructed .\n[{'aspect': 'device', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'poorly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: definitely a great spot for a nice occasion or date .\n->definitely a great spot for a nice occasion or date .\n[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\n->", + "output": "{\"text\": \"apparently there is some optimizations that google has done with the op1 and pixel that allow what is called instant ink .\", \"labels\": \"[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my daughter ordered the chromebook with her graduation money and she loves it .\n->my daughter ordered the chromebook with her graduation money and she loves it .\n[{'aspect': 'chromebook', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: as everyone else says ; the keyboard is not backlit .\n->as everyone else says ; the keyboard is not backlit .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i have also noticed the pro seems to run hotter and have a noticeably shorter battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have also noticed the pro seems to run hotter and have a noticeably shorter battery life .\n->", + "output": "{\"text\": \"i have also noticed the pro seems to run hotter and have a noticeably shorter battery life .\", \"labels\": \"[{'aspect': 'pro', 'opinion': 'hotter', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'shorter', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The highly spiced chai tea was great too .\n->The highly spiced chai tea was great too .\n[{'aspect': 'chai tea', 'opinion': 'highly spiced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chai tea', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: always good drinks and service is pretty good ;\n->always good drinks and service is pretty good ;\n[{'aspect': 'drinks', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: initially the product lived up to the expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ninitially the product lived up to the expectations .\n->", + "output": "{\"text\": \"initially the product lived up to the expectations .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sake menu should not be overlooked !\n->the sake menu should not be overlooked !\n[{'aspect': 'sake menu', 'opinion': 'overlooked', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: there was no tap beer that evening , which was a disappointment .\n->there was no tap beer that evening , which was a disappointment .\n[{'aspect': 'beer', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: there were challenges with the chromebook specific apps and the google play store apps worked sometimes , but most of them had the size issue where they are only the size of a phone screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere were challenges with the chromebook specific apps and the google play store apps worked sometimes , but most of them had the size issue where they are only the size of a phone screen .\n->", + "output": "{\"text\": \"there were challenges with the chromebook specific apps and the google play store apps worked sometimes , but most of them had the size issue where they are only the size of a phone screen .\", \"labels\": \"[{'aspect': 'chromebook specific apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}, {'aspect': 'google play store apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my only complaint is that the trackpad is just awful .\n->my only complaint is that the trackpad is just awful .\n[{'aspect': 'trackpad', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n->my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n[{'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: reliability is completely terrible - i use it for work but definitely wouldn ' t buy for personal use as it ' s had to be repaired and replaced twice already - once for just momentarily stopping and then for the touch screen not working with hand input ( worked for the pen which btw is kind of useless except in a few android apps as the handwriting recognition is limited to a lame popup panel - there ' s no integration ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreliability is completely terrible - i use it for work but definitely wouldn ' t buy for personal use as it ' s had to be repaired and replaced twice already - once for just momentarily stopping and then for the touch screen not working with hand input ( worked for the pen which btw is kind of useless except in a few android apps as the handwriting recognition is limited to a lame popup panel - there ' s no integration ) .\n->", + "output": "{\"text\": \"reliability is completely terrible - i use it for work but definitely wouldn ' t buy for personal use as it ' s had to be repaired and replaced twice already - once for just momentarily stopping and then for the touch screen not working with hand input ( worked for the pen which btw is kind of useless except in a few android apps as the handwriting recognition is limited to a lame popup panel - there ' s no integration ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen looks good despite some other reviews .\n->the screen looks good despite some other reviews .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: The corned beef was tender and melted in my mouth .\n->The corned beef was tender and melted in my mouth .\n[{'aspect': 'corned beef', 'opinion': 'tender', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'corned beef', 'opinion': 'melted', 'polarity': 'positive', 'category': 'NULL'}]\ntext: also the case design is sort of rounded at both sides - a minor issue but it makes the device pop open in my purse and detracts from the ` ` feel ` ` of the device by making it feel a lot thicker than it actually is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso the case design is sort of rounded at both sides - a minor issue but it makes the device pop open in my purse and detracts from the ` ` feel ` ` of the device by making it feel a lot thicker than it actually is .\n->", + "output": "{\"text\": \"also the case design is sort of rounded at both sides - a minor issue but it makes the device pop open in my purse and detracts from the ` ` feel ` ` of the device by making it feel a lot thicker than it actually is .\", \"labels\": \"[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speaking of charges , it ' s so nice to be able to use usb c .\n->speaking of charges , it ' s so nice to be able to use usb c .\n[{'aspect': 'usb c', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n->in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n[{'aspect': 'fingerprints', 'opinion': 'dislike', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}]\ntext: horrible , purchased directly from google , dead pixel on arrival , note feature did not pick up stylus and bluetooth rarely worked .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhorrible , purchased directly from google , dead pixel on arrival , note feature did not pick up stylus and bluetooth rarely worked .\n->", + "output": "{\"text\": \"horrible , purchased directly from google , dead pixel on arrival , note feature did not pick up stylus and bluetooth rarely worked .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'pixel', 'opinion': 'dead', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - great quality build .\n->- great quality build .\n[{'aspect': 'quality build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i would highly recommend this product if you want to get into music production like myself .\n->i would highly recommend this product if you want to get into music production like myself .\n[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: sent it in , arrived back in an inadequate box ( i shipped it in the oroginal with protective foam came back in the box from samsung w / o any protection ) and 3 days after i recieved the item back the stylus fell apart .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsent it in , arrived back in an inadequate box ( i shipped it in the oroginal with protective foam came back in the box from samsung w / o any protection ) and 3 days after i recieved the item back the stylus fell apart .\n->", + "output": "{\"text\": \"sent it in , arrived back in an inadequate box ( i shipped it in the oroginal with protective foam came back in the box from samsung w / o any protection ) and 3 days after i recieved the item back the stylus fell apart .\", \"labels\": \"[{'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This big draw is the all you can sushi here for $ 19.95 !\n->This big draw is the all you can sushi here for $ 19.95 !\n[{'aspect': 'sushi', 'opinion': 'draw', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: also , because it is so thin , it gets cold very quickly and its not that filling .\n->also , because it is so thin , it gets cold very quickly and its not that filling .\n[{'aspect': 'NULL', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: customer service told me that i ' d have to buy a new one , item is still under warranty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncustomer service told me that i ' d have to buy a new one , item is still under warranty .\n->", + "output": "{\"text\": \"customer service told me that i ' d have to buy a new one , item is still under warranty .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: warm and friendly in the winter and terrific outdoor seating in the warmer months .\n->warm and friendly in the winter and terrific outdoor seating in the warmer months .\n[{'aspect': 'outdoor seating', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Not worth the prices .\n->Not worth the prices .\n[{'aspect': 'prices', 'opinion': 'worth', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i picked up the stylus and it fell apart , no drops no damage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni picked up the stylus and it fell apart , no drops no damage .\n->", + "output": "{\"text\": \"i picked up the stylus and it fell apart , no drops no damage .\", \"labels\": \"[{'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no matter where on earth , you can get your apple product repaired .\n->no matter where on earth , you can get your apple product repaired .\n[{'aspect': 'apple product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: disappointing battery life , even with light use i have to recharge every 4 - 5 hours ( at best ) .\n->disappointing battery life , even with light use i have to recharge every 4 - 5 hours ( at best ) .\n[{'aspect': 'battery life', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: samsung makes garbage and treats their customers with no respect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsamsung makes garbage and treats their customers with no respect .\n->", + "output": "{\"text\": \"samsung makes garbage and treats their customers with no respect .\", \"labels\": \"[{'aspect': 'samsung', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And they provided a delicious dessert on the house !\n->And they provided a delicious dessert on the house !\n[{'aspect': 'dessert', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the 360 - degree hinges allow me to make presentations to customers and the battery life is amazing .\n->the 360 - degree hinges allow me to make presentations to customers and the battery life is amazing .\n[{'aspect': '360 - degree hinges', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: never again will i buy samsung products and thats what i ' d suggest here .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnever again will i buy samsung products and thats what i ' d suggest here .\n->", + "output": "{\"text\": \"never again will i buy samsung products and thats what i ' d suggest here .\", \"labels\": \"[{'aspect': 'samsung products', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First went here to enjoy their garden terrace .\n->First went here to enjoy their garden terrace .\n[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n->the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n[{'aspect': 'intel rst driver', 'opinion': 'old', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: no caps lock on the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno caps lock on the keyboard .\n->", + "output": "{\"text\": \"no caps lock on the keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n->on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power', 'opinion': 'failed', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: this place had all the trimmings and i mean all .\n->this place had all the trimmings and i mean all .\n[{'aspect': 'trimmings', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: virtually every single app for it is compromised in some manner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvirtually every single app for it is compromised in some manner .\n->", + "output": "{\"text\": \"virtually every single app for it is compromised in some manner .\", \"labels\": \"[{'aspect': 'app', 'opinion': 'compromised', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my first chromebook , and so far ( about one month of use ) i like it .\n->my first chromebook , and so far ( about one month of use ) i like it .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: bought this for my daughter ' s senior year of college and she ' s very happy .\n->bought this for my daughter ' s senior year of college and she ' s very happy .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: apps have to close and be reopened to get them to a somewhat decent size .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napps have to close and be reopened to get them to a somewhat decent size .\n->", + "output": "{\"text\": \"apps have to close and be reopened to get them to a somewhat decent size .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n->looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\nExample:\ntext: it ' s pretty light , too , so it ' s easy to travel with .\n->it ' s pretty light , too , so it ' s easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: chrome os is not matured in any way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchrome os is not matured in any way .\n->", + "output": "{\"text\": \"chrome os is not matured in any way .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: problem is nothing at prune is particularly memorable .\n->problem is nothing at prune is particularly memorable .\n[{'aspect': 'prune', 'opinion': 'memorable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: more importantly i appreciate the uncanny speed to boot up or wake up .\n->more importantly i appreciate the uncanny speed to boot up or wake up .\n[{'aspect': 'speed', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: it calls itself a computer , but it ' s really not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit calls itself a computer , but it ' s really not .\n->", + "output": "{\"text\": \"it calls itself a computer , but it ' s really not .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the c302 is clean and feels solid .\n->the c302 is clean and feels solid .\n[{'aspect': 'c302', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'c302', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: customer service is difficult .\n->customer service is difficult .\n[{'aspect': 'customer service', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: it fancies itself a convertible notebook / app consuming functional tablet , but it is not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit fancies itself a convertible notebook / app consuming functional tablet , but it is not .\n->", + "output": "{\"text\": \"it fancies itself a convertible notebook / app consuming functional tablet , but it is not .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it also has enough power to multi - task .\n->it also has enough power to multi - task .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i ' m more about personality than looks , but this little thing is a looker , too .\n->i ' m more about personality than looks , but this little thing is a looker , too .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n->", + "output": "{\"text\": \"for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so i decided to purchase the asus flip , and so far it has met every one of my nitpicky demanding needs .\n->so i decided to purchase the asus flip , and so far it has met every one of my nitpicky demanding needs .\n[{'aspect': 'asus flip', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - jstorrent works nicely for any torrenting needs .\n->- jstorrent works nicely for any torrenting needs .\n[{'aspect': 'jstorrent', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: google is very concerned about arc to chromeos connections for security , etc etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoogle is very concerned about arc to chromeos connections for security , etc etc .\n->", + "output": "{\"text\": \"google is very concerned about arc to chromeos connections for security , etc etc .\", \"labels\": \"[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so i went ahead and ordered the c302ca , and after a week of use there are no issues to report .\n->so i went ahead and ordered the c302ca , and after a week of use there are no issues to report .\n[{'aspect': 'c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n->i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n[{'aspect': 'NULL', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n->", + "output": "{\"text\": \"the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: going to bark is always worth the train ride , and will make your tongue and belly very happy !\n->going to bark is always worth the train ride , and will make your tongue and belly very happy !\n[{'aspect': 'bark', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: needless to say , android / play rollout is being managed very , very poorly .\n->needless to say , android / play rollout is being managed very , very poorly .\n[{'aspect': 'android / play rollout', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: apps are glitchy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napps are glitchy .\n->", + "output": "{\"text\": \"apps are glitchy .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'glitchy', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: has all the top features and runs fast .\n->has all the top features and runs fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: keyboard key fragile .\n->keyboard key fragile .\n[{'aspect': 'keyboard', 'opinion': 'fragile', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsamsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n->", + "output": "{\"text\": \"samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\", \"labels\": \"[{'aspect': 'stylus', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'oem stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is a buzzing sound that comes from inside of the keyboard .\n->there is a buzzing sound that comes from inside of the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n->i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'android apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncertain apps ( especially flash based apps ) will get the machine very hot .\n->", + "output": "{\"text\": \"certain apps ( especially flash based apps ) will get the machine very hot .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it charges insanely fast .\n->it charges insanely fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food came out wrong , the waiter was no where to be found and the wine showed up at the end of the meal .\n->The food came out wrong , the waiter was no where to be found and the wine showed up at the end of the meal .\n[{'aspect': 'food', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the bottom plastic piece - this is not a unibody machine - will be too hot to rest on bare skin .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bottom plastic piece - this is not a unibody machine - will be too hot to rest on bare skin .\n->", + "output": "{\"text\": \"the bottom plastic piece - this is not a unibody machine - will be too hot to rest on bare skin .\", \"labels\": \"[{'aspect': 'plastic piece', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good sound quality\n->good sound quality\n[{'aspect': 'sound quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Cafe Noir', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n->", + "output": "{\"text\": \"i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\", \"labels\": \"[{'aspect': 'voltage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n->the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n[{'aspect': 'tracking pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: you can not go wrong at the red eye grill .\n->you can not go wrong at the red eye grill .\n[{'aspect': 'red eye grill', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\n->", + "output": "{\"text\": \"the left charging port does not always make full contact with the charger and so sometimes the usb c charger is plugged in and slightly awry and therefore not charging .\", \"labels\": \"[{'aspect': 'charging port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have eaten at saul , many times , the food is always consistently , outrageously good .\n->i have eaten at saul , many times , the food is always consistently , outrageously good .\n[{'aspect': 'food', 'opinion': 'outrageously good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food is great and reasonably priced .\n->The food is great and reasonably priced .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the touchpad doesn ' t have clickable function in the top right or top left corners which is becomes annoying for certain tasks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchpad doesn ' t have clickable function in the top right or top left corners which is becomes annoying for certain tasks .\n->", + "output": "{\"text\": \"the touchpad doesn ' t have clickable function in the top right or top left corners which is becomes annoying for certain tasks .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my wife and i always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n->my wife and i always enjoy the young , not always well trained but nevertheless friendly , staff , all of whom have a story .\n[{'aspect': 'staff', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n->this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n[{'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: many of acer ' s chromebooks have , historically , been hampered by their poor displays .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmany of acer ' s chromebooks have , historically , been hampered by their poor displays .\n->", + "output": "{\"text\": \"many of acer ' s chromebooks have , historically , been hampered by their poor displays .\", \"labels\": \"[{'aspect': 'displays', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fried rice is amazing here .\n->The fried rice is amazing here .\n[{'aspect': 'fried rice', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: no plans to return anytime soon .\n->no plans to return anytime soon .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the chromebook 14 has a 1080p ips display with fantastic viewing angles and excellent brightness .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook 14 has a 1080p ips display with fantastic viewing angles and excellent brightness .\n->", + "output": "{\"text\": \"the chromebook 14 has a 1080p ips display with fantastic viewing angles and excellent brightness .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'display', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there are certain very basic tasks that this computer can do .\n->there are certain very basic tasks that this computer can do .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i keep it at about 60 - 70 % and it looks fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni keep it at about 60 - 70 % and it looks fantastic .\n->", + "output": "{\"text\": \"i keep it at about 60 - 70 % and it looks fantastic .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for $ 389 on cyber monday 2017 .\n->i bought this for $ 389 on cyber monday 2017 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i use it for gaming and it runs rocket league at max graphics and it looks amazing !\n->i use it for gaming and it runs rocket league at max graphics and it looks amazing !\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it feels wonderful to finally say that about an acer display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit feels wonderful to finally say that about an acer display .\n->", + "output": "{\"text\": \"it feels wonderful to finally say that about an acer display .\", \"labels\": \"[{'aspect': 'acer display', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n->purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n[{'aspect': '8th gen i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n->Furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n[{'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'NULL'}]\ntext: a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\n->", + "output": "{\"text\": \"a more recent trend among chrome os devices is the inclusion of 4gb of ram and 32gb of storage , instead of the 2gb / 16gb to which we ' ve become accustomed .\", \"labels\": \"[{'aspect': 'chrome os devices', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this acer chromebook 14 is by far the best chromebook 300 dollars can buy .\n->this acer chromebook 14 is by far the best chromebook 300 dollars can buy .\n[{'aspect': 'acer chromebook 14', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: processer is blazing fast ( competes with 7th gen i7 hq line - check cpu benchmark ) .\n->processer is blazing fast ( competes with 7th gen i7 hq line - check cpu benchmark ) .\n[{'aspect': 'processer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: the extra ram , however , is great for everyone , as it makes this device more capable of running many tabs , or handling higher demand tasks like streaming content , without tabs crashing out or caching / reloading .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe extra ram , however , is great for everyone , as it makes this device more capable of running many tabs , or handling higher demand tasks like streaming content , without tabs crashing out or caching / reloading .\n->", + "output": "{\"text\": \"the extra ram , however , is great for everyone , as it makes this device more capable of running many tabs , or handling higher demand tasks like streaming content , without tabs crashing out or caching / reloading .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'great', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n->quite simply it ' s like stepping out of manhattan and into a haven of tranquility .\n[{'aspect': 'NULL', 'opinion': 'tranquility', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: my husbands birthday and my sons was not as it was intended . . . and we drove two hours to spend too much money to be treated terribly !\n->my husbands birthday and my sons was not as it was intended . . . and we drove two hours to spend too much money to be treated terribly !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n->", + "output": "{\"text\": \"the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'storage', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'intel processor', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n->sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: awesome computer .\n->awesome computer .\n[{'aspect': 'computer', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it also has much better performance , including an easily upgraded m .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit also has much better performance , including an easily upgraded m .\n->", + "output": "{\"text\": \"it also has much better performance , including an easily upgraded m .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Though the Spider Roll may look like a challenge to eat , with soft shell crab hanging out of the roll , it is well worth the price you pay for them .\n->Though the Spider Roll may look like a challenge to eat , with soft shell crab hanging out of the roll , it is well worth the price you pay for them .\n[{'aspect': 'price', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shell crab', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i recommend it !\n->i recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the acer is very nice , to be sure , especially considering the price or $ 299 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe acer is very nice , to be sure , especially considering the price or $ 299 .\n->", + "output": "{\"text\": \"the acer is very nice , to be sure , especially considering the price or $ 299 .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .\n->Kind of a small place but I guess if they are not too busy might be able to fit a group or kids .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The setting is casual and romantic .\n->The setting is casual and romantic .\n[{'aspect': 'setting', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it also has pretty decent i / o with two usb 3 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit also has pretty decent i / o with two usb 3 .\n->", + "output": "{\"text\": \"it also has pretty decent i / o with two usb 3 .\", \"labels\": \"[{'aspect': 'i / o', 'opinion': 'decent', 'polarity': 'positive', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the price is reasonable although the service is poor .\n->the price is reasonable although the service is poor .\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: Rao 's has the best service and atmosphere in NYC .\n->Rao 's has the best service and atmosphere in NYC .\n[{'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i can ' t help but feel that people are also slightly exaggerating the performance offered by the n3160 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can ' t help but feel that people are also slightly exaggerating the performance offered by the n3160 .\n->", + "output": "{\"text\": \"i can ' t help but feel that people are also slightly exaggerating the performance offered by the n3160 .\", \"labels\": \"[{'aspect': 'n3160', 'opinion': 'exaggerating', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n->the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: We ended the dinner with a surprisingly light and flaky apple tarte tatin .\n->We ended the dinner with a surprisingly light and flaky apple tarte tatin .\n[{'aspect': 'apple tarte tatin', 'opinion': 'flaky', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is solid , and does a decent job rendering pages , and paired with the extra ram here , it adequately handles streaming content in the background - such as youtube or spotify - and still maintains a decent browsing experience across eight to ten additional tabs ;\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is solid , and does a decent job rendering pages , and paired with the extra ram here , it adequately handles streaming content in the background - such as youtube or spotify - and still maintains a decent browsing experience across eight to ten additional tabs ;\n->", + "output": "{\"text\": \"it is solid , and does a decent job rendering pages , and paired with the extra ram here , it adequately handles streaming content in the background - such as youtube or spotify - and still maintains a decent browsing experience across eight to ten additional tabs ;\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Aside from the rushed service , we were very impressed with the food and the drinks .\n->Aside from the rushed service , we were very impressed with the food and the drinks .\n[{'aspect': 'service', 'opinion': 'rushed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n->an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n[{'aspect': 'spot', 'opinion': 'unpretentious', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'effective', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: none of this means the acer chromebook 14 is a bad chromebook ; on the contrary , it is an excellent chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnone of this means the acer chromebook 14 is a bad chromebook ; on the contrary , it is an excellent chromebook !\n->", + "output": "{\"text\": \"none of this means the acer chromebook 14 is a bad chromebook ; on the contrary , it is an excellent chromebook !\", \"labels\": \"[{'aspect': 'acer chromebook 14', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicious crab cakes too .\n->delicious crab cakes too .\n[{'aspect': 'crab cakes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: now audio in and out are combined in just one port .\n->now audio in and out are combined in just one port .\n[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: i led with those first five points for a reason : being able to purchase a decently - performing chromebook with aluminum build , a 1080p ips display , 4gb of ram , 32gb of local storage , and an intel quad - core processor for $ 299 is basically what the entirely of the chromeos subreddit has been asking for over the past few years .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni led with those first five points for a reason : being able to purchase a decently - performing chromebook with aluminum build , a 1080p ips display , 4gb of ram , 32gb of local storage , and an intel quad - core processor for $ 299 is basically what the entirely of the chromeos subreddit has been asking for over the past few years .\n->", + "output": "{\"text\": \"i led with those first five points for a reason : being able to purchase a decently - performing chromebook with aluminum build , a 1080p ips display , 4gb of ram , 32gb of local storage , and an intel quad - core processor for $ 299 is basically what the entirely of the chromeos subreddit has been asking for over the past few years .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'decently', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n->Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n[{'aspect': 'mussels', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'puff pastry goat cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salad with a delicious dressing', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hanger steak au poivre', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n->My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n[{'aspect': 'cheese', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\n->", + "output": "{\"text\": \"5 years old or matched by chromebooks at half the price , the absence of a touchscreen upgrade , or the use of slower emmc storage instead of a real m .\", \"labels\": \"[{'aspect': 'emmc storage', 'opinion': 'slower', 'polarity': 'negative', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->the service is excellent , the decor is great , and the food is delicious and comes in large portions .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: these are all valid points that probably make this more of a 4 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthese are all valid points that probably make this more of a 4 .\n->", + "output": "{\"text\": \"these are all valid points that probably make this more of a 4 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chrome extensions are great productivity tools to boot - they help me squeeze the most out of everyday .\n->chrome extensions are great productivity tools to boot - they help me squeeze the most out of everyday .\n[{'aspect': 'chrome extensions', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: a restaurant that does n ' t try to do anything except serve great food with great service in a pleasant atmosphere .\n->a restaurant that does n ' t try to do anything except serve great food with great service in a pleasant atmosphere .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: acer has identified the chromebook market gaps and solved almost everything in this extremely solid offering - a thin , metal , almost mac - like build , armed with 4gb of ram , a full 14 ` ` hd display and a quad - core cpu .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer has identified the chromebook market gaps and solved almost everything in this extremely solid offering - a thin , metal , almost mac - like build , armed with 4gb of ram , a full 14 ` ` hd display and a quad - core cpu .\n->", + "output": "{\"text\": \"acer has identified the chromebook market gaps and solved almost everything in this extremely solid offering - a thin , metal , almost mac - like build , armed with 4gb of ram , a full 14 ` ` hd display and a quad - core cpu .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n->its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: faan is sooo good .\n->faan is sooo good .\n[{'aspect': 'faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven the touchpad and keyboard perform as well as the very best chromebooks currently available .\n->", + "output": "{\"text\": \"even the touchpad and keyboard perform as well as the very best chromebooks currently available .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n->it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: not as much i can do on it , but it is adequate .\n->not as much i can do on it , but it is adequate .\n[{'aspect': 'NULL', 'opinion': 'adequate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: anything that runs in a chrome browser on a desktop or laptop will run on a chromebook , and there are novel app - like extensions for almost anything else ( calculator apps , command lines , etc . )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nanything that runs in a chrome browser on a desktop or laptop will run on a chromebook , and there are novel app - like extensions for almost anything else ( calculator apps , command lines , etc . )\n->", + "output": "{\"text\": \"anything that runs in a chrome browser on a desktop or laptop will run on a chromebook , and there are novel app - like extensions for almost anything else ( calculator apps , command lines , etc . )\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'app - like extensions', 'opinion': 'novel', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but right out the box the battery will not charge .\n->but right out the box the battery will not charge .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is the best shabu - shabu restaurant in the try - state area .\n->this is the best shabu - shabu restaurant in the try - state area .\n[{'aspect': 'shabu - shabu restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: for an office , you can give everyone a chromebook and their environment becomes available on login and disappears when they sign - out , and the machines can also be remotely managed if you ' re a google apps customer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor an office , you can give everyone a chromebook and their environment becomes available on login and disappears when they sign - out , and the machines can also be remotely managed if you ' re a google apps customer .\n->", + "output": "{\"text\": \"for an office , you can give everyone a chromebook and their environment becomes available on login and disappears when they sign - out , and the machines can also be remotely managed if you ' re a google apps customer .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The noise level was unbearable , conversation impossible .\n->The noise level was unbearable , conversation impossible .\n[{'aspect': 'noise level', 'opinion': 'unbearable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: one of the best hot dogs i have ever eaten .\n->one of the best hot dogs i have ever eaten .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: for students and road warriors , the extended battery life and automatic cloud integration makes sync - ing a breeze , especially if you hotspot from your phone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor students and road warriors , the extended battery life and automatic cloud integration makes sync - ing a breeze , especially if you hotspot from your phone .\n->", + "output": "{\"text\": \"for students and road warriors , the extended battery life and automatic cloud integration makes sync - ing a breeze , especially if you hotspot from your phone .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keys feel fantastic to type on .\n->the keys feel fantastic to type on .\n[{'aspect': 'keys', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: the pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria ' s .\n->the pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria ' s .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mozzarella', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: from the camera and audio quality through to the build styling , every aspect of this pc has been meticulously considered and it represents the best chromebook that has been delivered to date\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfrom the camera and audio quality through to the build styling , every aspect of this pc has been meticulously considered and it represents the best chromebook that has been delivered to date\n->", + "output": "{\"text\": \"from the camera and audio quality through to the build styling , every aspect of this pc has been meticulously considered and it represents the best chromebook that has been delivered to date\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: both forward and rear facing cameras would be nice too .\n->both forward and rear facing cameras would be nice too .\n[{'aspect': 'cameras', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: i went with this one because keys are better and i like the laptop feel and look to it .\n->i went with this one because keys are better and i like the laptop feel and look to it .\n[{'aspect': 'keys', 'opinion': 'better', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'laptop', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the keyboard on this unit is actually quite nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard on this unit is actually quite nice .\n->", + "output": "{\"text\": \"the keyboard on this unit is actually quite nice .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n->We could n't carry our conversation as we were routinely interrupted by waitress and servants asking us to order and hinting that we 're taking too much time -- amazing , we just sat down .\n[{'aspect': 'waitress', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'servants', 'opinion': 'interrupted', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i got the $ 10 10 - piece dim sum combo , every bite of which was great .\n->i got the $ 10 10 - piece dim sum combo , every bite of which was great .\n[{'aspect': '$ 10 10 - piece dim sum combo', 'opinion': 'i', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the hardware specs on this are very nice compared to other offerings : 4g ram , 32g storage , quad - core processor , etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hardware specs on this are very nice compared to other offerings : 4g ram , 32g storage , quad - core processor , etc .\n->", + "output": "{\"text\": \"the hardware specs on this are very nice compared to other offerings : 4g ram , 32g storage , quad - core processor , etc .\", \"labels\": \"[{'aspect': 'hardware specs', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could not be happier with this computer because i am not the best person with technology .\n->i could not be happier with this computer because i am not the best person with technology .\n[{'aspect': 'computer', 'opinion': 'happier', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Food was OK - fish was cooked well .\n->Food was OK - fish was cooked well .\n[{'aspect': 'Food', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen is beautiful , and the speakers are very nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is beautiful , and the speakers are very nice .\n->", + "output": "{\"text\": \"the screen is beautiful , and the speakers are very nice .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'speakers', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n->the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n[{'aspect': 'intel rst driver', 'opinion': 'old', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n->i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n[{'aspect': 'dining garden', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'jazz bar', 'opinion': 'new', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'thin crust pizzas', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lasagna menu', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the mouse area is responsive , and has a nice feel to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mouse area is responsive , and has a nice feel to it .\n->", + "output": "{\"text\": \"the mouse area is responsive , and has a nice feel to it .\", \"labels\": \"[{'aspect': 'mouse area', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the main complaints are the touch screen .\n->the main complaints are the touch screen .\n[{'aspect': 'touch screen', 'opinion': 'complaints', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: i double checked the outside labels and upon opening the box saw that the laptop had several deep scratches on the bottom of it .\n->i double checked the outside labels and upon opening the box saw that the laptop had several deep scratches on the bottom of it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#QUALITY'}]\ntext: the overall unit has a slim profile , and is light weight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe overall unit has a slim profile , and is light weight .\n->", + "output": "{\"text\": \"the overall unit has a slim profile , and is light weight .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n->the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n[{'aspect': 'servers', 'opinion': 'perfected', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n->The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n[{'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: thus far , i am very happy with the purchase , and highly recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthus far , i am very happy with the purchase , and highly recommend it .\n->", + "output": "{\"text\": \"thus far , i am very happy with the purchase , and highly recommend it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the price is right too .\n->the price is right too .\n[{'aspect': 'NULL', 'opinion': 'right', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n->it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i think biggest reason i like it is that it ' s sturdy and i don ' t mind throwing it in and out of my backpack whereas with a $ 1000 mb air i might be a whole lot more careful in how i handle it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think biggest reason i like it is that it ' s sturdy and i don ' t mind throwing it in and out of my backpack whereas with a $ 1000 mb air i might be a whole lot more careful in how i handle it .\n->", + "output": "{\"text\": \"i think biggest reason i like it is that it ' s sturdy and i don ' t mind throwing it in and out of my backpack whereas with a $ 1000 mb air i might be a whole lot more careful in how i handle it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n->pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n[{'aspect': 'flip functions', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\n->Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\n[{'aspect': 'dishes', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon caserole', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: hope to continue to get many more years of use out of it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhope to continue to get many more years of use out of it !\n->", + "output": "{\"text\": \"hope to continue to get many more years of use out of it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n->have not tried customer service so no comment , but this is a nice sub $ 400 machine .\n[{'aspect': 'machine', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n->this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'retina display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'compatibility', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: bought this for someone else , can ' t believe how good it is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought this for someone else , can ' t believe how good it is .\n->", + "output": "{\"text\": \"bought this for someone else , can ' t believe how good it is .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is my first time using the intel optane thing and after this i can not recommend it .\n->this laptop is my first time using the intel optane thing and after this i can not recommend it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n->everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !\n[{'aspect': 'atmosphere', 'opinion': 'raved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rooms', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'views', 'opinion': 'incomparable', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this thing is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis thing is good .\n->", + "output": "{\"text\": \"this thing is good .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought the 4gb model which will hopefully last me a few years but it ' s nice and snappy now .\n->i bought the 4gb model which will hopefully last me a few years but it ' s nice and snappy now .\n[{'aspect': '4gb model', 'opinion': 'hopefully', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': '4gb model', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': '4gb model', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The portions are small but being that the food was so good makes up for that .\n->The portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is one of the best purchases i have made in years .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is one of the best purchases i have made in years .\n->", + "output": "{\"text\": \"this is one of the best purchases i have made in years .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what this tells me is that the hdmi port on my chromebook is defective .\n->what this tells me is that the hdmi port on my chromebook is defective .\n[{'aspect': 'hdmi port on my chromebook', 'opinion': 'defective', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n->this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n[{'aspect': 'runner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: since i already do all of my work in google drive , this is perfect for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsince i already do all of my work in google drive , this is perfect for me .\n->", + "output": "{\"text\": \"since i already do all of my work in google drive , this is perfect for me .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Make sure you try this place as often as you can .\n->Make sure you try this place as often as you can .\n[{'aspect': 'place', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the worst excuse for japanese food i ' ve ever encountered .\n->the worst excuse for japanese food i ' ve ever encountered .\n[{'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: it ' s the best solution i ' ve used for collaborating on a budget .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s the best solution i ' ve used for collaborating on a budget .\n->", + "output": "{\"text\": \"it ' s the best solution i ' ve used for collaborating on a budget .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was so-so .\n->The food was so-so .\n[{'aspect': 'food', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: at first i was totally stoked on this chromebook .\n->at first i was totally stoked on this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'stoked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: however , know what you ' re buying before you get a chromebook : it operates on a chrome operating system and you can ' t install apps or games on it like world of warcraft .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , know what you ' re buying before you get a chromebook : it operates on a chrome operating system and you can ' t install apps or games on it like world of warcraft .\n->", + "output": "{\"text\": \"however , know what you ' re buying before you get a chromebook : it operates on a chrome operating system and you can ' t install apps or games on it like world of warcraft .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n->one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The fried rice is really good too .\n->The fried rice is really good too .\n[{'aspect': 'fried rice', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: screen quality is perfect and matte so no annoying glare !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen quality is perfect and matte so no annoying glare !\n->", + "output": "{\"text\": \"screen quality is perfect and matte so no annoying glare !\", \"labels\": \"[{'aspect': 'screen quality', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n->nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n[{'aspect': 'mac os x', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: this is a wonderful device .\n->this is a wonderful device .\n[{'aspect': 'device', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the keys on the keyboard are sunk in a little too low to accommodate for the sleek / thin profile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keys on the keyboard are sunk in a little too low to accommodate for the sleek / thin profile .\n->", + "output": "{\"text\": \"the keys on the keyboard are sunk in a little too low to accommodate for the sleek / thin profile .\", \"labels\": \"[{'aspect': 'keys on the keyboard are', 'opinion': 'low', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Most importantly , food is excellent .\n->Most importantly , food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n->the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it take a bit of getting used to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit take a bit of getting used to .\n->", + "output": "{\"text\": \"it take a bit of getting used to .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was very excited at the prospect of buying this laptop .\n->i was very excited at the prospect of buying this laptop .\n[{'aspect': 'laptop', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the waitress , seems to be more concerned of looking good than actually waitressing .\n->the waitress , seems to be more concerned of looking good than actually waitressing .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: first off the chromebook itself is excellent , one of the few budget models with 1080p and a long battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst off the chromebook itself is excellent , one of the few budget models with 1080p and a long battery life .\n->", + "output": "{\"text\": \"first off the chromebook itself is excellent , one of the few budget models with 1080p and a long battery life .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: manufactures need to quality check their products before sending them out .\n->manufactures need to quality check their products before sending them out .\n[{'aspect': 'manufactures', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\ntext: it arrived in ` ` as new ` ` condition despite being a refurbished ( likely a return ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit arrived in ` ` as new ` ` condition despite being a refurbished ( likely a return ) .\n->", + "output": "{\"text\": \"it arrived in ` ` as new ` ` condition despite being a refurbished ( likely a return ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the price is reasonable although the service is poor .\n->the price is reasonable although the service is poor .\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: never swaying , never a bad meal , never bad service . . .\n->never swaying , never a bad meal , never bad service . . .\n[{'aspect': 'meal', 'opinion': 'never a bad', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'never bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it is a steal at $ 179 and worth a 5 star rating for the previous reasons .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a steal at $ 179 and worth a 5 star rating for the previous reasons .\n->", + "output": "{\"text\": \"it is a steal at $ 179 and worth a 5 star rating for the previous reasons .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what a hassle !\n->what a hassle !\n[{'aspect': 'NULL', 'opinion': 'hassle', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n->The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n[{'aspect': 'Bagels', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'chewy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'gummy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: as other reviews have mentioned - its a bit heavier than expected for such a slim frame .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas other reviews have mentioned - its a bit heavier than expected for such a slim frame .\n->", + "output": "{\"text\": \"as other reviews have mentioned - its a bit heavier than expected for such a slim frame .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: multitasking is pretty good .\n->multitasking is pretty good .\n[{'aspect': 'multitasking', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: definitely worth the trip to battery park city !\n->definitely worth the trip to battery park city !\n[{'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: overall , i ' m definitely satisfied , i bought it wanting a well built laptop on the larger end ( which is why i ultimately went with the 14 inch ) with a solid material / frame .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , i ' m definitely satisfied , i bought it wanting a well built laptop on the larger end ( which is why i ultimately went with the 14 inch ) with a solid material / frame .\n->", + "output": "{\"text\": \"overall , i ' m definitely satisfied , i bought it wanting a well built laptop on the larger end ( which is why i ultimately went with the 14 inch ) with a solid material / frame .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\n->The food can get pricey but the prixe fixe tasting menu is the greatest food for a good price and they cater the food to any food allergies or food you do n't like .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'prixe fixe tasting menu', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 10\n->10\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: build quality seems excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuild quality seems excellent .\n->", + "output": "{\"text\": \"build quality seems excellent .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Delicious food at a great price but do not go here on a cold day and sit by the front door .\n->Delicious food at a great price but do not go here on a cold day and sit by the front door .\n[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'front door', 'opinion': 'cold', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i can ' t help but feel that people are also slightly exaggerating the performance offered by the n3160 .\n->i can ' t help but feel that people are also slightly exaggerating the performance offered by the n3160 .\n[{'aspect': 'n3160', 'opinion': 'exaggerating', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: i purchased mine in the gold color and its beautiful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni purchased mine in the gold color and its beautiful .\n->", + "output": "{\"text\": \"i purchased mine in the gold color and its beautiful .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mine tasted like the bartender had forgotten to add the tequila .\n->mine tasted like the bartender had forgotten to add the tequila .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: Service was prompt , friendly and great .\n->Service was prompt , friendly and great .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: beautiful ips display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeautiful ips display .\n->", + "output": "{\"text\": \"beautiful ips display .\", \"labels\": \"[{'aspect': 'ips display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good food .\n->good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i ' m saving up for my next visit .\n->i ' m saving up for my next visit .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i ' m running it at 2500x1400 resolution extremely thin and lightweight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m running it at 2500x1400 resolution extremely thin and lightweight .\n->", + "output": "{\"text\": \"i ' m running it at 2500x1400 resolution extremely thin and lightweight .\", \"labels\": \"[{'aspect': 'resolution', 'opinion': 'thin', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'resolution', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n->The staff makes you feel at home , the food is great and the atmosphere is WONDERFUL !\n[{'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: apple told me to upgrade it to just buy the new one when they release it .\n->apple told me to upgrade it to just buy the new one when they release it .\n[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: now it kind of feels a little bit like windows but still different .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow it kind of feels a little bit like windows but still different .\n->", + "output": "{\"text\": \"now it kind of feels a little bit like windows but still different .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delivery is fast too .\n->delivery is fast too .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: best italian food i ever had ( and being italian , that means alot ) .\n->best italian food i ever had ( and being italian , that means alot ) .\n[{'aspect': 'italian food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it on the developer channel and have full access to the play store .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit on the developer channel and have full access to the play store .\n->", + "output": "{\"text\": \"it on the developer channel and have full access to the play store .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: super fast boot .\n->super fast boot .\n[{'aspect': 'boot', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\nExample:\ntext: Though the Spider Roll may look like a challenge to eat , with soft shell crab hanging out of the roll , it is well worth the price you pay for them .\n->Though the Spider Roll may look like a challenge to eat , with soft shell crab hanging out of the roll , it is well worth the price you pay for them .\n[{'aspect': 'price', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shell crab', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}]\ntext: update : device now has full support for the play store on the beta channel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupdate : device now has full support for the play store on the beta channel .\n->", + "output": "{\"text\": \"update : device now has full support for the play store on the beta channel .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: every app i have downloaded from the google app store has worked perfectly .\n->every app i have downloaded from the google app store has worked perfectly .\n[{'aspect': 'app', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: so the audio can easily be muffled .\n->so the audio can easily be muffled .\n[{'aspect': 'audio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: this makes this chromebook closer to a real computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis makes this chromebook closer to a real computer .\n->", + "output": "{\"text\": \"this makes this chromebook closer to a real computer .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great notebook .\n->this is a great notebook .\n[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: my friends settled for rice dishes , but we came back the following day to try the dim sum , which was good . . . not outstanding , but good .\n->my friends settled for rice dishes , but we came back the following day to try the dim sum , which was good . . . not outstanding , but good .\n[{'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'dim sum', 'opinion': 'not outstanding', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: the system is quick , and used for browsing , and basic notes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe system is quick , and used for browsing , and basic notes .\n->", + "output": "{\"text\": \"the system is quick , and used for browsing , and basic notes .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n[{'aspect': 'staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'stressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'unisex bathroom', 'opinion': 'stressed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it is set far from the small street it ' s on , and there is no traffic noise .\n->it is set far from the small street it ' s on , and there is no traffic noise .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: the keyboard gives a satisfying audible response to being used , and the mouse is nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard gives a satisfying audible response to being used , and the mouse is nice .\n->", + "output": "{\"text\": \"the keyboard gives a satisfying audible response to being used , and the mouse is nice .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'mouse', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great notebook .\n->this is a great notebook .\n[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->this little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'place', 'opinion': 'exceeded my expectations', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: that machine couldn ' t handle all the tabs i wanted to keep open , so after much research , i jumped into this acer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat machine couldn ' t handle all the tabs i wanted to keep open , so after much research , i jumped into this acer .\n->", + "output": "{\"text\": \"that machine couldn ' t handle all the tabs i wanted to keep open , so after much research , i jumped into this acer .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good music , great food , speedy service affordable prices .\n->good music , great food , speedy service affordable prices .\n[{'aspect': 'music', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service was attentive .\n->The service was attentive .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n->", + "output": "{\"text\": \"it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'insane', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was very good , a great deal , and the place its self was great .\n->The food was very good , a great deal , and the place its self was great .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the overall durability is a bit suspect but with the price tag , it is essentially a 1 - 2 year investment for school .\n->the overall durability is a bit suspect but with the price tag , it is essentially a 1 - 2 year investment for school .\n[{'aspect': 'NULL', 'opinion': 'suspect', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: one thing keeps it from getting a five - star rave from me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none thing keeps it from getting a five - star rave from me .\n->", + "output": "{\"text\": \"one thing keeps it from getting a five - star rave from me .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was really good pizza .\n->it was really good pizza .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: - ssd ( solid state drive ) is not easily upgradable .\n->- ssd ( solid state drive ) is not easily upgradable .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\ntext: it is very light weight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is very light weight .\n->", + "output": "{\"text\": \"it is very light weight .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re coming from a pc you ' ll love the battery .\n->if you ' re coming from a pc you ' ll love the battery .\n[{'aspect': 'battery', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: the exotic food is beautifully presented and is a delight in delicious combinations .\n->the exotic food is beautifully presented and is a delight in delicious combinations .\n[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: more importantly i appreciate the uncanny speed to boot up or wake up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmore importantly i appreciate the uncanny speed to boot up or wake up .\n->", + "output": "{\"text\": \"more importantly i appreciate the uncanny speed to boot up or wake up .\", \"labels\": \"[{'aspect': 'speed', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i barely do anything on it and it is a complete garbage can of a laptop .\n->i barely do anything on it and it is a complete garbage can of a laptop .\n[{'aspect': 'laptop', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: very clean computer everything looks brand new !\n->very clean computer everything looks brand new !\n[{'aspect': 'computer', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: never has it run out of power while on battery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnever has it run out of power while on battery .\n->", + "output": "{\"text\": \"never has it run out of power while on battery .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My chow fun and chow see was really bland and oily .\n->My chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: love it !\n->love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: but they didn ' t make it $ 300 by wasting money , there are compromises .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut they didn ' t make it $ 300 by wasting money , there are compromises .\n->", + "output": "{\"text\": \"but they didn ' t make it $ 300 by wasting money , there are compromises .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The kitchen however , is almost always slow .\n->The kitchen however , is almost always slow .\n[{'aspect': 'kitchen', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: nice build quality , very fast and beautiful display .\n->nice build quality , very fast and beautiful display .\n[{'aspect': 'build quality', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'display', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: the 1080p display is very sharp .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 1080p display is very sharp .\n->", + "output": "{\"text\": \"the 1080p display is very sharp .\", \"labels\": \"[{'aspect': '1080p display', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: samsung keeps telling me that the serial number is invalid .\n->samsung keeps telling me that the serial number is invalid .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: this place is worth an one - hour drive .\n->this place is worth an one - hour drive .\n[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: you can watch 1080p video on it and it looks great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can watch 1080p video on it and it looks great .\n->", + "output": "{\"text\": \"you can watch 1080p video on it and it looks great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n->samsung sent me a new stylus free of charge and free of hassle , which was nice of them because the top of the oem stylus popped off one day and could not be reassembled .\n[{'aspect': 'stylus', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'oem stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i find the picture more watchable than my tv .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni find the picture more watchable than my tv .\n->", + "output": "{\"text\": \"i find the picture more watchable than my tv .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'watchable', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Bagels are ok , but be sure not to make any special requests !\n->Bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: pros : nice size , clear screen , quick on start up , very functional and easy to use .\n->pros : nice size , clear screen , quick on start up , very functional and easy to use .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: the audio is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe audio is great .\n->", + "output": "{\"text\": \"the audio is great .\", \"labels\": \"[{'aspect': 'audio', 'opinion': 'great', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is not worth the prices .\n->This place is not worth the prices .\n[{'aspect': 'prices', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n->this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the keyboard is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is good .\n->", + "output": "{\"text\": \"the keyboard is good .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: since i already do all of my work in google drive , this is perfect for me .\n->since i already do all of my work in google drive , this is perfect for me .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: be sure to try the seasonal , and always delicious , specials .\n->be sure to try the seasonal , and always delicious , specials .\n[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: but the 4 gb of ram makes the things you use a chromebook for work without delay .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the 4 gb of ram makes the things you use a chromebook for work without delay .\n->", + "output": "{\"text\": \"but the 4 gb of ram makes the things you use a chromebook for work without delay .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is now my fastest - charging device .\n->this is now my fastest - charging device .\n[{'aspect': 'device', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: never got an explanation as to what was going on .\n->never got an explanation as to what was going on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: where they didn ' t sacrifice is on build quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhere they didn ' t sacrifice is on build quality .\n->", + "output": "{\"text\": \"where they didn ' t sacrifice is on build quality .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our server was very helpful and friendly .\n->our server was very helpful and friendly .\n[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: - the rma process needs improvement - buyer must pay to return the product for repair .\n->- the rma process needs improvement - buyer must pay to return the product for repair .\n[{'aspect': 'rma process', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: great device for browsing the internet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat device for browsing the internet .\n->", + "output": "{\"text\": \"great device for browsing the internet .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: loved it for an hour than it went black and we got a chrome os missing or damage message .\n->loved it for an hour than it went black and we got a chrome os missing or damage message .\n[{'aspect': 'chrome os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#QUALITY'}]\nExample:\ntext: after really enjoying ourselves at the bar we sat down at a table and had dinner .\n->after really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: hulu app is not supported for this computer so you will need to use the browser .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhulu app is not supported for this computer so you will need to use the browser .\n->", + "output": "{\"text\": \"hulu app is not supported for this computer so you will need to use the browser .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I was really disappointed ant wanted to tell everyone not to go eat or even take out food from there .\n->I was really disappointed ant wanted to tell everyone not to go eat or even take out food from there .\n[{'aspect': 'food', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i have never ever had such an unpleasant experience .\n->i have never ever had such an unpleasant experience .\n[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the keyboard is difficult to get used to due to the placement and spacing of the keys compared to a regular keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is difficult to get used to due to the placement and spacing of the keys compared to a regular keyboard .\n->", + "output": "{\"text\": \"the keyboard is difficult to get used to due to the placement and spacing of the keys compared to a regular keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: today tried to turned it on , but to a blank screen !\n->today tried to turned it on , but to a blank screen !\n[{'aspect': 'blank screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: It is so easy to get a reservation at a top place in NYC with a week 's notice .\n->It is so easy to get a reservation at a top place in NYC with a week 's notice .\n[{'aspect': 'reservation', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\n->", + "output": "{\"text\": \"the battery life has started to go down after a year of solid use ( still quite good ) , but the quality screen and chromeos make it worth it .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'solid', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'worth', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'quality', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'chromeos', 'opinion': 'worth', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as soon as i turned on the computer , it froze as i tried to sync information .\n->as soon as i turned on the computer , it froze as i tried to sync information .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great bar , most gorgeous bartenders you 've ever seen ( specifically the blond lady ) .\n->Great bar , most gorgeous bartenders you 've ever seen ( specifically the blond lady ) .\n[{'aspect': 'bar', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bartenders', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: acer put together a solid package .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer put together a solid package .\n->", + "output": "{\"text\": \"acer put together a solid package .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'solid', 'polarity': 'positive', 'category': 'COMPANY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the core m3 allows this system to get fast and to stay quiet .\n->the core m3 allows this system to get fast and to stay quiet .\n[{'aspect': 'core m3', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'core m3', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: The wine list is also really nice .\n->The wine list is also really nice .\n[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i still had some of those kinds of mishaps with the chromebook - - but not nearly as many .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni still had some of those kinds of mishaps with the chromebook - - but not nearly as many .\n->", + "output": "{\"text\": \"i still had some of those kinds of mishaps with the chromebook - - but not nearly as many .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'mishaps', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We all agreed that mare is one of the best seafood restaurants in New York .\n->We all agreed that mare is one of the best seafood restaurants in New York .\n[{'aspect': 'mare', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m more about personality than looks , but this little thing is a looker , too .\n->i ' m more about personality than looks , but this little thing is a looker , too .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\n->", + "output": "{\"text\": \"the machine is great for streaming videos - - my wife and i use it regularly to watch things on amazon prime - - and the battery last quite a while .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n->Ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: delivery is fast too .\n->delivery is fast too .\n[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: he wanted something that could browse the internet fast - and this chromebook does just that !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhe wanted something that could browse the internet fast - and this chromebook does just that !\n->", + "output": "{\"text\": \"he wanted something that could browse the internet fast - and this chromebook does just that !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n->initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: They 're also friendlier here , especially the owner , Kenny .\n->They 're also friendlier here , especially the owner , Kenny .\n[{'aspect': 'owner', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love how slim the design is - will fit easily into my backpack : ) highly recommend for everyday or school use !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love how slim the design is - will fit easily into my backpack : ) highly recommend for everyday or school use !\n->", + "output": "{\"text\": \"i love how slim the design is - will fit easily into my backpack : ) highly recommend for everyday or school use !\", \"labels\": \"[{'aspect': 'design', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n->Food was very good as well , considering that we tried the budget selection ( though I wish the pork belly that I ordered was roasted a bit longer , so that fat was more of a melt-in-your-mouth experience ) .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i asked for a menu and the same waitress looked at my like i was insane .\n->i asked for a menu and the same waitress looked at my like i was insane .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\n->", + "output": "{\"text\": \"i ' ve kept an eye on chromebooks for the last couple of years as a possible option for a low - cost laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was good too .\n->The food was good too .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We were looking forward to nice glass of Sangria when we arrived .\n->We were looking forward to nice glass of Sangria when we arrived .\n[{'aspect': 'glass of Sangria', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the chrome rdp app also works well , connecting to my windows machines with good performance , and i can access files on my nas using network file shares after using the chromebook for the last couple of weeks , i ' d confidently recommend one for everything but gaming and special purpose applications .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chrome rdp app also works well , connecting to my windows machines with good performance , and i can access files on my nas using network file shares after using the chromebook for the last couple of weeks , i ' d confidently recommend one for everything but gaming and special purpose applications .\n->", + "output": "{\"text\": \"the chrome rdp app also works well , connecting to my windows machines with good performance , and i can access files on my nas using network file shares after using the chromebook for the last couple of weeks , i ' d confidently recommend one for everything but gaming and special purpose applications .\", \"labels\": \"[{'aspect': 'chrome rdp app', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chrome rdp app', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for a fabulous wedding !\n->for a fabulous wedding !\n[{'aspect': 'NULL', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: rao is a good restaurant , but it ' s nothing special .\n->rao is a good restaurant , but it ' s nothing special .\n[{'aspect': 'rao', 'opinion': 'good', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'rao', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\ntext: the quality of this unit exceeds my expectations , and if feels solidly built while still very thin and light .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe quality of this unit exceeds my expectations , and if feels solidly built while still very thin and light .\n->", + "output": "{\"text\": \"the quality of this unit exceeds my expectations , and if feels solidly built while still very thin and light .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'solidly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'unit', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n->needless to say , if you thought this capability would allow you to use a browser other than chrome , forget it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n->looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\ntext: the keyboard and track pad are both quite good , although i always use a real mouse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard and track pad are both quite good , although i always use a real mouse .\n->", + "output": "{\"text\": \"the keyboard and track pad are both quite good , although i always use a real mouse .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'track pad', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: , the advertised ram is 16gb and in the answer questions section has answers from the manufacturer saying ` ` this will have 16gb of memory ! ` `\n->, the advertised ram is 16gb and in the answer questions section has answers from the manufacturer saying ` ` this will have 16gb of memory ! ` `\n[{'aspect': 'ram', 'opinion': '16gb', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: battery life is excellent , and i can get several days use before needing to plug in .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is excellent , and i can get several days use before needing to plug in .\n->", + "output": "{\"text\": \"battery life is excellent , and i can get several days use before needing to plug in .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most of the time it works very well and one is subject to the vagaries of the various apps , browser , etc .\n->most of the time it works very well and one is subject to the vagaries of the various apps , browser , etc .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: don ' t know what was going on , but , it seems like this laptop is working fine .\n->don ' t know what was going on , but , it seems like this laptop is working fine .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n->", + "output": "{\"text\": \"with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'solid', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'good', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our family never expected such incredible entertainment in a restaurant .\n->our family never expected such incredible entertainment in a restaurant .\n[{'aspect': 'entertainment', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: ( i had to check that the caps lock was off after typing that last word . )\n->( i had to check that the caps lock was off after typing that last word . )\n[{'aspect': 'caps lock', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: saving the best for last , the full hd screen is one of the primary reasons i chose this chromebook , and it is wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsaving the best for last , the full hd screen is one of the primary reasons i chose this chromebook , and it is wonderful .\n->", + "output": "{\"text\": \"saving the best for last , the full hd screen is one of the primary reasons i chose this chromebook , and it is wonderful .\", \"labels\": \"[{'aspect': 'hd screen', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you do n ' t need a full blown laptop this is a good choice .\n->if you do n ' t need a full blown laptop this is a good choice .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n->I only tried a simple dish of spinach ravioli in a light oil and garlic sauce , but it actually faired better than most NYC Italian joints I 've tried similar dishes at .\n[{'aspect': 'spinach ravioli in a light oil and garlic sauce', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: to me , this chromebook is a great value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto me , this chromebook is a great value .\n->", + "output": "{\"text\": \"to me , this chromebook is a great value .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i just got this yesterday and i am very satisfied with the speed .\n->i just got this yesterday and i am very satisfied with the speed .\n[{'aspect': 'speed', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this acer chromebook 14 is by far the best chromebook 300 dollars can buy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis acer chromebook 14 is by far the best chromebook 300 dollars can buy .\n->", + "output": "{\"text\": \"this acer chromebook 14 is by far the best chromebook 300 dollars can buy .\", \"labels\": \"[{'aspect': 'acer chromebook 14', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i asked for a menu and the same waitress looked at my like i was insane .\n->i asked for a menu and the same waitress looked at my like i was insane .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: He takes real pride in his food and his business .\n->He takes real pride in his food and his business .\n[{'aspect': 'food', 'opinion': 'pride', 'polarity': 'positive', 'category': 'NULL'}]\ntext: with chrome remote desktop i am able to play turn - based video games such as hearthstone or civ 5 while miles away from the computer actually running them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith chrome remote desktop i am able to play turn - based video games such as hearthstone or civ 5 while miles away from the computer actually running them .\n->", + "output": "{\"text\": \"with chrome remote desktop i am able to play turn - based video games such as hearthstone or civ 5 while miles away from the computer actually running them .\", \"labels\": \"[{'aspect': 'chrome remote desktop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the prices were cheap compared to the quality of service and food .\n->the prices were cheap compared to the quality of service and food .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: Then , get ripped on free box wine .\n->Then , get ripped on free box wine .\n[{'aspect': 'box wine', 'opinion': 'free', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is also very gnu + linux friendly if you want to replace the os entirely .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is also very gnu + linux friendly if you want to replace the os entirely .\n->", + "output": "{\"text\": \"it is also very gnu + linux friendly if you want to replace the os entirely .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is also one of the nicest most comfortable i have ever used .\n->the keyboard is also one of the nicest most comfortable i have ever used .\n[{'aspect': 'keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: now it kind of feels a little bit like windows but still different .\n->now it kind of feels a little bit like windows but still different .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}]\ntext: overall this chromebook is perfect for my current use - case and would recommend it to anyone in the chromebook market with 300 dollars to spare .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall this chromebook is perfect for my current use - case and would recommend it to anyone in the chromebook market with 300 dollars to spare .\n->", + "output": "{\"text\": \"overall this chromebook is perfect for my current use - case and would recommend it to anyone in the chromebook market with 300 dollars to spare .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food arrived 20 minutes after i called , cold and soggy .\n->the food arrived 20 minutes after i called , cold and soggy .\n[{'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this was a christmas present and now we ' re scrambling because it sucks .\n->this was a christmas present and now we ' re scrambling because it sucks .\n[{'aspect': 'NULL', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n->", + "output": "{\"text\": \"it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: drinks way over priced .\n->drinks way over priced .\n[{'aspect': 'drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: choose this one .\n->choose this one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: you can ' t use it to edit a word document .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can ' t use it to edit a word document .\n->", + "output": "{\"text\": \"you can ' t use it to edit a word document .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer has a solid processor and enough memory to handle pretty much anything the average user willl throw at it , short of graphics - heavy gaming .\n->the computer has a solid processor and enough memory to handle pretty much anything the average user willl throw at it , short of graphics - heavy gaming .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#QUALITY'}]\nExample:\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i could not be happier with this computer because i am not the best person with technology .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni could not be happier with this computer because i am not the best person with technology .\n->", + "output": "{\"text\": \"i could not be happier with this computer because i am not the best person with technology .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'happier', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in the evening , this place attracted a well dressed , with it , ny crowd .\n->in the evening , this place attracted a well dressed , with it , ny crowd .\n[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: i replug and restarted the laptop 3 times and it still does n ' t work .\n->i replug and restarted the laptop 3 times and it still does n ' t work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i don ' t know how to use a lot of computers but this is simple and easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t know how to use a lot of computers but this is simple and easy to use .\n->", + "output": "{\"text\": \"i don ' t know how to use a lot of computers but this is simple and easy to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but i fed up with the price it cost to upgrade the product as well as the software .\n->but i fed up with the price it cost to upgrade the product as well as the software .\n[{'aspect': 'NULL', 'opinion': 'fed up', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: samsung keeps telling me that the serial number is invalid .\n->samsung keeps telling me that the serial number is invalid .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\ntext: very easy to set up and light to carry .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery easy to set up and light to carry .\n->", + "output": "{\"text\": \"very easy to set up and light to carry .\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: either that , or the editor ' s idea of romance must be sharing a conversation with the next table , while trying to speak louder than the traffic on 57th .\n->either that , or the editor ' s idea of romance must be sharing a conversation with the next table , while trying to speak louder than the traffic on 57th .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the keyboard is good .\n->the keyboard is good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: this is an amazing computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is an amazing computer .\n->", + "output": "{\"text\": \"this is an amazing computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n->For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n[{'aspect': 'lobby area', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n->my wife and i ate here earlier this week and have not stopped ranting and raving about the food .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: however , having been out of school for several years now , the viability of purchasing a mac ( particularly as they are increasing in price ) no longer seems viable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , having been out of school for several years now , the viability of purchasing a mac ( particularly as they are increasing in price ) no longer seems viable .\n->", + "output": "{\"text\": \"however , having been out of school for several years now , the viability of purchasing a mac ( particularly as they are increasing in price ) no longer seems viable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the counter service is bad .\n->the counter service is bad .\n[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: charging is crazy fast .\n->charging is crazy fast .\n[{'aspect': 'NULL', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: first of all , the battery life on it is insane .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst of all , the battery life on it is insane .\n->", + "output": "{\"text\": \"first of all , the battery life on it is insane .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'insane', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their calzones are horrific , bad , vomit-inducing , YUCK .\n->Their calzones are horrific , bad , vomit-inducing , YUCK .\n[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'vomit-inducing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'YUCK', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: but the pizza is way to expensive .\n->but the pizza is way to expensive .\n[{'aspect': 'pizza', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: first , it is hard to run more than 10 tabs open at any given time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst , it is hard to run more than 10 tabs open at any given time .\n->", + "output": "{\"text\": \"first , it is hard to run more than 10 tabs open at any given time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the crunchy tuna , it is to die for .\n->try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: battery life is great if you use half of the brightness .\n->battery life is great if you use half of the brightness .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\n->", + "output": "{\"text\": \"first time users might have a hard time using the keyboard ( some keys like caps lock , pagedown , pageup , delete , home etc are missing , browse the internet to get the equivalent ones ) .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only three months in , and the laptop won ' t charge .\n->only three months in , and the laptop won ' t charge .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this thing is the ultimate mobile workhorse .\n->this thing is the ultimate mobile workhorse .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: the build quality is good ( all aluminum body ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is good ( all aluminum body ) .\n->", + "output": "{\"text\": \"the build quality is good ( all aluminum body ) .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n->Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n[{'aspect': 'salmon', 'opinion': 'wasnt impressed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servings', 'opinion': 'Small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: everything was good for a few days after receiving the product .\n->everything was good for a few days after receiving the product .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the boot up is fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe boot up is fast .\n->", + "output": "{\"text\": \"the boot up is fast .\", \"labels\": \"[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is incredibly helpful and attentive .\n->The staff is incredibly helpful and attentive .\n[{'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We both opted for a pasta dish and they were served timely and fresh .\n->We both opted for a pasta dish and they were served timely and fresh .\n[{'aspect': 'pasta dish', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: camera is not that good , so video call on mobile phone is better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncamera is not that good , so video call on mobile phone is better .\n->", + "output": "{\"text\": \"camera is not that good , so video call on mobile phone is better .\", \"labels\": \"[{'aspect': 'camera', 'opinion': 'not that good', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: laptop is as described .\n->laptop is as described .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it can not .\n->it can not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n->", + "output": "{\"text\": \"this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'boot up', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n->The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n[{'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'well trained', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n->We would like to thank Marcelo and Grace for a wonderful dining experience ! ! !\n[{'aspect': 'dining', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the chromebook is quite ideal for those who use their computer mostly for surfing the internet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook is quite ideal for those who use their computer mostly for surfing the internet .\n->", + "output": "{\"text\": \"the chromebook is quite ideal for those who use their computer mostly for surfing the internet .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'ideal', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely recommend this chromebook , it ' s a beautiful machine .\n->definitely recommend this chromebook , it ' s a beautiful machine .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n->it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n[{'aspect': 'tablet', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\n->", + "output": "{\"text\": \"we actually returned the first acer chromebook 14 , we got because the screen would blink randomly and go off .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the machine is near - unusable out of the box .\n->the machine is near - unusable out of the box .\n[{'aspect': 'machine', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The service was excellent - friendly and attentive .\n->The service was excellent - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: as for actual device it is completely gorgeous and ( now ) works flawlessly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas for actual device it is completely gorgeous and ( now ) works flawlessly .\n->", + "output": "{\"text\": \"as for actual device it is completely gorgeous and ( now ) works flawlessly .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a computer , for typing and using internet in general , this computer is good .\n->as a computer , for typing and using internet in general , this computer is good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: a wonderful device with extremely clear display .\n->a wonderful device with extremely clear display .\n[{'aspect': 'device', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: he was ecstatic at the power , the screen , the layout - pretty much everything .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhe was ecstatic at the power , the screen , the layout - pretty much everything .\n->", + "output": "{\"text\": \"he was ecstatic at the power , the screen , the layout - pretty much everything .\", \"labels\": \"[{'aspect': 'power', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'screen', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: perfect ` ` computer ` ` for my young child .\n->perfect ` ` computer ` ` for my young child .\n[{'aspect': 'computer', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: worst customer service experience in years .\n->worst customer service experience in years .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: while the device is awesome , and there are lots of other reviews that will tell you exactly how awesome , i am just so impressed by how amazon ' s customer service handled the issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile the device is awesome , and there are lots of other reviews that will tell you exactly how awesome , i am just so impressed by how amazon ' s customer service handled the issue .\n->", + "output": "{\"text\": \"while the device is awesome , and there are lots of other reviews that will tell you exactly how awesome , i am just so impressed by how amazon ' s customer service handled the issue .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': \"amazon ' s customer service\", 'opinion': 'impressed', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You can not go wrong with this place .\n->You can not go wrong with this place .\n[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it was the first place we ate on our first trip to new york , and it will be the last place we stop as we head out of town on our next trip to new york .\n->it was the first place we ate on our first trip to new york , and it will be the last place we stop as we head out of town on our next trip to new york .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: guaranteed excellent customer service !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nguaranteed excellent customer service !\n->", + "output": "{\"text\": \"guaranteed excellent customer service !\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer put together a solid package .\n->acer put together a solid package .\n[{'aspect': 'acer', 'opinion': 'solid', 'polarity': 'positive', 'category': 'COMPANY#QUALITY'}]\nExample:\ntext: the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n->the service was fast and friendly and the food was very tasty and they had the best hot sauce to add to your meals .\n[{'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot sauce', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: keep in mind , you have to pay $ for ms office where google docs is free .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeep in mind , you have to pay $ for ms office where google docs is free .\n->", + "output": "{\"text\": \"keep in mind , you have to pay $ for ms office where google docs is free .\", \"labels\": \"[{'aspect': 'google docs', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s so much faster and the mac os is so much more secure .\n->it ' s so much faster and the mac os is so much more secure .\n[{'aspect': 'NULL', 'opinion': 'faster', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac os', 'opinion': 'secure', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\nExample:\ntext: I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n->I highly recommend Cafe St. Bart 's for their food , the ambience and wonderful service .\n[{'aspect': 'food', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overall , the way i see it , i ' m getting a big screen hd android galaxy tablet with a keyboard for the price of a tablet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , the way i see it , i ' m getting a big screen hd android galaxy tablet with a keyboard for the price of a tablet .\n->", + "output": "{\"text\": \"overall , the way i see it , i ' m getting a big screen hd android galaxy tablet with a keyboard for the price of a tablet .\", \"labels\": \"[{'aspect': 'tablet', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was the friendliest that have seen in New York .\n->The staff was the friendliest that have seen in New York .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and i hate to say this but i doubt i ' ll ever go back .\n->and i hate to say this but i doubt i ' ll ever go back .\n[{'aspect': 'NULL', 'opinion': 'doubt', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: this acer is a web surfer that ' s easy to travel with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis acer is a web surfer that ' s easy to travel with .\n->", + "output": "{\"text\": \"this acer is a web surfer that ' s easy to travel with .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing is that it does ' t have too much storage room .\n->the only thing is that it does ' t have too much storage room .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n->on our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: first of all , this is a physically beautiful machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst of all , this is a physically beautiful machine .\n->", + "output": "{\"text\": \"first of all , this is a physically beautiful machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i will keep my rating at 3 stars , as the issues with the screen quality / shine - and volume / brightness keys being unusable and nonexistent , to be large issues for me .\n->i will keep my rating at 3 stars , as the issues with the screen quality / shine - and volume / brightness keys being unusable and nonexistent , to be large issues for me .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'volume', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'volume', 'opinion': 'nonexistent', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n->", + "output": "{\"text\": \"the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'large', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n->i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n[{'aspect': 'flip', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: The sandwiches are dry , tasteless and way overpriced .\n->The sandwiches are dry , tasteless and way overpriced .\n[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\ntext: love the style , aluminum shell , 14 inches monitor , and decent resolution .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the style , aluminum shell , 14 inches monitor , and decent resolution .\n->", + "output": "{\"text\": \"love the style , aluminum shell , 14 inches monitor , and decent resolution .\", \"labels\": \"[{'aspect': 'style', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'aluminum shell', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '14 inches monitor', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had fish and my husband had the filet - both of which exceeded our expectations .\n->i had fish and my husband had the filet - both of which exceeded our expectations .\n[{'aspect': 'fish', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'filet', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n->Next time , we would n't dare ordering anything else other than some simple Asian appetizers and drinks .\n[{'aspect': 'Asian appetizers', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this little chromebook is very nice and is pretty much what i expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little chromebook is very nice and is pretty much what i expected .\n->", + "output": "{\"text\": \"this little chromebook is very nice and is pretty much what i expected .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , after a couple of months the keyboard case started to crack at the corner .\n->however , after a couple of months the keyboard case started to crack at the corner .\n[{'aspect': 'keyboard case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: while finishing our meals which included a high - end bottle of wine , our son ' s fiance joined us for a glass of wine and dessert .\n->while finishing our meals which included a high - end bottle of wine , our son ' s fiance joined us for a glass of wine and dessert .\n[{'aspect': 'bottle of wine', 'opinion': 'high - end', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: this is my first chromebook purchase and i have to say that i ' m enjoying the speed and simplicity of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my first chromebook purchase and i have to say that i ' m enjoying the speed and simplicity of it .\n->", + "output": "{\"text\": \"this is my first chromebook purchase and i have to say that i ' m enjoying the speed and simplicity of it .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ' ve got android nougat on beta running pretty well now .\n->we ' ve got android nougat on beta running pretty well now .\n[{'aspect': 'android nougat', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: after running good for the initial 25 first days it won ' t power on .\n->after running good for the initial 25 first days it won ' t power on .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: in summary , it looks great and performs very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin summary , it looks great and performs very well .\n->", + "output": "{\"text\": \"in summary , it looks great and performs very well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n->the pizza was delivered cold and the cheese was n ' t even fully melted !\n[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: 10 months in my battery will no longer charge .\n->10 months in my battery will no longer charge .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: battery life has been very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life has been very good .\n->", + "output": "{\"text\": \"battery life has been very good .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the product came highly recommended .\n->the product came highly recommended .\n[{'aspect': 'product', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: guaranteed excellent customer service !\n->guaranteed excellent customer service !\n[{'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\ntext: acer makes a good unit too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer makes a good unit too .\n->", + "output": "{\"text\": \"acer makes a good unit too .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza is yummy and i like the atmoshpere .\n->the pizza is yummy and i like the atmoshpere .\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it 's the only place you can get yummy authentic japanese comfort food .\n->it 's the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i purchased this item 7 months ago and i love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni purchased this item 7 months ago and i love it .\n->", + "output": "{\"text\": \"i purchased this item 7 months ago and i love it .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n->Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n[{'aspect': 'group dinner', 'opinion': 'easy', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i plugged it back in , let it fully charge as directed and have had no problems since .\n->i plugged it back in , let it fully charge as directed and have had no problems since .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i purchased this chromebook strictly for writing / editing ( i ' m an independent and published author ) and web browsing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni purchased this chromebook strictly for writing / editing ( i ' m an independent and published author ) and web browsing .\n->", + "output": "{\"text\": \"i purchased this chromebook strictly for writing / editing ( i ' m an independent and published author ) and web browsing .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the most well priced laptop for its spec\n->this is the most well priced laptop for its spec\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'spec', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Overall , the best bagel in town .\n->Overall , the best bagel in town .\n[{'aspect': 'bagel', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n->", + "output": "{\"text\": \"the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\", \"labels\": \"[{'aspect': 'aluminum casing', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Baluchi 's has solid food and a nice decor at reasonable prices .\n->Baluchi 's has solid food and a nice decor at reasonable prices .\n[{'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n->i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n[{'aspect': 'beef cubes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the only thing i did not like about the design is the fact that the speakers are on the bottom of the unit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing i did not like about the design is the fact that the speakers are on the bottom of the unit .\n->", + "output": "{\"text\": \"the only thing i did not like about the design is the fact that the speakers are on the bottom of the unit .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'not like', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: giving lower stars for not realizing its limited capabilities is your own fault .\n->giving lower stars for not realizing its limited capabilities is your own fault .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: zero ambiance to boot .\n->zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'zero', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: it ' s beautiful and i love it , but i think i have to send it back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s beautiful and i love it , but i think i have to send it back .\n->", + "output": "{\"text\": \"it ' s beautiful and i love it , but i think i have to send it back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good food : my favorite is the seafood spaghetti .\n->good food : my favorite is the seafood spaghetti .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood spaghetti', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: outside of that , the keyboard is solid , the back lighting was not a selling point to me .\n->outside of that , the keyboard is solid , the back lighting was not a selling point to me .\n[{'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'back lighting', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: losing the function keys for a toucher was a deal breaker .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlosing the function keys for a toucher was a deal breaker .\n->", + "output": "{\"text\": \"losing the function keys for a toucher was a deal breaker .\", \"labels\": \"[{'aspect': 'function keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our family never expected such incredible entertainment in a restaurant .\n->Our family never expected such incredible entertainment in a restaurant .\n[{'aspect': 'entertainment', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n->battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: the base model went up in price $ 400 , which excludes any performance benefits .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe base model went up in price $ 400 , which excludes any performance benefits .\n->", + "output": "{\"text\": \"the base model went up in price $ 400 , which excludes any performance benefits .\", \"labels\": \"[{'aspect': 'base model', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambience is delightful , service impeccable .\n->Ambience is delightful , service impeccable .\n[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i wouldn ' t want to do any extensive typing on it .\n->i wouldn ' t want to do any extensive typing on it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: the only real upgrade for the new one , before adding on options is faster memory .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only real upgrade for the new one , before adding on options is faster memory .\n->", + "output": "{\"text\": \"the only real upgrade for the new one , before adding on options is faster memory .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Largest and freshest pieces of sushi , and delicious !\n->Largest and freshest pieces of sushi , and delicious !\n[{'aspect': 'pieces of sushi', 'opinion': 'Largest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we took advanatage of the half price sushi deal on saturday so it was well worth it .\n->we took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n->", + "output": "{\"text\": \"i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\", \"labels\": \"[{'aspect': 'the new upgrades', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n->We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n[{'aspect': 'quality', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'care', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This place has realy fresh sushi and a nice large menu of Japanese classic cuisine .\n->This place has realy fresh sushi and a nice large menu of Japanese classic cuisine .\n[{'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: while usb - c is cool , a bag full of dongles is not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile usb - c is cool , a bag full of dongles is not .\n->", + "output": "{\"text\": \"while usb - c is cool , a bag full of dongles is not .\", \"labels\": \"[{'aspect': 'usb - c', 'opinion': 'cool', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I really liked this place .\n->I really liked this place .\n[{'aspect': 'place', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: nice computer for the price .\n->nice computer for the price .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: with real function keys , i don ' t worry about one vendor or another not upgrading fast enough and losing virtual function keys , so this model is golden .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith real function keys , i don ' t worry about one vendor or another not upgrading fast enough and losing virtual function keys , so this model is golden .\n->", + "output": "{\"text\": \"with real function keys , i don ' t worry about one vendor or another not upgrading fast enough and losing virtual function keys , so this model is golden .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'golden', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'back room', 'opinion': 'secret', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n->if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n[{'aspect': 'sushi', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: anyway , if you are contemplating buying one of these and they are still available , do it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nanyway , if you are contemplating buying one of these and they are still available , do it .\n->", + "output": "{\"text\": \"anyway , if you are contemplating buying one of these and they are still available , do it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n->all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n[{'aspect': 'greek and cypriot dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'gyro', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it wouldn ' t charge .\n->it wouldn ' t charge .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: everything from the power cord to the computer looks brand new .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything from the power cord to the computer looks brand new .\n->", + "output": "{\"text\": \"everything from the power cord to the computer looks brand new .\", \"labels\": \"[{'aspect': 'power cord', 'opinion': 'new', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve had this for 3 days and so far the laptop is fine .\n->i ' ve had this for 3 days and so far the laptop is fine .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n->suddenly the keyboard goes crazy , starting on his own typing non stop ` ` mmmmmmmmm ` ` .\n[{'aspect': 'keyboard', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: in fact , the only way you know it ' s probably not new is because it didn ' t arrive in an apple original box ; it arrived wrapped ( neat and tight ) in bubble wrap in an amazon box .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin fact , the only way you know it ' s probably not new is because it didn ' t arrive in an apple original box ; it arrived wrapped ( neat and tight ) in bubble wrap in an amazon box .\n->", + "output": "{\"text\": \"in fact , the only way you know it ' s probably not new is because it didn ' t arrive in an apple original box ; it arrived wrapped ( neat and tight ) in bubble wrap in an amazon box .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not enough wines by the glass either .\n->Not enough wines by the glass either .\n[{'aspect': 'wines by the glass', 'opinion': 'Not enough', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the computer runs great .\n->the computer runs great .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: with the recent apple upgrade to macbook pros ( the touch of genius bar or whatever ) , the price has jumped .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith the recent apple upgrade to macbook pros ( the touch of genius bar or whatever ) , the price has jumped .\n->", + "output": "{\"text\": \"with the recent apple upgrade to macbook pros ( the touch of genius bar or whatever ) , the price has jumped .\", \"labels\": \"[{'aspect': 'macbook pros', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Chennai Garden is my favorite Indian restaurant in the city .\n->Chennai Garden is my favorite Indian restaurant in the city .\n[{'aspect': 'Chennai Garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i love how slim the design is - will fit easily into my backpack : ) highly recommend for everyday or school use !\n->i love how slim the design is - will fit easily into my backpack : ) highly recommend for everyday or school use !\n[{'aspect': 'design', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i can ' t justify paying that kind of money for some ridiculous upgrade .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can ' t justify paying that kind of money for some ridiculous upgrade .\n->", + "output": "{\"text\": \"i can ' t justify paying that kind of money for some ridiculous upgrade .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'ridiculous', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: terrible product and worse customer service - - do not buy\n->terrible product and worse customer service - - do not buy\n[{'aspect': 'product', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'customer service', 'opinion': 'worse', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: The restuarant itself is not large , but seems to have several round tables to accomodate large groups hoping to save a buck to eat authentic Taiwanese .\n->The restuarant itself is not large , but seems to have several round tables to accomodate large groups hoping to save a buck to eat authentic Taiwanese .\n[{'aspect': 'round tables', 'opinion': 'several', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Taiwanese', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: happy i got a great machine for half the cost !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhappy i got a great machine for half the cost !\n->", + "output": "{\"text\": \"happy i got a great machine for half the cost !\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n->Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n[{'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The one vegetarian entree ( Abby 's treasure ) was actually quite a surprise - it was delicious and had wintermelon covering an assortment of fresh mushrooms and vegetables .\n->The one vegetarian entree ( Abby 's treasure ) was actually quite a surprise - it was delicious and had wintermelon covering an assortment of fresh mushrooms and vegetables .\n[{'aspect': 'vegetarian entree', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetarian entree', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Abby 's treasure\", 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"Abby 's treasure\", 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assortment of fresh mushrooms and vegetables', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m seeing a lot of reviews saying they received the wrong item .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m seeing a lot of reviews saying they received the wrong item .\n->", + "output": "{\"text\": \"i ' m seeing a lot of reviews saying they received the wrong item .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and when it did work it was very slow .\n->and when it did work it was very slow .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city .\n->The food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n->", + "output": "{\"text\": \"the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mac is life , but i take one star away for the price .\n->mac is life , but i take one star away for the price .\n[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: monitor went out 35 days after receiving .\n->monitor went out 35 days after receiving .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: - new radeon graphics card ( finally ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- new radeon graphics card ( finally ! )\n->", + "output": "{\"text\": \"- new radeon graphics card ( finally ! )\", \"labels\": \"[{'aspect': 'radeon graphics card', 'opinion': 'new', 'polarity': 'positive', 'category': 'GRAPHICS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n->and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: * excellent form factor , extremely portable while remaining a serious pro computer\n->* excellent form factor , extremely portable while remaining a serious pro computer\n[{'aspect': 'pro computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro computer', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: - retina display is actually sharper .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- retina display is actually sharper .\n->", + "output": "{\"text\": \"- retina display is actually sharper .\", \"labels\": \"[{'aspect': 'retina display', 'opinion': 'sharper', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n->The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n[{'aspect': 'ingredients', 'opinion': 'fresher', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'crispier', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'slice', 'opinion': 'less oily', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: \u2022 bright screen with good colors\n->\u2022 bright screen with good colors\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: i thought that it wouldn ' t make any difference , but it does on games\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni thought that it wouldn ' t make any difference , but it does on games\n->", + "output": "{\"text\": \"i thought that it wouldn ' t make any difference , but it does on games\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen is bright , doesn ' t feel heavy .\n->screen is bright , doesn ' t feel heavy .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': \"' t feel heavy\", 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n->it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n[{'aspect': 'tablet', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: - the computer is 1 pound ( approx .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the computer is 1 pound ( approx .\n->", + "output": "{\"text\": \"- the computer is 1 pound ( approx .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - though the case is plastic , the keyboard area itself has a cold metallic feel .\n->- though the case is plastic , the keyboard area itself has a cold metallic feel .\n[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard area', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: we were n ' t !\n->we were n ' t !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: that is awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat is awesome .\n->", + "output": "{\"text\": \"that is awesome .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything works fast and smooth .\n->everything works fast and smooth .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: track pad is a little spongy , but definitely not a showstopper .\n->track pad is a little spongy , but definitely not a showstopper .\n[{'aspect': 'track pad', 'opinion': 'spongy', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: super fast boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuper fast boot .\n->", + "output": "{\"text\": \"super fast boot .\", \"labels\": \"[{'aspect': 'boot', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is can became on e of the ny italian food fare institutions .\n->this is can became on e of the ny italian food fare institutions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the food is flavorful , plentiful and reasonably priced .\n->the food is flavorful , plentiful and reasonably priced .\n[{'aspect': 'food', 'opinion': 'flavorful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n->", + "output": "{\"text\": \"when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'okay', 'polarity': 'positive', 'category': 'FANS&COOLING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Staff is very accomodating .\n->Staff is very accomodating .\n[{'aspect': 'Staff', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: unfortunately , before i purchased it , i failed to research what the thunderbolt ports were .\n->unfortunately , before i purchased it , i failed to research what the thunderbolt ports were .\n[{'aspect': 'thunderbolt ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: the plug keeps unplugging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe plug keeps unplugging .\n->", + "output": "{\"text\": \"the plug keeps unplugging .\", \"labels\": \"[{'aspect': 'plug', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n->The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n[{'aspect': 'in-house lady DJ', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was very disappointed with this restaurant .\n->i was very disappointed with this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: with magsafe 2 , even the gentlest pull makes the plug to disconnect , which is very annoying for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith magsafe 2 , even the gentlest pull makes the plug to disconnect , which is very annoying for me .\n->", + "output": "{\"text\": \"with magsafe 2 , even the gentlest pull makes the plug to disconnect , which is very annoying for me .\", \"labels\": \"[{'aspect': 'plug', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I love the fact that the pizza tastes so good and is so cheap .\n->I love the fact that the pizza tastes so good and is so cheap .\n[{'aspect': 'pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it has been great for everything i ' ve done .\n->it has been great for everything i ' ve done .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: - memory is not easily upgradable anymore .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- memory is not easily upgradable anymore .\n->", + "output": "{\"text\": \"- memory is not easily upgradable anymore .\", \"labels\": \"[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n->so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: - my only real gripe is that i wish it was .\n->- my only real gripe is that i wish it was .\n[{'aspect': 'NULL', 'opinion': 'gripe', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: - ssd ( solid state drive ) is not easily upgradable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- ssd ( solid state drive ) is not easily upgradable .\n->", + "output": "{\"text\": \"- ssd ( solid state drive ) is not easily upgradable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n->stylus also failed and is now stuck inside the laptop so i can ' t really use it for screen capturing .\n[{'aspect': 'stylus', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'stylus', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: We had a party in their private room and they made it truly memorable and were very helpful in the planning .\n->We had a party in their private room and they made it truly memorable and were very helpful in the planning .\n[{'aspect': 'private room', 'opinion': 'truly memorable', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: - no ethernet connection and firewire 800 port .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- no ethernet connection and firewire 800 port .\n->", + "output": "{\"text\": \"- no ethernet connection and firewire 800 port .\", \"labels\": \"[{'aspect': 'firewire 800 port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n->Yet paired with such rude service , would never recommend for anyone interested in carrying any kind of conversation while there .\n[{'aspect': 'service', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: we were worried we would have trouble getting in , but somehow managed to have a short wait .\n->we were worried we would have trouble getting in , but somehow managed to have a short wait .\n[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: - no audio in port .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- no audio in port .\n->", + "output": "{\"text\": \"- no audio in port .\", \"labels\": \"[{'aspect': 'audio in port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this one is pretty , but obviously not sturdy .\n->this one is pretty , but obviously not sturdy .\n[{'aspect': 'NULL', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not sturdy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the keyboard is ok .\n->the keyboard is ok .\n[{'aspect': 'keyboard', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\ntext: now audio in and out are combined in just one port .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow audio in and out are combined in just one port .\n->", + "output": "{\"text\": \"now audio in and out are combined in just one port .\", \"labels\": \"[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - retina display is nice but not mind blowing as other have suggested\n->- retina display is nice but not mind blowing as other have suggested\n[{'aspect': 'retina display', 'opinion': 'nice', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'never had a problem', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i don ' t like this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t like this .\n->", + "output": "{\"text\": \"i don ' t like this .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n->ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the food is prepared quickly and efficiently .\n->the food is prepared quickly and efficiently .\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: it ' s very expensive for such a simple device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s very expensive for such a simple device .\n->", + "output": "{\"text\": \"it ' s very expensive for such a simple device .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu has lots of options : I hope to go back to try those potato pancakes .\n->The menu has lots of options : I hope to go back to try those potato pancakes .\n[{'aspect': 'menu', 'opinion': 'lots', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'potato pancakes', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - the screen is harder to clean .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the screen is harder to clean .\n->", + "output": "{\"text\": \"- the screen is harder to clean .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You will pay a lot for the decore , but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY .\n->You will pay a lot for the decore , but the food is no better or worse than a lot of other Chinese and Asian fusion places in NY .\n[{'aspect': 'decore', 'opinion': 'pay a lot', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'no better or worse', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The exotic food is beautifully presented and is a delight in delicious combinations .\n->The exotic food is beautifully presented and is a delight in delicious combinations .\n[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - keyboard keys have a shorter touch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- keyboard keys have a shorter touch .\n->", + "output": "{\"text\": \"- keyboard keys have a shorter touch .\", \"labels\": \"[{'aspect': 'keyboard keys', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mousepad is a little wonky if you ' re not deliberate with your fingers , recommend using a mouse .\n->mousepad is a little wonky if you ' re not deliberate with your fingers , recommend using a mouse .\n[{'aspect': 'mousepad', 'opinion': 'wonky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m okay with that given that the body is thinner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m okay with that given that the body is thinner .\n->", + "output": "{\"text\": \"i ' m okay with that given that the body is thinner .\", \"labels\": \"[{'aspect': 'body', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n->There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n[{'aspect': 'delivery guys', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the broken mic was not a dealbreaker but annoying in a brand new device .\n->the broken mic was not a dealbreaker but annoying in a brand new device .\n[{'aspect': 'mic', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: in the beginning i felt a little weird about this trackpad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin the beginning i felt a little weird about this trackpad .\n->", + "output": "{\"text\": \"in the beginning i felt a little weird about this trackpad .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'weird', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very impressive design .\n->very impressive design .\n[{'aspect': 'design', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: asus is a great computer company .\n->asus is a great computer company .\n[{'aspect': 'asus', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'computer company', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: overall , i think it is a great purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , i think it is a great purchase .\n->", + "output": "{\"text\": \"overall , i think it is a great purchase .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have the iced tea .\n->have the iced tea .\n[{'aspect': 'iced tea', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the design and atmosphere is just as good .\n->the design and atmosphere is just as good .\n[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: with how slim this thing is i really don ' t see a need for the macbook air line .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith how slim this thing is i really don ' t see a need for the macbook air line .\n->", + "output": "{\"text\": \"with how slim this thing is i really don ' t see a need for the macbook air line .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was pretty tradional but it was hot and good with large portions .\n->The food was pretty tradional but it was hot and good with large portions .\n[{'aspect': 'food', 'opinion': 'tradional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: manufactures need to quality check their products before sending them out .\n->manufactures need to quality check their products before sending them out .\n[{'aspect': 'manufactures', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\ntext: the display is fantastic on this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display is fantastic on this laptop .\n->", + "output": "{\"text\": \"the display is fantastic on this laptop .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sound is not real loud from the speakers , but i am reasonably pleased with the quality .\n->the sound is not real loud from the speakers , but i am reasonably pleased with the quality .\n[{'aspect': 'sound', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'speakers', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: Try green curry with vegetables .\n->Try green curry with vegetables .\n[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: battery life : i haven ' t had the laptop for too long but easily get 8 hours or more on a charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life : i haven ' t had the laptop for too long but easily get 8 hours or more on a charge .\n->", + "output": "{\"text\": \"battery life : i haven ' t had the laptop for too long but easily get 8 hours or more on a charge .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve used this daily for nearly eight months and have been very happy with .\n->i ' ve used this daily for nearly eight months and have been very happy with .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the most recent incident is the sound wo n ' t work .\n->the most recent incident is the sound wo n ' t work .\n[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: the keyboard is ok .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is ok .\n->", + "output": "{\"text\": \"the keyboard is ok .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The flavors are very fresh and pretty unobtrusive , nothing flashy .\n->The flavors are very fresh and pretty unobtrusive , nothing flashy .\n[{'aspect': 'flavors', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavors', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is an okay laptop and nothing more .\n->it is an okay laptop and nothing more .\n[{'aspect': 'laptop', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: other than that , the key sizes are good and the backlighting works just fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother than that , the key sizes are good and the backlighting works just fine .\n->", + "output": "{\"text\": \"other than that , the key sizes are good and the backlighting works just fine .\", \"labels\": \"[{'aspect': 'key', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'backlighting', 'opinion': 'fine', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: losing the function keys for a toucher was a deal breaker .\n->losing the function keys for a toucher was a deal breaker .\n[{'aspect': 'function keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the computer literally blue screened on the second day because system 32 was corrupt .\n->the computer literally blue screened on the second day because system 32 was corrupt .\n[{'aspect': 'system 32', 'opinion': 'corrupt', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the trackpad has gotten better with this new model .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe trackpad has gotten better with this new model .\n->", + "output": "{\"text\": \"the trackpad has gotten better with this new model .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use this for leisure activities and the ability to flip this around to watch movies is awesome !\n->i use this for leisure activities and the ability to flip this around to watch movies is awesome !\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n->i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: the click is now provided by a haptic engine and provides more functionality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe click is now provided by a haptic engine and provides more functionality .\n->", + "output": "{\"text\": \"the click is now provided by a haptic engine and provides more functionality .\", \"labels\": \"[{'aspect': 'click', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ca n ' t wait to go back .\n->ca n ' t wait to go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: murray wo n ' t do it .\n->murray wo n ' t do it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the thing is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe thing is fantastic .\n->", + "output": "{\"text\": \"the thing is fantastic .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only problem is that certain streaming video applications freeze up in windows 10 and i can not remember how i resolved the problem on my old laptop .\n->the only problem is that certain streaming video applications freeze up in windows 10 and i can not remember how i resolved the problem on my old laptop .\n[{'aspect': 'streaming video applications', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: absolutely terrible quality control to not even get past the initial boot .\n->absolutely terrible quality control to not even get past the initial boot .\n[{'aspect': 'quality control', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: this is easily the best trackpad i have ever used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is easily the best trackpad i have ever used .\n->", + "output": "{\"text\": \"this is easily the best trackpad i have ever used .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n->I must say it 's a little pricey for the food because it was not as spectacular as the view .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n->The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n[{'aspect': 'three course meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: apps open instantly and the ac wifi performance is very nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napps open instantly and the ac wifi performance is very nice .\n->", + "output": "{\"text\": \"apps open instantly and the ac wifi performance is very nice .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'instantly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'ac wifi', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cons : webcam doesn ' t have video - only takes pics ; awful , uncomfortable keyboard & trackpad ; chromebook extensions are lacking and don ' t address / make up for the limitations of this chromebook ; a bit heavy and clunky ; hard to figure out google cloud print\n->cons : webcam doesn ' t have video - only takes pics ; awful , uncomfortable keyboard & trackpad ; chromebook extensions are lacking and don ' t address / make up for the limitations of this chromebook ; a bit heavy and clunky ; hard to figure out google cloud print\n[{'aspect': 'webcam', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'clunky', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the screen is pleasantly satisfying with the touch screen and foldability .\n->the screen is pleasantly satisfying with the touch screen and foldability .\n[{'aspect': 'screen', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: you can even run time machine backups while the computer is sleeping now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can even run time machine backups while the computer is sleeping now .\n->", + "output": "{\"text\": \"you can even run time machine backups while the computer is sleeping now .\", \"labels\": \"[{'aspect': 'machine backups', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it calls itself a computer , but it ' s really not .\n->it calls itself a computer , but it ' s really not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i highly recommend this product to any one whose needs are simple and mostly web based .\n->i highly recommend this product to any one whose needs are simple and mostly web based .\n[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' m not sure when this feature was introduced , but it is very welcome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m not sure when this feature was introduced , but it is very welcome .\n->", + "output": "{\"text\": \"i ' m not sure when this feature was introduced , but it is very welcome .\", \"labels\": \"[{'aspect': 'feature', 'opinion': 'welcome', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: running android apps themselves are a pretty ` ` meh ` ` experience .\n->running android apps themselves are a pretty ` ` meh ` ` experience .\n[{'aspect': 'android apps', 'opinion': 'meh', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food is tasty and portion sizes are appropriate .\n->the food is tasty and portion sizes are appropriate .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the os is easy to use , the app store lacks many apps that i use , so i have to go directly to the developers site to download them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe os is easy to use , the app store lacks many apps that i use , so i have to go directly to the developers site to download them .\n->", + "output": "{\"text\": \"the os is easy to use , the app store lacks many apps that i use , so i have to go directly to the developers site to download them .\", \"labels\": \"[{'aspect': 'os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'app store', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza is delicious and the proprietor is one of the nicest in nyc .\n->the pizza is delicious and the proprietor is one of the nicest in nyc .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: for 7 years they have put out the most tasty , most delicious food and kept it that way . . .\n->for 7 years they have put out the most tasty , most delicious food and kept it that way . . .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i have not messed with the continuity feature much , but it is nice having the favorites and open tabs sync from my laptop to my iphone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have not messed with the continuity feature much , but it is nice having the favorites and open tabs sync from my laptop to my iphone .\n->", + "output": "{\"text\": \"i have not messed with the continuity feature much , but it is nice having the favorites and open tabs sync from my laptop to my iphone .\", \"labels\": \"[{'aspect': 'continuity feature', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}, {'aspect': 'continuity feature', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: greatest thing i ' ve bought myself in a long time .\n->greatest thing i ' ve bought myself in a long time .\n[{'aspect': 'NULL', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'lemon salad', 'opinion': 'exception', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the current model only has two , which can be problematic if you hook up an external hard drive and an external bluray drive ( which requires two ports ) at the same time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe current model only has two , which can be problematic if you hook up an external hard drive and an external bluray drive ( which requires two ports ) at the same time .\n->", + "output": "{\"text\": \"the current model only has two , which can be problematic if you hook up an external hard drive and an external bluray drive ( which requires two ports ) at the same time .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'problematic', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really love the laptop !\n->i really love the laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n->and it all comes at a very reasonable price ( congee , noodles , and rice dishes are no more than $ 3 - 6 each ) .\n[{'aspect': 'NULL', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'congee', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'noodles', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'rice dishes', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n->", + "output": "{\"text\": \"sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in essence , if you want a gaming pc , this one will do the job .\n->in essence , if you want a gaming pc , this one will do the job .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n->we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'delight', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: it ' s nice and lightweight , the sd port is useful , and it ' s powerful enough for me to do fairly layer intensive graphic design work in photoshop and basic grading in lightroom .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s nice and lightweight , the sd port is useful , and it ' s powerful enough for me to do fairly layer intensive graphic design work in photoshop and basic grading in lightroom .\n->", + "output": "{\"text\": \"it ' s nice and lightweight , the sd port is useful , and it ' s powerful enough for me to do fairly layer intensive graphic design work in photoshop and basic grading in lightroom .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'sd port', 'opinion': 'useful', 'polarity': 'positive', 'category': 'PORTS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food however , is what one might expect .\n->the food however , is what one might expect .\n[{'aspect': 'food', 'opinion': 'expect', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the system is quick , and used for browsing , and basic notes .\n->the system is quick , and used for browsing , and basic notes .\n[{'aspect': 'system', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the keys feel fantastic to type on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keys feel fantastic to type on .\n->", + "output": "{\"text\": \"the keys feel fantastic to type on .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The shrimp scampi was excellent and the antipasti were plentiful .\n->The shrimp scampi was excellent and the antipasti were plentiful .\n[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the kitchen however , is almost always slow .\n->the kitchen however , is almost always slow .\n[{'aspect': 'kitchen', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the overall design is sleek and pleasing to look at and hold .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe overall design is sleek and pleasing to look at and hold .\n->", + "output": "{\"text\": \"the overall design is sleek and pleasing to look at and hold .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'pleasing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pixels are currently stuck .\n->the pixels are currently stuck .\n[{'aspect': 'pixels', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The selection changes frequently but the basic dishes are always available .\n->The selection changes frequently but the basic dishes are always available .\n[{'aspect': 'selection', 'opinion': 'changes frequently', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'basic dishes', 'opinion': 'available', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the force touch trackpad works great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe force touch trackpad works great .\n->", + "output": "{\"text\": \"the force touch trackpad works great .\", \"labels\": \"[{'aspect': 'force touch trackpad', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Calling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n->Calling the place Hampton Chutney Co. does warn you that these folks offer more style than substance , but in this unattractive room with unhelpful clerks there was a dearth of the former too .\n[{'aspect': 'place', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'room', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'clerks', 'opinion': 'unhelpful', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: overall , decent food at a good price , with friendly people .\n->overall , decent food at a good price , with friendly people .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: overall , i feel this is the best laptop i ' ve ever purchased or used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , i feel this is the best laptop i ' ve ever purchased or used .\n->", + "output": "{\"text\": \"overall , i feel this is the best laptop i ' ve ever purchased or used .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n->we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n[{'aspect': 'r11', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i got hair in my food 2 times of then !\n->i got hair in my food 2 times of then !\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: it feels sturdy and reliable , both hardware and software - wise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit feels sturdy and reliable , both hardware and software - wise .\n->", + "output": "{\"text\": \"it feels sturdy and reliable , both hardware and software - wise .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'hardware', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'software', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'software', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You should pass on the calamari .\n->You should pass on the calamari .\n[{'aspect': 'calamari', 'opinion': 'pass', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: then tonight ( april 29th ) my daughter says it ' s not charging .\n->then tonight ( april 29th ) my daughter says it ' s not charging .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i waited for a while before writing a review about this product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni waited for a while before writing a review about this product .\n->", + "output": "{\"text\": \"i waited for a while before writing a review about this product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we have been to this place many times , and always have great food , wine , and service .\n->we have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: everyone must come here at least once .\n->everyone must come here at least once .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: after a couple of months of using this product , i finally could say that it ' s the best thing that i have ever bought up to this moment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter a couple of months of using this product , i finally could say that it ' s the best thing that i have ever bought up to this moment .\n->", + "output": "{\"text\": \"after a couple of months of using this product , i finally could say that it ' s the best thing that i have ever bought up to this moment .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they are lightweight and easy to carry .\n->they are lightweight and easy to carry .\n[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the keyboard is comfortable to type with backlit , screen quality is good enough for me .\n->the keyboard is comfortable to type with backlit , screen quality is good enough for me .\n[{'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'screen quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: regardless to the fact that the outside box was damaged , the inside box was fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nregardless to the fact that the outside box was damaged , the inside box was fine .\n->", + "output": "{\"text\": \"regardless to the fact that the outside box was damaged , the inside box was fine .\", \"labels\": \"[{'aspect': 'outside box', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'inside box', 'opinion': 'fine', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n->i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n[{'aspect': 'asus customer service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: udpate : after talking to tech support , i found out that they made the wrong repair .\n->udpate : after talking to tech support , i found out that they made the wrong repair .\n[{'aspect': 'tech support', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: i use this laptop every single day for at least 2 hours and believe me it ' s just great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use this laptop every single day for at least 2 hours and believe me it ' s just great .\n->", + "output": "{\"text\": \"i use this laptop every single day for at least 2 hours and believe me it ' s just great .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n->even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n[{'aspect': 'touchpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: The wine list is also really nice .\n->The wine list is also really nice .\n[{'aspect': 'wine list', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: therefore , my advice to you if you ' re a student and you need a laptop for school , this laptop is the best choice for you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntherefore , my advice to you if you ' re a student and you need a laptop for school , this laptop is the best choice for you .\n->", + "output": "{\"text\": \"therefore , my advice to you if you ' re a student and you need a laptop for school , this laptop is the best choice for you .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend the Sophia pizza .\n->I highly recommend the Sophia pizza .\n[{'aspect': 'Sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Also a little more expensive than your average bagel place .\n->Also a little more expensive than your average bagel place .\n[{'aspect': 'bagel', 'opinion': 'expensive', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n->", + "output": "{\"text\": \"and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'not worry', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it arrived in ` ` as new ` ` condition despite being a refurbished ( likely a return ) .\n->it arrived in ` ` as new ` ` condition despite being a refurbished ( likely a return ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Do n't waste money on decor .\n->Do n't waste money on decor .\n[{'aspect': 'decor', 'opinion': 'waste', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i ' m glad i got this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m glad i got this .\n->", + "output": "{\"text\": \"i ' m glad i got this .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love chromebooks and have been using them before they were available to the public .\n->i love chromebooks and have been using them before they were available to the public .\n[{'aspect': 'chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I think I 've had some the best meals of my life at minnow .\n->I think I 've had some the best meals of my life at minnow .\n[{'aspect': 'meals', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: amazon warehouse marked this as ` ` like new ` ` but upon receiving , this unit was brand new .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazon warehouse marked this as ` ` like new ` ` but upon receiving , this unit was brand new .\n->", + "output": "{\"text\": \"amazon warehouse marked this as ` ` like new ` ` but upon receiving , this unit was brand new .\", \"labels\": \"[{'aspect': 'this unit', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this product for black friday and i have been using it steadily since then .\n->i bought this product for black friday and i have been using it steadily since then .\n[{'aspect': 'product', 'opinion': 'steadily', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The pizza is yummy and I like the atmoshpere .\n->The pizza is yummy and I like the atmoshpere .\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i am very happy with this purchase especially for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very happy with this purchase especially for the price .\n->", + "output": "{\"text\": \"i am very happy with this purchase especially for the price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everythig about it - especially the shows and actors .\n->i loved everythig about it - especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: awesome display .\n->awesome display .\n[{'aspect': 'display', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: at the time of this posting , this model was going only for 1800 for the 512gb with a dedicated video card .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat the time of this posting , this model was going only for 1800 for the 512gb with a dedicated video card .\n->", + "output": "{\"text\": \"at the time of this posting , this model was going only for 1800 for the 512gb with a dedicated video card .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fine dining restaurant quality .\n->fine dining restaurant quality .\n[{'aspect': 'quality', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it take a bit of getting used to .\n->it take a bit of getting used to .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}]\ntext: win for the ports , win for the price , and win for a brand new unopened macbook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwin for the ports , win for the price , and win for a brand new unopened macbook .\n->", + "output": "{\"text\": \"win for the ports , win for the price , and win for a brand new unopened macbook .\", \"labels\": \"[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: slightly on the pricey side but worth it !\n->slightly on the pricey side but worth it !\n[{'aspect': 'NULL', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Great service , great food .\n->Great service , great food .\n[{'aspect': 'service', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love love love this laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove love love this laptop !\n->", + "output": "{\"text\": \"love love love this laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: price is high but the food is good , so i would come back again .\n->price is high but the food is good , so i would come back again .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'high', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it has a weird smell that ' s why i ' m giving it 3 stars .\n->it has a weird smell that ' s why i ' m giving it 3 stars .\n[{'aspect': 'NULL', 'opinion': 'weird', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the display is excellent !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display is excellent !\n->", + "output": "{\"text\": \"the display is excellent !\", \"labels\": \"[{'aspect': 'display', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: never again will i buy samsung products and thats what i ' d suggest here .\n->never again will i buy samsung products and thats what i ' d suggest here .\n[{'aspect': 'samsung products', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: and the blue mail email app handles my multiple emails from multiple domains .\n->and the blue mail email app handles my multiple emails from multiple domains .\n[{'aspect': 'blue mail email app', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: you can actually take a picture of this screen and not have near the amount of wavy lines that you used to get .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can actually take a picture of this screen and not have near the amount of wavy lines that you used to get .\n->", + "output": "{\"text\": \"you can actually take a picture of this screen and not have near the amount of wavy lines that you used to get .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mizu is home to creative and unique rolls not to found anywhere else .\n->mizu is home to creative and unique rolls not to found anywhere else .\n[{'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n->it ' s a little out of our price range for dining there except on special occasions , but we ' ve eaten there 6 times in the last 2 years .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: i really like the fact that when it boots up , it ' s up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really like the fact that when it boots up , it ' s up .\n->", + "output": "{\"text\": \"i really like the fact that when it boots up , it ' s up .\", \"labels\": \"[{'aspect': 'boots up', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it can not .\n->it can not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n->the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n[{'aspect': 'laptop', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'spec', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: this thing boots up , you log in and you ' re ready to go !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis thing boots up , you log in and you ' re ready to go !\n->", + "output": "{\"text\": \"this thing boots up , you log in and you ' re ready to go !\", \"labels\": \"[{'aspect': 'boots up', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speaking of asus flip itself - it is great in every way !\n->speaking of asus flip itself - it is great in every way !\n[{'aspect': 'asus flip', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it take a bit of getting used to .\n->it take a bit of getting used to .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}]\ntext: my review is based on the fact that i got a pretty good value on this when it was selling for $ 200 less than normal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy review is based on the fact that i got a pretty good value on this when it was selling for $ 200 less than normal .\n->", + "output": "{\"text\": \"my review is based on the fact that i got a pretty good value on this when it was selling for $ 200 less than normal .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worth visiting the 1st ave spot because it is the original store .\n->worth visiting the 1st ave spot because it is the original store .\n[{'aspect': '1st ave spot', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n->", + "output": "{\"text\": \"i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'biased', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she replied ` ` well it would be more convenient for us if you ordered now , since you are a larger party , and it might get crowded . ' '\n->she replied ` ` well it would be more convenient for us if you ordered now , since you are a larger party , and it might get crowded . ' '\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: please do n ' t fool us .\n->please do n ' t fool us .\n[{'aspect': 'NULL', 'opinion': 'fool', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: if you ' re considering a switch from windows based pcs to apple computers , i ' d probably recommend that you look at what you ' re going to use it for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re considering a switch from windows based pcs to apple computers , i ' d probably recommend that you look at what you ' re going to use it for .\n->", + "output": "{\"text\": \"if you ' re considering a switch from windows based pcs to apple computers , i ' d probably recommend that you look at what you ' re going to use it for .\", \"labels\": \"[{'aspect': 'apple computers', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n->We took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the sashimi is always fresh and the rolls are innovative and delicious .\n->the sashimi is always fresh and the rolls are innovative and delicious .\n[{'aspect': 'sashimi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: for some of us , even though they are more expensive , they still offer the better value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor some of us , even though they are more expensive , they still offer the better value .\n->", + "output": "{\"text\": \"for some of us , even though they are more expensive , they still offer the better value .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were very pleasantly surprised .\n->we were very pleasantly surprised .\n[{'aspect': 'NULL', 'opinion': 'pleasantly surprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The food was average or above including some surprising tasty dishes .\n->The food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: fantastic computer !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfantastic computer !\n->", + "output": "{\"text\": \"fantastic computer !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chrome book joins the group and is itself excellent and different .\n->this chrome book joins the group and is itself excellent and different .\n[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chrome book', 'opinion': 'different', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it started to get slow a week ago .\n->it started to get slow a week ago .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i was due to upgrade and this product seemed perfect for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was due to upgrade and this product seemed perfect for me .\n->", + "output": "{\"text\": \"i was due to upgrade and this product seemed perfect for me .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n->This is the perfect spot for meeting friends , having lunch , dinner , pre-theatre or after-theatre drinks !\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: salads are a delicious way to begin the meal .\n->salads are a delicious way to begin the meal .\n[{'aspect': 'salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i have had three macbook pro ' s and all have been excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had three macbook pro ' s and all have been excellent .\n->", + "output": "{\"text\": \"i have had three macbook pro ' s and all have been excellent .\", \"labels\": \"[{'aspect': \"macbook pro ' s\", 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love , love , love this computer .\n->i love , love , love this computer .\n[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Fish was overdone .\n->Fish was overdone .\n[{'aspect': 'Fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the key board is one of the best i ' ve ever typed on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe key board is one of the best i ' ve ever typed on .\n->", + "output": "{\"text\": \"the key board is one of the best i ' ve ever typed on .\", \"labels\": \"[{'aspect': 'key board', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not only is the cuisine the best around , the service has always been attentive and charming .\n->not only is the cuisine the best around , the service has always been attentive and charming .\n[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: poor service and management\n->poor service and management\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: this is my first ever macbook and i will never go back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my first ever macbook and i will never go back .\n->", + "output": "{\"text\": \"this is my first ever macbook and i will never go back .\", \"labels\": \"[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n->wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i love the feel of a lighter os and can do many tasks using google / web based apps .\n->i love the feel of a lighter os and can do many tasks using google / web based apps .\n[{'aspect': 'os', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: i think the mbp ticks off more of the nice - to - have boxes for me than the xps13 overall .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think the mbp ticks off more of the nice - to - have boxes for me than the xps13 overall .\n->", + "output": "{\"text\": \"i think the mbp ticks off more of the nice - to - have boxes for me than the xps13 overall .\", \"labels\": \"[{'aspect': 'mbp', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the trackpad works well and the screen display is great too .\n->the trackpad works well and the screen display is great too .\n[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'screen display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: the mbp does not run as hot as my old white mb did .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mbp does not run as hot as my old white mb did .\n->", + "output": "{\"text\": \"the mbp does not run as hot as my old white mb did .\", \"labels\": \"[{'aspect': 'mbp', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a large is $ 20 , and toppings are about $ 3 each .\n->a large is $ 20 , and toppings are about $ 3 each .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'toppings', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: the mac pro is also very fast , and i have only experienced the rainbow wheel once or twice whenever i was on a website that wasn ' t responding .\n->the mac pro is also very fast , and i have only experienced the rainbow wheel once or twice whenever i was on a website that wasn ' t responding .\n[{'aspect': 'mac pro', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the battery life is really pretty comparable ( slightly more with the mbp , and i can obtain info on app energy usage a bit easier imo ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is really pretty comparable ( slightly more with the mbp , and i can obtain info on app energy usage a bit easier imo ) .\n->", + "output": "{\"text\": \"the battery life is really pretty comparable ( slightly more with the mbp , and i can obtain info on app energy usage a bit easier imo ) .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'comparable', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was slow had to wait to order and get food although not crowded .\n->service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n->but that was n ' t the icing on the cake : a tiramisu that resembled nothing i have ever had .\n[{'aspect': 'tiramisu', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the trackpad is the ultimate difference - maker for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe trackpad is the ultimate difference - maker for me .\n->", + "output": "{\"text\": \"the trackpad is the ultimate difference - maker for me .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is my only device with this issue in my home .\n->it is my only device with this issue in my home .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n->having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n[{'aspect': 'win 8', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the mbp trackpad is best in class and if you are not using a mouse , this makes a huge difference .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mbp trackpad is best in class and if you are not using a mouse , this makes a huge difference .\n->", + "output": "{\"text\": \"the mbp trackpad is best in class and if you are not using a mouse , this makes a huge difference .\", \"labels\": \"[{'aspect': 'mbp trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n->I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n[{'aspect': 'jelly fish', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drunken chicken', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soupy dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'stir fry blue crab', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: love yuka .\n->love yuka .\n[{'aspect': 'yuka', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the mbp 13 ` ` is plenty mobile .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mbp 13 ` ` is plenty mobile .\n->", + "output": "{\"text\": \"the mbp 13 ` ` is plenty mobile .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we thought that this place is using too much of msg cooking in the foods .\n->we thought that this place is using too much of msg cooking in the foods .\n[{'aspect': 'foods', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: leon is an east village gem : casual but hip , with well prepared basic french bistro fare , good specials , a warm and lively atmosphere .\n->leon is an east village gem : casual but hip , with well prepared basic french bistro fare , good specials , a warm and lively atmosphere .\n[{'aspect': 'leon', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'leon', 'opinion': 'hip', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'specials', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'lively', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'french bistro fare', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n->", + "output": "{\"text\": \"the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google docs / drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the processor is very snappy and will likely never be an issue for anything a normal chromebook user would need it for .\n->the processor is very snappy and will likely never be an issue for anything a normal chromebook user would need it for .\n[{'aspect': 'processor', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: delivery guy sometimes get upset if you do n ' t tip more than 10 % .\n->delivery guy sometimes get upset if you do n ' t tip more than 10 % .\n[{'aspect': 'delivery guy', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: adobe apps are also power hogs , but that is to be expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nadobe apps are also power hogs , but that is to be expected .\n->", + "output": "{\"text\": \"adobe apps are also power hogs , but that is to be expected .\", \"labels\": \"[{'aspect': 'adobe apps', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but how expensive it is .\n->but how expensive it is .\n[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the worst excuse for japanese food i ' ve ever encountered .\n->the worst excuse for japanese food i ' ve ever encountered .\n[{'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i also feel like apple hides too much under the hood from the user , but i suppose you can still work around that if you are so inclined .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni also feel like apple hides too much under the hood from the user , but i suppose you can still work around that if you are so inclined .\n->", + "output": "{\"text\": \"i also feel like apple hides too much under the hood from the user , but i suppose you can still work around that if you are so inclined .\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: openvpn support needs some serious work still .\n->openvpn support needs some serious work still .\n[{'aspect': 'openvpn support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#DESIGN_FEATURES'}]\nExample:\ntext: If you go for the pre-theatre menu , it 's an even greater deal .\n->If you go for the pre-theatre menu , it 's an even greater deal .\n[{'aspect': 'pre-theatre menu', 'opinion': 'greater', 'polarity': 'positive', 'category': 'NULL'}]\ntext: an unexpected benefit for me has been the iphone / mbp integration .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nan unexpected benefit for me has been the iphone / mbp integration .\n->", + "output": "{\"text\": \"an unexpected benefit for me has been the iphone / mbp integration .\", \"labels\": \"[{'aspect': 'iphone / mbp integration', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Fish was overdone .\n->Fish was overdone .\n[{'aspect': 'Fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i love the airdrop and receiving imessages on my mbp ( although it can cut into productivity if you ' re not careful ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love the airdrop and receiving imessages on my mbp ( although it can cut into productivity if you ' re not careful ) .\n->", + "output": "{\"text\": \"i love the airdrop and receiving imessages on my mbp ( although it can cut into productivity if you ' re not careful ) .\", \"labels\": \"[{'aspect': 'airdrop', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can use this for school .\n->i can use this for school .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the keyboard is good .\n->the keyboard is good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: overall , it ' s a great machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , it ' s a great machine .\n->", + "output": "{\"text\": \"overall , it ' s a great machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n->I really like both the scallops and the mahi mahi ( on saffron risotto yum ! ) .\n[{'aspect': 'scallops', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mahi mahi ( on saffron risotto', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n->i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n[{'aspect': 'asus support', 'opinion': 'sloth', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n->", + "output": "{\"text\": \"it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is an amazing place to try some roti rolls .\n->This is an amazing place to try some roti rolls .\n[{'aspect': 'roti rolls', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: keyboard problems too .\n->keyboard problems too .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: it works great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit works great !\n->", + "output": "{\"text\": \"it works great !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recommend the meatballs and caprese salad and the beans on toast were a wonderful start to the meal !\n->I recommend the meatballs and caprese salad and the beans on toast were a wonderful start to the meal !\n[{'aspect': 'meatballs', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caprese salad', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beans on toast', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'wonderful', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: no desert menu , no apology , nothing ! ! ! ! ! !\n->no desert menu , no apology , nothing ! ! ! ! ! !\n[{'aspect': 'NULL', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i updated it to high sierra and it ' s running smoothly so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni updated it to high sierra and it ' s running smoothly so far .\n->", + "output": "{\"text\": \"i updated it to high sierra and it ' s running smoothly so far .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Salads are a delicious way to begin the meal .\n->Salads are a delicious way to begin the meal .\n[{'aspect': 'Salads', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: well . . . they can run but they ca n ' t hide .\n->well . . . they can run but they ca n ' t hide .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: battery life is good , screen looks fine , and all of the keys and ports are functional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is good , screen looks fine , and all of the keys and ports are functional .\n->", + "output": "{\"text\": \"battery life is good , screen looks fine , and all of the keys and ports are functional .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keys', 'opinion': 'functional', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'ports', 'opinion': 'functional', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was bland oily .\n->The food was bland oily .\n[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: however , it charges insanely quickly : you can get a full charge in under an hour .\n->however , it charges insanely quickly : you can get a full charge in under an hour .\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: the only thing i didn ' t like is the big dent on the front of the lid .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing i didn ' t like is the big dent on the front of the lid .\n->", + "output": "{\"text\": \"the only thing i didn ' t like is the big dent on the front of the lid .\", \"labels\": \"[{'aspect': 'dent', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n->this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n[{'aspect': 'silver bullet', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'silver bullet', 'opinion': 'functional', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: dessert is a joke . . . dont bother\n->dessert is a joke . . . dont bother\n[{'aspect': 'dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: this was a outstanding upgrade from the early 2013 650m model .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was a outstanding upgrade from the early 2013 650m model .\n->", + "output": "{\"text\": \"this was a outstanding upgrade from the early 2013 650m model .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall i ' m happy with it for what i use it for .\n->overall i ' m happy with it for what i use it for .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n->the hot dogs are top notch , and they ' re slamwich is amazing !\n[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: as far as gaming performance , the m370x does quite well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas far as gaming performance , the m370x does quite well .\n->", + "output": "{\"text\": \"as far as gaming performance , the m370x does quite well .\", \"labels\": \"[{'aspect': 'm370x', 'opinion': 'well', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n->the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'storage', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'intel processor', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great pizza and fantastic service .\n->Great pizza and fantastic service .\n[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i predict based on tests performed by other users on youtube with battlefield 4 ( frostbyte 3 engine ) that this machine will perform well with the impending starwars battlefront ( also frostbyte 3 ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni predict based on tests performed by other users on youtube with battlefield 4 ( frostbyte 3 engine ) that this machine will perform well with the impending starwars battlefront ( also frostbyte 3 ) .\n->", + "output": "{\"text\": \"i predict based on tests performed by other users on youtube with battlefield 4 ( frostbyte 3 engine ) that this machine will perform well with the impending starwars battlefront ( also frostbyte 3 ) .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Not impressed with the food .\n->Not impressed with the food .\n[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\n->", + "output": "{\"text\": \"with the windows 10 / directx 12 improvements , frostbite 3 games will do very well on this machine at 1050p graphics / medium / low with the new starwars game .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: she then put the check down without asking if we were done , and came to check on the bill every two minutes , even though we were one of three occupied tables .\n->she then put the check down without asking if we were done , and came to check on the bill every two minutes , even though we were one of three occupied tables .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n->Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\n->", + "output": "{\"text\": \"nothing beats the quality of the mac os x as well , which is one of the main reasons for this purchase .\", \"labels\": \"[{'aspect': 'mac os x', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook 14 has a 1080p ips display with fantastic viewing angles and excellent brightness .\n->the chromebook 14 has a 1080p ips display with fantastic viewing angles and excellent brightness .\n[{'aspect': 'display', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'display', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: When I lived upstate for a while I would buy freeze the bagels and they would still be better than any else .\n->When I lived upstate for a while I would buy freeze the bagels and they would still be better than any else .\n[{'aspect': 'bagels', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but i fed up with the price it cost to upgrade the product as well as the software .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut i fed up with the price it cost to upgrade the product as well as the software .\n->", + "output": "{\"text\": \"but i fed up with the price it cost to upgrade the product as well as the software .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fed up', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the os doesn ' t leave menu bar at the top for copying in programs for studies .\n->the os doesn ' t leave menu bar at the top for copying in programs for studies .\n[{'aspect': 'os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: this laptop is actually horrible .\n->this laptop is actually horrible .\n[{'aspect': 'laptop', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i ventured back to the macbook pro world and purchased this model after much research .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ventured back to the macbook pro world and purchased this model after much research .\n->", + "output": "{\"text\": \"i ventured back to the macbook pro world and purchased this model after much research .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amma is nothing special .\n->amma is nothing special .\n[{'aspect': 'amma', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n->as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n[{'aspect': 'audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: one of the first things i noticed after firing up this device was how quiet and effortlessly it performs all tasks that i ask of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of the first things i noticed after firing up this device was how quiet and effortlessly it performs all tasks that i ask of it .\n->", + "output": "{\"text\": \"one of the first things i noticed after firing up this device was how quiet and effortlessly it performs all tasks that i ask of it .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'effortlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had nothing to lose since it was a paper weight otherwise .\n->i had nothing to lose since it was a paper weight otherwise .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n->We took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - love the trackpad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- love the trackpad .\n->", + "output": "{\"text\": \"- love the trackpad .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'trackpad', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n->but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'indian food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it is so romantic .\n->it is so romantic .\n[{'aspect': 'NULL', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: - performance and processing speed are bar none\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- performance and processing speed are bar none\n->", + "output": "{\"text\": \"- performance and processing speed are bar none\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was absolutely amazing ! !\n->The food was absolutely amazing ! !\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n->Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n[{'aspect': 'mussels', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'puff pastry goat cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salad with a delicious dressing', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hanger steak au poivre', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - retina display is nice but not mind blowing as other have suggested\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- retina display is nice but not mind blowing as other have suggested\n->", + "output": "{\"text\": \"- retina display is nice but not mind blowing as other have suggested\", \"labels\": \"[{'aspect': 'retina display', 'opinion': 'nice', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There is also very limited seating and there can be a substantial wait in getting food at peak times .\n->There is also very limited seating and there can be a substantial wait in getting food at peak times .\n[{'aspect': 'seating', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'substantial', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: anything that runs in a chrome browser on a desktop or laptop will run on a chromebook , and there are novel app - like extensions for almost anything else ( calculator apps , command lines , etc . )\n->anything that runs in a chrome browser on a desktop or laptop will run on a chromebook , and there are novel app - like extensions for almost anything else ( calculator apps , command lines , etc . )\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'app - like extensions', 'opinion': 'novel', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: all - in - all , i have been extremely satisfied so far with this purchase and am enjoying my not - so - cutting edge purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall - in - all , i have been extremely satisfied so far with this purchase and am enjoying my not - so - cutting edge purchase .\n->", + "output": "{\"text\": \"all - in - all , i have been extremely satisfied so far with this purchase and am enjoying my not - so - cutting edge purchase .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is the type of place to run into old friends and have a late , raucous dinner .\n->It is the type of place to run into old friends and have a late , raucous dinner .\n[{'aspect': 'dinner', 'opinion': 'raucous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the only real upgrade for the new one , before adding on options is faster memory .\n->the only real upgrade for the new one , before adding on options is faster memory .\n[{'aspect': 'memory', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: the screen is crisp and clear , easy to set up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is crisp and clear , easy to set up .\n->", + "output": "{\"text\": \"the screen is crisp and clear , easy to set up .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: update : device now has full support for the play store on the beta channel .\n->update : device now has full support for the play store on the beta channel .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: it printed easily to our wireless printer too !\n->it printed easily to our wireless printer too !\n[{'aspect': 'NULL', 'opinion': 'easily', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: great deal on a great computer !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat deal on a great computer !\n->", + "output": "{\"text\": \"great deal on a great computer !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A must for all the Dosa lovers .\n->A must for all the Dosa lovers .\n[{'aspect': 'Dosa', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n->the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n[{'aspect': 'laptop', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'spec', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: my first mac and i ' m in love .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy first mac and i ' m in love .\n->", + "output": "{\"text\": \"my first mac and i ' m in love .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in laptop mode the trackpad works very well for this .\n->in laptop mode the trackpad works very well for this .\n[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The prices were CHEAP compared to the quality of service and food .\n->The prices were CHEAP compared to the quality of service and food .\n[{'aspect': 'prices', 'opinion': 'CHEAP', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so far it ' s running smooth with no issues as if it was new .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far it ' s running smooth with no issues as if it was new .\n->", + "output": "{\"text\": \"so far it ' s running smooth with no issues as if it was new .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no problems with this computer .\n->no problems with this computer .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Nothing fancy but really good food with pretty reasonable price .\n->Nothing fancy but really good food with pretty reasonable price .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: computer looked excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomputer looked excellent .\n->", + "output": "{\"text\": \"computer looked excellent .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: probably would not go again . . .\n->probably would not go again . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the design and atmosphere is just as good .\n->the design and atmosphere is just as good .\n[{'aspect': 'design', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'good', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: it came in a regular brown box and the power cord was a bit scratched .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit came in a regular brown box and the power cord was a bit scratched .\n->", + "output": "{\"text\": \"it came in a regular brown box and the power cord was a bit scratched .\", \"labels\": \"[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Mermaid Inn is an overall good restaurant with really good seafood .\n->Mermaid Inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Mermaid Inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: power plug doesn ' t fit well - connection is erratic .\n->power plug doesn ' t fit well - connection is erratic .\n[{'aspect': 'power plug', 'opinion': \"' t fit well\", 'polarity': 'negative', 'category': 'POWER_SUPPLY#CONNECTIVITY'}, {'aspect': 'power plug', 'opinion': 'erratic', 'polarity': 'negative', 'category': 'POWER_SUPPLY#CONNECTIVITY'}]\ntext: but the mac looked and worked great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the mac looked and worked great .\n->", + "output": "{\"text\": \"but the mac looked and worked great .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'mac', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n->The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n[{'aspect': 'bruscetta', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mix of greens', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: this is the first macbook i have ever purchased , i wish i had purchased sooner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the first macbook i have ever purchased , i wish i had purchased sooner .\n->", + "output": "{\"text\": \"this is the first macbook i have ever purchased , i wish i had purchased sooner .\", \"labels\": \"[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'trendi', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the best !\n->the best !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: thought all apple products are over priced , i wouldn ' t have anything else .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthought all apple products are over priced , i wouldn ' t have anything else .\n->", + "output": "{\"text\": \"thought all apple products are over priced , i wouldn ' t have anything else .\", \"labels\": \"[{'aspect': 'apple products', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'COMPANY#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just straight up cheap , good food .\n->just straight up cheap , good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: the seafood is amazing , there ' s a good wine list , and the ever - changing menu always offers some great surprises .\n->the seafood is amazing , there ' s a good wine list , and the ever - changing menu always offers some great surprises .\n[{'aspect': 'seafood', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wine list', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'menu', 'opinion': 'ever - changing', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'menu', 'opinion': 'great surprises', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: laptop in perfect condition .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop in perfect condition .\n->", + "output": "{\"text\": \"laptop in perfect condition .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the actual laptop is very much darker and blue .\n->the actual laptop is very much darker and blue .\n[{'aspect': 'actual laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n->it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n[{'aspect': 'chrome os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'glossy', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: i like the size of the laptop , and it ' s processing speed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like the size of the laptop , and it ' s processing speed .\n->", + "output": "{\"text\": \"i like the size of the laptop , and it ' s processing speed .\", \"labels\": \"[{'aspect': 'size of the laptop', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n->The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining hall', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: charging is crazy fast .\n->charging is crazy fast .\n[{'aspect': 'NULL', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: laptop is as described .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop is as described .\n->", + "output": "{\"text\": \"laptop is as described .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I did n't complain , I liked the atmosphere so much .\n->I did n't complain , I liked the atmosphere so much .\n[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The buffet had a nice selection .\n->The buffet had a nice selection .\n[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it didn ' t come with the box but it came with the charger and so far , i ' ve only been using for a few days , but i have no issues with the item at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit didn ' t come with the box but it came with the charger and so far , i ' ve only been using for a few days , but i have no issues with the item at all .\n->", + "output": "{\"text\": \"it didn ' t come with the box but it came with the charger and so far , i ' ve only been using for a few days , but i have no issues with the item at all .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The rest of the dim sum , though pricey by Chinatown standards , is worth it .\n->The rest of the dim sum , though pricey by Chinatown standards , is worth it .\n[{'aspect': 'dim sum', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: all i got was a fancy 2lb metal slab that can only be used to swat flies .\n->all i got was a fancy 2lb metal slab that can only be used to swat flies .\n[{'aspect': 'metal slab', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: looks brand new and the battery life lasts a long time ( see photos )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlooks brand new and the battery life lasts a long time ( see photos )\n->", + "output": "{\"text\": \"looks brand new and the battery life lasts a long time ( see photos )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the entrees were served with miso soup and rice .\n->the entrees were served with miso soup and rice .\n[{'aspect': 'entrees', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: overall this chromebook worked well and was reliable .\n->overall this chromebook worked well and was reliable .\n[{'aspect': 'chromebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i always was a huge fan of apple and i always thought the price on the macbook pros were too steep but i finally took the plunge and i ' m satisfied .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni always was a huge fan of apple and i always thought the price on the macbook pros were too steep but i finally took the plunge and i ' m satisfied .\n->", + "output": "{\"text\": \"i always was a huge fan of apple and i always thought the price on the macbook pros were too steep but i finally took the plunge and i ' m satisfied .\", \"labels\": \"[{'aspect': 'macbook pros', 'opinion': 'satisfied', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was pretty tradional but it was hot and good with large portions .\n->The food was pretty tradional but it was hot and good with large portions .\n[{'aspect': 'food', 'opinion': 'tradional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: our visit their to say the least , was an unpleasant and costly experience !\n->our visit their to say the least , was an unpleasant and costly experience !\n[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'costly', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: the seller ' s customer service was great very fast response time and the computer came packaged really well , in original box and it great shape .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe seller ' s customer service was great very fast response time and the computer came packaged really well , in original box and it great shape .\n->", + "output": "{\"text\": \"the seller ' s customer service was great very fast response time and the computer came packaged really well , in original box and it great shape .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'customer service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not imagine better indian food in all of the city .\n->i can not imagine better indian food in all of the city .\n[{'aspect': 'indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: tapping it on either end is hit or miss .\n->tapping it on either end is hit or miss .\n[{'aspect': 'tapping', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re thinking about this or the 2016 version i ' d stick with this for now .\n->", + "output": "{\"text\": \"if you ' re thinking about this or the 2016 version i ' d stick with this for now .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is exactly what i needed .\n->this is exactly what i needed .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I have eaten there 3-4 times and the food was always good .\n->I have eaten there 3-4 times and the food was always good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the 13 ` ` is a good weight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 13 ` ` is a good weight .\n->", + "output": "{\"text\": \"the 13 ` ` is a good weight .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is reliable and the price is moderate .\n->The food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i will constantly be thinking about how it will probably fall apart in a few moths .\n->i will constantly be thinking about how it will probably fall apart in a few moths .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: screen resolution super ( retina ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen resolution super ( retina ) .\n->", + "output": "{\"text\": \"screen resolution super ( retina ) .\", \"labels\": \"[{'aspect': 'screen resolution', 'opinion': 'super', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n->my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n[{'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: It is the type of place to run into old friends and have a late , raucous dinner .\n->It is the type of place to run into old friends and have a late , raucous dinner .\n[{'aspect': 'dinner', 'opinion': 'raucous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the display is beautiful and the amount if software you get makes it worth the price !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display is beautiful and the amount if software you get makes it worth the price !\n->", + "output": "{\"text\": \"the display is beautiful and the amount if software you get makes it worth the price !\", \"labels\": \"[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'software', 'opinion': 'worth', 'polarity': 'positive', 'category': 'SOFTWARE#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i hope it is fixed this time .\n->i hope it is fixed this time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: battery life is good at about 10 hours .\n->battery life is good at about 10 hours .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: mac is life , but i take one star away for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmac is life , but i take one star away for the price .\n->", + "output": "{\"text\": \"mac is life , but i take one star away for the price .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google locked me out because after an update , my keyboard output was not as it should have been ( some keys were inverted ) .\n->google locked me out because after an update , my keyboard output was not as it should have been ( some keys were inverted ) .\n[{'aspect': 'keyboard output', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: Overall , not worth the money .\n->Overall , not worth the money .\n[{'aspect': 'money', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it boots up instantaneously .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit boots up instantaneously .\n->", + "output": "{\"text\": \"it boots up instantaneously .\", \"labels\": \"[{'aspect': 'boots up', 'opinion': 'instantaneously', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even though its good seafood , the prices are too high .\n->even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'too high', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: if you want to watch movies or listen to music , this might not be the machine for you .\n->if you want to watch movies or listen to music , this might not be the machine for you .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: my father is very satisfied with this laptop\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy father is very satisfied with this laptop\n->", + "output": "{\"text\": \"my father is very satisfied with this laptop\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n->i thanked my friend who recommended me this restaurant and will certainly recommend it to others .\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n->if you ' re thinking about this or the 2016 version i ' d stick with this for now .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it came exactly as advertised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit came exactly as advertised .\n->", + "output": "{\"text\": \"it came exactly as advertised .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it cost 8 dollars and shipping is not cheap .\n->it cost 8 dollars and shipping is not cheap .\n[{'aspect': 'shipping', 'opinion': 'not cheap', 'polarity': 'negative', 'category': 'SHIPPING#PRICE'}]\nExample:\ntext: So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n->So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n[{'aspect': 'thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is a small , light , powerful device that until 2016 seemed custom fit for content creators , photographers , and other such lines of work or hobbies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a small , light , powerful device that until 2016 seemed custom fit for content creators , photographers , and other such lines of work or hobbies .\n->", + "output": "{\"text\": \"it is a small , light , powerful device that until 2016 seemed custom fit for content creators , photographers , and other such lines of work or hobbies .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'small', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s easy find and delete pics and files you ' ve downloaded .\n->it ' s easy find and delete pics and files you ' ve downloaded .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: i love the airdrop and receiving imessages on my mbp ( although it can cut into productivity if you ' re not careful ) .\n->i love the airdrop and receiving imessages on my mbp ( although it can cut into productivity if you ' re not careful ) .\n[{'aspect': 'airdrop', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\ntext: it is expensive , but it is absolutely , positively worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is expensive , but it is absolutely , positively worth it .\n->", + "output": "{\"text\": \"it is expensive , but it is absolutely , positively worth it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only complaint is the pricing - - i believe it would be more reasonable to pay a dollar less on each item listed on the menu .\n->only complaint is the pricing - - i believe it would be more reasonable to pay a dollar less on each item listed on the menu .\n[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: Always a nice crowd , but never loud .\n->Always a nice crowd , but never loud .\n[{'aspect': 'crowd', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crowd', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the new pro model is very light and compact , and can easily be carried around with you every day .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe new pro model is very light and compact , and can easily be carried around with you every day .\n->", + "output": "{\"text\": \"the new pro model is very light and compact , and can easily be carried around with you every day .\", \"labels\": \"[{'aspect': 'pro model', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'easily be carried', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this worked ok for about a year and then just totally died .\n->this worked ok for about a year and then just totally died .\n[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'died', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the coolest thing is the touch screen on something this size .\n->the coolest thing is the touch screen on something this size .\n[{'aspect': 'touch screen', 'opinion': 'coolest', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: the display is amazing and the new force click trackpad is an awesome addition .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display is amazing and the new force click trackpad is an awesome addition .\n->", + "output": "{\"text\": \"the display is amazing and the new force click trackpad is an awesome addition .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'force click trackpad', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n->the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'staff', 'opinion': 'not seem knowledgeable', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: for the price , it ' s a solid laptop .\n->for the price , it ' s a solid laptop .\n[{'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: it ' s a little weird at first , knowing that the button isn ' t actually moving when you click down , but you definitely get used to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a little weird at first , knowing that the button isn ' t actually moving when you click down , but you definitely get used to it .\n->", + "output": "{\"text\": \"it ' s a little weird at first , knowing that the button isn ' t actually moving when you click down , but you definitely get used to it .\", \"labels\": \"[{'aspect': 'button', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n->Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n[{'aspect': 'people', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"does n't quite match up\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Thalia', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n->i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the mac pro is also very fast , and i have only experienced the rainbow wheel once or twice whenever i was on a website that wasn ' t responding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mac pro is also very fast , and i have only experienced the rainbow wheel once or twice whenever i was on a website that wasn ' t responding .\n->", + "output": "{\"text\": \"the mac pro is also very fast , and i have only experienced the rainbow wheel once or twice whenever i was on a website that wasn ' t responding .\", \"labels\": \"[{'aspect': 'mac pro', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * keyboard * - the keyboard is alright .\n->* keyboard * - the keyboard is alright .\n[{'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: Faan is sooo good .\n->Faan is sooo good .\n[{'aspect': 'Faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but so far this laptop has been up to its expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut so far this laptop has been up to its expectations .\n->", + "output": "{\"text\": \"but so far this laptop has been up to its expectations .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza was really good .\n->The pizza was really good .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n->The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great computer but to took a while to adjust to not having as much memory as my last laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat computer but to took a while to adjust to not having as much memory as my last laptop .\n->", + "output": "{\"text\": \"great computer but to took a while to adjust to not having as much memory as my last laptop .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place ' s decor and hidden bathrooms made for a good laugh .\n->the place ' s decor and hidden bathrooms made for a good laugh .\n[{'aspect': 'decor', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'hidden bathrooms', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n->it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n[{'aspect': 'anti - reflective coating', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: has a long battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhas a long battery life .\n->", + "output": "{\"text\": \"has a long battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but today i noticed it ' s cracking ( ref : pics ) .\n->but today i noticed it ' s cracking ( ref : pics ) .\n[{'aspect': 'NULL', 'opinion': 'cracking', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s complementary , not revolutionary , which is much more intuitive and useful .\n->it ' s complementary , not revolutionary , which is much more intuitive and useful .\n[{'aspect': 'NULL', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i like everything about this macpro .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like everything about this macpro .\n->", + "output": "{\"text\": \"i like everything about this macpro .\", \"labels\": \"[{'aspect': 'macpro', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked properly for less than a week , and the touch screen stopped functioning again .\n->it worked properly for less than a week , and the touch screen stopped functioning again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: this laptop will be for school , netflix and youtube mostly .\n->this laptop will be for school , netflix and youtube mostly .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: stable , long battery life , and great build .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstable , long battery life , and great build .\n->", + "output": "{\"text\": \"stable , long battery life , and great build .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'stable', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good screen .\n->good screen .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n[{'aspect': 'staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'stressed', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'unisex bathroom', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: as of now , highly recommended refurbished products\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas of now , highly recommended refurbished products\n->", + "output": "{\"text\": \"as of now , highly recommended refurbished products\", \"labels\": \"[{'aspect': 'products', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this biggest weak point and my only major complaint is that the speakers on it just suck .\n->this biggest weak point and my only major complaint is that the speakers on it just suck .\n[{'aspect': 'speakers', 'opinion': 'weak', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'speakers', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'speakers', 'opinion': 'suck', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: online support from asus says they only repair not replace .\n->online support from asus says they only repair not replace .\n[{'aspect': 'online support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: the new retina is amazing and the speed is awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe new retina is amazing and the speed is awesome .\n->", + "output": "{\"text\": \"the new retina is amazing and the speed is awesome .\", \"labels\": \"[{'aspect': 'retina', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'speed', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first chromebook , and i ' m absolutely loving it .\n->this is my first chromebook , and i ' m absolutely loving it .\n[{'aspect': 'this', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i sit with it in my lap all day long and it never gets hot .\n->i sit with it in my lap all day long and it never gets hot .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: the only detail that i have to reproach is that the connection cables are a little dirty , but in acceptable condition .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only detail that i have to reproach is that the connection cables are a little dirty , but in acceptable condition .\n->", + "output": "{\"text\": \"the only detail that i have to reproach is that the connection cables are a little dirty , but in acceptable condition .\", \"labels\": \"[{'aspect': 'connection cables', 'opinion': 'dirty', 'polarity': 'neutral', 'category': 'PORTS#QUALITY'}, {'aspect': 'connection cables', 'opinion': 'acceptable', 'polarity': 'neutral', 'category': 'PORTS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n->The wait staff is very freindly , they make it feel like you 're eating in a freindly little european town .\n[{'aspect': 'wait staff', 'opinion': 'freindly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is an amazing computer .\n->this is an amazing computer .\n[{'aspect': 'computer', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: love this mac book pro .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove this mac book pro .\n->", + "output": "{\"text\": \"love this mac book pro .\", \"labels\": \"[{'aspect': 'mac book pro', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is actually horrible .\n->this laptop is actually horrible .\n[{'aspect': 'laptop', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i have had this laptop for about a month now and every time i turn it on , i am still blown away !\n->i have had this laptop for about a month now and every time i turn it on , i am still blown away !\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: well macbook is no less than expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwell macbook is no less than expected .\n->", + "output": "{\"text\": \"well macbook is no less than expected .\", \"labels\": \"[{'aspect': 'macbook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n->The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n[{'aspect': 'food', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portioins', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: what a great place .\n->what a great place .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n->", + "output": "{\"text\": \"this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\", \"labels\": \"[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'retina display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'compatibility', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even if the food was n ' t this good , the garden is a great place to sit outside and relax .\n->even if the food was n ' t this good , the garden is a great place to sit outside and relax .\n[{'aspect': 'garden', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': \"n ' t this good\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: excellent quality and very fast delivery .\n->excellent quality and very fast delivery .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: crisp screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncrisp screen .\n->", + "output": "{\"text\": \"crisp screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n->The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .\n[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n->The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n[{'aspect': 'parathas', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kebabs', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i have had this computer for almost a year now and i love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had this computer for almost a year now and i love it .\n->", + "output": "{\"text\": \"i have had this computer for almost a year now and i love it .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was great and the service was even better .\n->The food was great and the service was even better .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what a great place .\n->what a great place .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i have used it for business and school and wouldn ' t want any other computer for the job .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have used it for business and school and wouldn ' t want any other computer for the job .\n->", + "output": "{\"text\": \"i have used it for business and school and wouldn ' t want any other computer for the job .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: aside from that , laptop seems fine .\n->aside from that , laptop seems fine .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n->at this point , the waitress comes over and asks us if everything was okay , i was literally so shocked that i was speechless and did n ' t say anything , and guess what , the waitress walked away .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i love this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this laptop .\n->", + "output": "{\"text\": \"i love this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n->During the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: each time we ' ve been , the front of house staff ( not the waiters - they ' re fantastic - but the people who greet and seat you ) has been so hideous to us that were it not for the exceptional fish dishes i would never return .\n->each time we ' ve been , the front of house staff ( not the waiters - they ' re fantastic - but the people who greet and seat you ) has been so hideous to us that were it not for the exceptional fish dishes i would never return .\n[{'aspect': 'waiters', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'front of house staff', 'opinion': 'hideous', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'fish dishes', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: mac really knows how to build a good dev laptop\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmac really knows how to build a good dev laptop\n->", + "output": "{\"text\": \"mac really knows how to build a good dev laptop\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this laptop .\n->i love this laptop .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s so easy to travel with .\n->it ' s so easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: im thrilled with my mac .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nim thrilled with my mac .\n->", + "output": "{\"text\": \"im thrilled with my mac .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'thrilled', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: , as it doesn ' t have large built in memory .\n->, as it doesn ' t have large built in memory .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: looks wise it ' s beautiful , i love the minimal design and layout .\n->looks wise it ' s beautiful , i love the minimal design and layout .\n[{'aspect': 'NULL', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'layout', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\n->", + "output": "{\"text\": \"i was a bit apprehensive at first will ordering such an expensive item online , but it was delivered in perfect new condition .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'item', 'opinion': 'perfect', 'polarity': 'negative', 'category': 'SHIPPING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cpu and gpu are good , ram is good and i like the keyboard .\n->cpu and gpu are good , ram is good and i like the keyboard .\n[{'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'gpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'GRAPHICS#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: plus the screen is matte , so bright lights are n ' t glaring .\n->plus the screen is matte , so bright lights are n ' t glaring .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: i think i will forever be a mac user from now on , it is an awesome product !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think i will forever be a mac user from now on , it is an awesome product !\n->", + "output": "{\"text\": \"i think i will forever be a mac user from now on , it is an awesome product !\", \"labels\": \"[{'aspect': 'product', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: feel totally comfortable with it , and will never go back to a pc .\n->feel totally comfortable with it , and will never go back to a pc .\n[{'aspect': 'NULL', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it travels well , great battery life , and is powerful enough for 100 % of the tasks i need to do ( web , streaming , word processing , reports , email ) .\n->it travels well , great battery life , and is powerful enough for 100 % of the tasks i need to do ( web , streaming , word processing , reports , email ) .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: wonderful machine fast , clean , solid i have to said that this guy i fell from my hands the first day i use , goes to the floor and nothing happens screen is perfect and nothing is damage !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwonderful machine fast , clean , solid i have to said that this guy i fell from my hands the first day i use , goes to the floor and nothing happens screen is perfect and nothing is damage !\n->", + "output": "{\"text\": \"wonderful machine fast , clean , solid i have to said that this guy i fell from my hands the first day i use , goes to the floor and nothing happens screen is perfect and nothing is damage !\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'machine', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food , great prices , great service .\n->great food , great prices , great service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: big learning curve , would hate to see someone older try and figure it out .\n->big learning curve , would hate to see someone older try and figure it out .\n[{'aspect': 'NULL', 'opinion': 'hate', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: this is my first macbook pro i ' m impress !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my first macbook pro i ' m impress !\n->", + "output": "{\"text\": \"this is my first macbook pro i ' m impress !\", \"labels\": \"[{'aspect': 'macbook pro', 'opinion': 'impress', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n->received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: those rolls were big , but not good and sashimi was n ' t fresh .\n->those rolls were big , but not good and sashimi was n ' t fresh .\n[{'aspect': 'rolls', 'opinion': 'big', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sashimi', 'opinion': \"was n ' t fresh\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i love this mac .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this mac .\n->", + "output": "{\"text\": \"i love this mac .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i plugged it back in , let it fully charge as directed and have had no problems since .\n->i plugged it back in , let it fully charge as directed and have had no problems since .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i am very pleased with this version of the mac and hope i never have to upgrade again because this is by far the best !\n->i am very pleased with this version of the mac and hope i never have to upgrade again because this is by far the best !\n[{'aspect': 'mac', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'mac', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreceived on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n->", + "output": "{\"text\": \"received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am happy i did the food was awsome .\n->i am happy i did the food was awsome .\n[{'aspect': 'food', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n->We took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the computer itself is as stated and i received at a price my husband and i were both happy with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer itself is as stated and i received at a price my husband and i were both happy with .\n->", + "output": "{\"text\": \"the computer itself is as stated and i received at a price my husband and i were both happy with .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen , i have it running at the brightest setting and looks fine .\n->screen , i have it running at the brightest setting and looks fine .\n[{'aspect': 'screen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n->if you 're looking for perfect traditional sushi , go here - if you 're looking for interesting combinations , try Sushi of gari 's ( east side ) .\n[{'aspect': 'sushi', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: fast shipping .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast shipping .\n->", + "output": "{\"text\": \"fast shipping .\", \"labels\": \"[{'aspect': 'shipping', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great little laptop .\n->great little laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is my only device with this issue in my home .\n->it is my only device with this issue in my home .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: what an excellent computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat an excellent computer .\n->", + "output": "{\"text\": \"what an excellent computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While we enjoyed the food , we were highly disappointed by the poor service ( waiter was not quite competent and SLOW service ) and lack of remorse .\n->While we enjoyed the food , we were highly disappointed by the poor service ( waiter was not quite competent and SLOW service ) and lack of remorse .\n[{'aspect': 'food', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'not quite competent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'SLOW', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: you know what i mean all the positives things happening there made mw write this review .\n->you know what i mean all the positives things happening there made mw write this review .\n[{'aspect': 'NULL', 'opinion': 'positives', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: as a refurbished item it was indistinguishable from a new item .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas a refurbished item it was indistinguishable from a new item .\n->", + "output": "{\"text\": \"as a refurbished item it was indistinguishable from a new item .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keys are a bit thin and have an odd feel to them .\n->keys are a bit thin and have an odd feel to them .\n[{'aspect': 'keys', 'opinion': 'thin', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: This was my frist time at Cafe St. Bart 's and I must say how delicous the food and the service was .\n->This was my frist time at Cafe St. Bart 's and I must say how delicous the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'delicous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: condition even better then i expect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncondition even better then i expect .\n->", + "output": "{\"text\": \"condition even better then i expect .\", \"labels\": \"[{'aspect': 'condition', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works great !\n->it works great !\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the dj is awesome , i have been there for my birthday and a bunch of other times with friends and i keep going back .\n->the dj is awesome , i have been there for my birthday and a bunch of other times with friends and i keep going back .\n[{'aspect': 'dj', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the mac works like a new one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mac works like a new one .\n->", + "output": "{\"text\": \"the mac works like a new one .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is apparent the hard drive has failed yet again .\n->it is apparent the hard drive has failed yet again .\n[{'aspect': 'hard drive', 'opinion': 'failed', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: LOVE the atmosphere - felt like I was in Paris .\n->LOVE the atmosphere - felt like I was in Paris .\n[{'aspect': 'atmosphere', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great laptop , excellent quality , definitely meets my expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat laptop , excellent quality , definitely meets my expectations .\n->", + "output": "{\"text\": \"great laptop , excellent quality , definitely meets my expectations .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n->it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: mem , hdd , screed , dvd drive are all easily accessible and removable .\n->mem , hdd , screed , dvd drive are all easily accessible and removable .\n[{'aspect': 'mem', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'mem', 'opinion': 'removable', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'hdd', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'hdd', 'opinion': 'removable', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'screed', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screed', 'opinion': 'removable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'removable', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}]\ntext: the macbook was delivered soon and it is exactly as described\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe macbook was delivered soon and it is exactly as described\n->", + "output": "{\"text\": \"the macbook was delivered soon and it is exactly as described\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n->i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: And forget what you read under me , the atmosphere is n't that bad either .\n->And forget what you read under me , the atmosphere is n't that bad either .\n[{'aspect': 'atmosphere', 'opinion': \"is n't that bad\", 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i love this computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this computer .\n->", + "output": "{\"text\": \"i love this computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love pizza 33 . . .\n->love pizza 33 . . .\n[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: and the blue mail email app handles my multiple emails from multiple domains .\n->and the blue mail email app handles my multiple emails from multiple domains .\n[{'aspect': 'blue mail email app', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: it has been great for everything i ' ve done .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has been great for everything i ' ve done .\n->", + "output": "{\"text\": \"it has been great for everything i ' ve done .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They came out over cooked and the cheese was almost non existant .\n->They came out over cooked and the cheese was almost non existant .\n[{'aspect': 'cheese', 'opinion': 'non existant', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: worth visiting the 1st ave spot because it is the original store .\n->worth visiting the 1st ave spot because it is the original store .\n[{'aspect': '1st ave spot', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n->", + "output": "{\"text\": \"i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n->we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'tired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n->when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n[{'aspect': 'NULL', 'opinion': 'okay', 'polarity': 'positive', 'category': 'FANS&COOLING#GENERAL'}]\ntext: great price for a brand new product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat price for a brand new product .\n->", + "output": "{\"text\": \"great price for a brand new product .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: backlit keyboard is great ; feels sturdy ; fast processing .\n->backlit keyboard is great ; feels sturdy ; fast processing .\n[{'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: anybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .\n->anybody who likes this place must be from a different planet , where greasy , dry and tasteless are complimentary .\n[{'aspect': 'NULL', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: now i keep seeing headlines for bad battery life and other woes for the 2016 edition and feel extra satisfied by my choice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow i keep seeing headlines for bad battery life and other woes for the 2016 edition and feel extra satisfied by my choice .\n->", + "output": "{\"text\": \"now i keep seeing headlines for bad battery life and other woes for the 2016 edition and feel extra satisfied by my choice .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'bad', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great and reasonably priced .\n->The food is great and reasonably priced .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Zero ambiance to boot .\n->Zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'Zero', 'polarity': 'negative', 'category': 'NULL'}]\ntext: slim , very fast , great screen , lightweight , solid state drives , great battery ( even though it burns the battery life when i play my flight simulator due to processing demands of the graphics ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nslim , very fast , great screen , lightweight , solid state drives , great battery ( even though it burns the battery life when i play my flight simulator due to processing demands of the graphics ! )\n->", + "output": "{\"text\": \"slim , very fast , great screen , lightweight , solid state drives , great battery ( even though it burns the battery life when i play my flight simulator due to processing demands of the graphics ! )\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other than that it ' s everything i imagined and more .\n->other than that it ' s everything i imagined and more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Very good wine choices .\n->Very good wine choices .\n[{'aspect': 'wine choices', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s a great computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a great computer .\n->", + "output": "{\"text\": \"it ' s a great computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ll give the chromebook pro an extra star for its best asset , the screen .\n->i ' ll give the chromebook pro an extra star for its best asset , the screen .\n[{'aspect': 'screen', 'opinion': 'best', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: consideration : if you are of average frame and strength then this build will be perfect and the keyboard flex and body is the perfect fit between lightweight and rigidity .\n->consideration : if you are of average frame and strength then this build will be perfect and the keyboard flex and body is the perfect fit between lightweight and rigidity .\n[{'aspect': 'build', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'flex', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'body', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'body', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'body', 'opinion': 'rigidity .', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the pro is by far the best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pro is by far the best .\n->", + "output": "{\"text\": \"the pro is by far the best .\", \"labels\": \"[{'aspect': 'pro', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at first we were a little taken aback , as this seemed to present a problem , although the restaurant looked fairly empty , but they hastily put the table together for us .\n->at first we were a little taken aback , as this seemed to present a problem , although the restaurant looked fairly empty , but they hastily put the table together for us .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Great spot , whether looking for a couple of drinks or quiet dinner .\n->Great spot , whether looking for a couple of drinks or quiet dinner .\n[{'aspect': 'spot', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the retina screen does an excellent job of not tiring my eyes after a long day of computer work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe retina screen does an excellent job of not tiring my eyes after a long day of computer work .\n->", + "output": "{\"text\": \"the retina screen does an excellent job of not tiring my eyes after a long day of computer work .\", \"labels\": \"[{'aspect': 'retina screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: running android apps themselves are a pretty ` ` meh ` ` experience .\n->running android apps themselves are a pretty ` ` meh ` ` experience .\n[{'aspect': 'android apps', 'opinion': 'meh', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: our experience did not ever get any better .\n->our experience did not ever get any better .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: i am very pleased with this version of the mac and hope i never have to upgrade again because this is by far the best !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very pleased with this version of the mac and hope i never have to upgrade again because this is by far the best !\n->", + "output": "{\"text\": \"i am very pleased with this version of the mac and hope i never have to upgrade again because this is by far the best !\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'mac', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also on this model the ssd is not replaceable .\n->also on this model the ssd is not replaceable .\n[{'aspect': 'ssd', 'opinion': 'not replaceable', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\nExample:\ntext: I got an excellent piece of cheesecake and we had several other nice pastries .\n->I got an excellent piece of cheesecake and we had several other nice pastries .\n[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you ' re coming from a pc you ' ll love the battery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re coming from a pc you ' ll love the battery .\n->", + "output": "{\"text\": \"if you ' re coming from a pc you ' ll love the battery .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you like spicy food get the chicken vindaloo .\n->If you like spicy food get the chicken vindaloo .\n[{'aspect': 'chicken vindaloo', 'opinion': 'get', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The view is spectacular , and the food is great .\n->The view is spectacular , and the food is great .\n[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 1 - itunes match ( $ 25 / yr i believe ) keeps all of your music synced across all of your ios devices with no limit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n1 - itunes match ( $ 25 / yr i believe ) keeps all of your music synced across all of your ios devices with no limit .\n->", + "output": "{\"text\": \"1 - itunes match ( $ 25 / yr i believe ) keeps all of your music synced across all of your ios devices with no limit .\", \"labels\": \"[{'aspect': 'itunes match', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: look and feel of asus unit seems high quality but keyboard failed in 45 days .\n->look and feel of asus unit seems high quality but keyboard failed in 45 days .\n[{'aspect': 'asus unit', 'opinion': 'high', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'failed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: the screen display is absolutely amazing and totally blows me away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen display is absolutely amazing and totally blows me away .\n->", + "output": "{\"text\": \"the screen display is absolutely amazing and totally blows me away .\", \"labels\": \"[{'aspect': 'screen display', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had previouslu bought an msi mobo which refused to boot unless windows 10 was the os , but i had worked around all of those problems .\n->i had previouslu bought an msi mobo which refused to boot unless windows 10 was the os , but i had worked around all of those problems .\n[{'aspect': 'msi mobo', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n->i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n[{'aspect': 'asus support', 'opinion': 'sloth', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: january 21 , 2016 update : i love this laptop even more than before .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njanuary 21 , 2016 update : i love this laptop even more than before .\n->", + "output": "{\"text\": \"january 21 , 2016 update : i love this laptop even more than before .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n->In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it integrates perfectly with my google account !\n->it integrates perfectly with my google account !\n[{'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: feel totally comfortable with it , and will never go back to a pc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfeel totally comfortable with it , and will never go back to a pc .\n->", + "output": "{\"text\": \"feel totally comfortable with it , and will never go back to a pc .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n->i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n[{'aspect': 'realtek audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: no caps lock on the keyboard .\n->no caps lock on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n->", + "output": "{\"text\": \"it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as of now , for $ 600 or less , this is a nice buy .\n->as of now , for $ 600 or less , this is a nice buy .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: port options are nice as well .\n->port options are nice as well .\n[{'aspect': 'port options', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\ntext: this laptop is incredibly lightweight and slim ( ultimately won me over after years of lugging around a whopping 5lb levono ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is incredibly lightweight and slim ( ultimately won me over after years of lugging around a whopping 5lb levono ) .\n->", + "output": "{\"text\": \"this laptop is incredibly lightweight and slim ( ultimately won me over after years of lugging around a whopping 5lb levono ) .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am not fond of touchpads anyway , so probably not the best one to judge them .\n->i am not fond of touchpads anyway , so probably not the best one to judge them .\n[{'aspect': 'touchpads', 'opinion': 'not fond of', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i ' ve enjoyed 99 % of the dishes we ' ve ordered with the only exceptions being the occasional too - authentic - for - me dish ( i ' m a daring eater but not that daring ) .\n->i ' ve enjoyed 99 % of the dishes we ' ve ordered with the only exceptions being the occasional too - authentic - for - me dish ( i ' m a daring eater but not that daring ) .\n[{'aspect': 'dishes', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dish', 'opinion': 'too - authentic - for - me', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i hate the new keyboard the newer version comes with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni hate the new keyboard the newer version comes with .\n->", + "output": "{\"text\": \"i hate the new keyboard the newer version comes with .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'hate', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My chow fun and chow see was really bland and oily .\n->My chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it is a very good laptop .\n->it is a very good laptop .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the 2015 version has the old keyboard with keys that you can actually type on without the fear of a typo every other word .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 2015 version has the old keyboard with keys that you can actually type on without the fear of a typo every other word .\n->", + "output": "{\"text\": \"the 2015 version has the old keyboard with keys that you can actually type on without the fear of a typo every other word .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i heard the lobster roll was excellent .\n->i heard the lobster roll was excellent .\n[{'aspect': 'lobster roll', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food was below average , the service was pathetic , there was no ambience at all .\n->The food was below average , the service was pathetic , there was no ambience at all .\n[{'aspect': 'food', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'no', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the battery life ( so far ) is fairly true to the description and has lasted me 8 hours of constant use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life ( so far ) is fairly true to the description and has lasted me 8 hours of constant use .\n->", + "output": "{\"text\": \"the battery life ( so far ) is fairly true to the description and has lasted me 8 hours of constant use .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The exotic food is beautifully presented and is a delight in delicious combinations .\n->The exotic food is beautifully presented and is a delight in delicious combinations .\n[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m no expert on screens but i personally think the panel looks very nice .\n->i ' m no expert on screens but i personally think the panel looks very nice .\n[{'aspect': 'panel', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: it ' s so much faster and the mac os is so much more secure .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s so much faster and the mac os is so much more secure .\n->", + "output": "{\"text\": \"it ' s so much faster and the mac os is so much more secure .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'faster', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac os', 'opinion': 'secure', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other than that , i like it , and this is my first chromebook .\n->other than that , i like it , and this is my first chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n->The bruscetta is a bit soggy , but the salads were fresh , included a nice mix of greens ( not iceberg ) all dishes are served piping hot from the kitchen .\n[{'aspect': 'bruscetta', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'hot', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mix of greens', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love my new computer , there were some knicks on the outside but it works well\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove my new computer , there were some knicks on the outside but it works well\n->", + "output": "{\"text\": \"love my new computer , there were some knicks on the outside but it works well\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is a small , light , powerful device that until 2016 seemed custom fit for content creators , photographers , and other such lines of work or hobbies .\n->it is a small , light , powerful device that until 2016 seemed custom fit for content creators , photographers , and other such lines of work or hobbies .\n[{'aspect': 'device', 'opinion': 'small', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Personal pans are the perfect size for those hungry nights .\n->Personal pans are the perfect size for those hungry nights .\n[{'aspect': 'Personal pans', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this was exactly what i needed and it performs as new which is exactly what i expected as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was exactly what i needed and it performs as new which is exactly what i expected as well .\n->", + "output": "{\"text\": \"this was exactly what i needed and it performs as new which is exactly what i expected as well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it began to shut down and restart all on it ' s own - continuously .\n->it began to shut down and restart all on it ' s own - continuously .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i really like my chromebook .\n->i really like my chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n->", + "output": "{\"text\": \"i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cheese plate is a varied delight and great bargain at $ 10 .\n->Cheese plate is a varied delight and great bargain at $ 10 .\n[{'aspect': 'Cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: one year later and the laptop is still in great condition !\n->one year later and the laptop is still in great condition !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: so i decided to use some of my christmas money to buy myself this one , and so far this is pretty awesome !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso i decided to use some of my christmas money to buy myself this one , and so far this is pretty awesome !\n->", + "output": "{\"text\": \"so i decided to use some of my christmas money to buy myself this one , and so far this is pretty awesome !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i put it into tablet mode , everything is great .\n->when i put it into tablet mode , everything is great .\n[{'aspect': 'tablet mode', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n->mine was a little burnt but still delicious with goat cheese and panchetta ( raddichio was kind of bitter though ) .\n[{'aspect': 'raddichio', 'opinion': 'bitter', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n->", + "output": "{\"text\": \"it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: feels nice and looks good but it truly is the worst chromebook on the market !\n->feels nice and looks good but it truly is the worst chromebook on the market !\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard and track pad are both quite good , although i always use a real mouse .\n->the keyboard and track pad are both quite good , although i always use a real mouse .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'track pad', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: i also like the display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni also like the display .\n->", + "output": "{\"text\": \"i also like the display .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'like', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Terrific menu full of unique rolls and special dishes .\n->Terrific menu full of unique rolls and special dishes .\n[{'aspect': 'menu', 'opinion': 'Terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Try the Pad Thai , it 's fabulous and their prices are so cheap !\n->Try the Pad Thai , it 's fabulous and their prices are so cheap !\n[{'aspect': 'Pad Thai', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the speakers on this model are really nice as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speakers on this model are really nice as well .\n->", + "output": "{\"text\": \"the speakers on this model are really nice as well .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We have been to this place many times , and always have great food , wine , and service .\n->We have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the pizza is delicious and the proprietor is one of the nicest in nyc .\n->the pizza is delicious and the proprietor is one of the nicest in nyc .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: it ' s also awesome how the charger for this computer is magnetic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s also awesome how the charger for this computer is magnetic .\n->", + "output": "{\"text\": \"it ' s also awesome how the charger for this computer is magnetic .\", \"labels\": \"[{'aspect': 'charger', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'charger', 'opinion': 'magnetic', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was very good , but not what I would consider out of this world .\n->Food was very good , but not what I would consider out of this world .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: its a little out of the way if you do n ' t live in the neighborhood , but definitely worth the trip from wherever you are .\n->its a little out of the way if you do n ' t live in the neighborhood , but definitely worth the trip from wherever you are .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LOCATION#GENERAL'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: overall , i ' m very happy i chose this model .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , i ' m very happy i chose this model .\n->", + "output": "{\"text\": \"overall , i ' m very happy i chose this model .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do n ' t think 16 gb is enough .\n->i do n ' t think 16 gb is enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: the os is easy to use , the app store lacks many apps that i use , so i have to go directly to the developers site to download them .\n->the os is easy to use , the app store lacks many apps that i use , so i have to go directly to the developers site to download them .\n[{'aspect': 'os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'app store', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: definitely glad i purchased my mac .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely glad i purchased my mac .\n->", + "output": "{\"text\": \"definitely glad i purchased my mac .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The counter service is bad .\n->The counter service is bad .\n[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Some of the workers ignore me and talk to the female customers , other times , they 've skipped my order .\n->Some of the workers ignore me and talk to the female customers , other times , they 've skipped my order .\n[{'aspect': 'workers', 'opinion': 'ignore', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'order', 'opinion': 'skipped', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the display clarity is outstanding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display clarity is outstanding .\n->", + "output": "{\"text\": \"the display clarity is outstanding .\", \"labels\": \"[{'aspect': 'display clarity', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is extensive and impressive .\n->The wine list is extensive and impressive .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Avoid this place !\n->Avoid this place !\n[{'aspect': 'place', 'opinion': 'Avoid', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i am fully satisfied with the purchase and communication with the seller was great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am fully satisfied with the purchase and communication with the seller was great .\n->", + "output": "{\"text\": \"i am fully satisfied with the purchase and communication with the seller was great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'seller', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n->i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: best pastrami i ever had and great portion without being ridiculous .\n->best pastrami i ever had and great portion without being ridiculous .\n[{'aspect': 'pastrami', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: i love the mac .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love the mac .\n->", + "output": "{\"text\": \"i love the mac .\", \"labels\": \"[{'aspect': 'mac', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything about this restaurant was special .\n->everything about this restaurant was special .\n[{'aspect': 'restaurant', 'opinion': 'special', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The food is okay and the prices here are mediocre .\n->The food is okay and the prices here are mediocre .\n[{'aspect': 'food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i love it , it is as shown on the picture\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love it , it is as shown on the picture\n->", + "output": "{\"text\": \"i love it , it is as shown on the picture\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: consistently good japanese tapas .\n->consistently good japanese tapas .\n[{'aspect': 'japanese tapas', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: inexpensive , unassuming , great time !\n->inexpensive , unassuming , great time !\n[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it is a great laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a great laptop !\n->", + "output": "{\"text\": \"it is a great laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our agreed favorite is the orrechiete with sausage and chicken ( usually the waiters are kind enough to split the dish in half so you get to sample both meats ) .\n->Our agreed favorite is the orrechiete with sausage and chicken ( usually the waiters are kind enough to split the dish in half so you get to sample both meats ) .\n[{'aspect': 'orrechiete with sausage and chicken', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s still good for basic internet usage .\n->it ' s still good for basic internet usage .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: beautiful display and runs fast !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeautiful display and runs fast !\n->", + "output": "{\"text\": \"beautiful display and runs fast !\", \"labels\": \"[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was an amazing laptop , until the graphics card started rotting after a month .\n->it was an amazing laptop , until the graphics card started rotting after a month .\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'graphics card', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\nExample:\ntext: Seriously , this place kicks ass .\n->Seriously , this place kicks ass .\n[{'aspect': 'place', 'opinion': 'kicks ass', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a great laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na great laptop !\n->", + "output": "{\"text\": \"a great laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has been just over 6 months since purchasing this and it already needs a 500 fix .\n->it has been just over 6 months since purchasing this and it already needs a 500 fix .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: have eaten at ginger house several times , and it ' s always good .\n->have eaten at ginger house several times , and it ' s always good .\n[{'aspect': 'ginger house', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: awsome machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nawsome machine .\n->", + "output": "{\"text\": \"awsome machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is thick and slightly soggy .\n->it is thick and slightly soggy .\n[{'aspect': 'NULL', 'opinion': 'thick', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n->this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n[{'aspect': 'hardware', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: as expected from apple !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas expected from apple !\n->", + "output": "{\"text\": \"as expected from apple !\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 6 inch laptop it appears fragile the keyboard itself feels like the keys will pop out .\n->6 inch laptop it appears fragile the keyboard itself feels like the keys will pop out .\n[{'aspect': '6 inch laptop', 'opinion': 'fragile', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: service was slow had to wait to order and get food although not crowded .\n->service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the best laptop i ever had including amazing retina display , light weighted , fast booting , high performance and prolong battery time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe best laptop i ever had including amazing retina display , light weighted , fast booting , high performance and prolong battery time .\n->", + "output": "{\"text\": \"the best laptop i ever had including amazing retina display , light weighted , fast booting , high performance and prolong battery time .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'retina display', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'laptop', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'high', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ' re not gon na find a deal like this too often .\n->you ' re not gon na find a deal like this too often .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The counter service is bad .\n->The counter service is bad .\n[{'aspect': 'counter service', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\ntext: great item by apple as usual .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat item by apple as usual .\n->", + "output": "{\"text\": \"great item by apple as usual .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .\n->the service leaves much to be desired , from feeling like you are rushed the place your order , to being ignored the rest of the night .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: screen resolution could be slightly better .\n->screen resolution could be slightly better .\n[{'aspect': 'screen resolution', 'opinion': 'better', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: works exactly like it ' s supposed to work !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks exactly like it ' s supposed to work !\n->", + "output": "{\"text\": \"works exactly like it ' s supposed to work !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it arrived as promised and was exactly as described .\n->it arrived as promised and was exactly as described .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\nExample:\ntext: touch pad is a + + .\n->touch pad is a + + .\n[{'aspect': 'touch pad', 'opinion': 'a +', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: a wonderful device with extremely clear display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na wonderful device with extremely clear display .\n->", + "output": "{\"text\": \"a wonderful device with extremely clear display .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Here 's to the fake fish tanks too ...\n->Here 's to the fake fish tanks too ...\n[{'aspect': 'fish tanks', 'opinion': 'fake', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n->Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n[{'aspect': 'people serving', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"does n't quite match up\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: one year later and the laptop is still in great condition !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none year later and the laptop is still in great condition !\n->", + "output": "{\"text\": \"one year later and the laptop is still in great condition !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: multiple system crashes .\n->multiple system crashes .\n[{'aspect': 'system', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: Highly recommended is the Spicy Fried Clam Rolls and Spider Rolls .\n->Highly recommended is the Spicy Fried Clam Rolls and Spider Rolls .\n[{'aspect': 'Spicy Fried Clam Rolls', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Spider Rolls', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the macbook pro is everything i hoped for and more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe macbook pro is everything i hoped for and more .\n->", + "output": "{\"text\": \"the macbook pro is everything i hoped for and more .\", \"labels\": \"[{'aspect': 'macbook pro', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s increasingly disappointing to see the much - hyped play access is still nonexistent .\n->it ' s increasingly disappointing to see the much - hyped play access is still nonexistent .\n[{'aspect': 'play access', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'SOFTWARE#QUALITY'}]\nExample:\ntext: this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n->this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n[{'aspect': 'chromebook', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'boot up', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: just made the move from pc to macbook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust made the move from pc to macbook !\n->", + "output": "{\"text\": \"just made the move from pc to macbook !\", \"labels\": \"[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n->The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n[{'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'well trained', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: love my macbook , beautiful and use daily !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove my macbook , beautiful and use daily !\n->", + "output": "{\"text\": \"love my macbook , beautiful and use daily !\", \"labels\": \"[{'aspect': 'macbook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'macbook', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Thai food is good .\n->The Thai food is good .\n[{'aspect': 'Thai food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: when i recieved the item i was amazed at the quality of it .\n->when i recieved the item i was amazed at the quality of it .\n[{'aspect': 'item', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: my 17 year old granddaughter loves it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy 17 year old granddaughter loves it .\n->", + "output": "{\"text\": \"my 17 year old granddaughter loves it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use it for streaming with the elgato device and it doesn ' t miss a beat .\n->i use it for streaming with the elgato device and it doesn ' t miss a beat .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' m more about personality than looks , but this little thing is a looker , too .\n->i ' m more about personality than looks , but this little thing is a looker , too .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the rig works better than advertised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe rig works better than advertised .\n->", + "output": "{\"text\": \"the rig works better than advertised .\", \"labels\": \"[{'aspect': 'rig', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after purchasing , this chromebook came with me on a 2 week trip to china .\n->after purchasing , this chromebook came with me on a 2 week trip to china .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i even tried reinstalling the drivers and doing a system restore , nothing would fix it .\n->i even tried reinstalling the drivers and doing a system restore , nothing would fix it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it was as they advertised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was as they advertised .\n->", + "output": "{\"text\": \"it was as they advertised .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n->i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n[{'aspect': 'the new upgrades', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: this is the best sushi in new york city - hands down .\n->this is the best sushi in new york city - hands down .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: great little laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat little laptop .\n->", + "output": "{\"text\": \"great little laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n->My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n[{'aspect': 'food', 'opinion': 'ranting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'raving', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery life : i haven ' t had the laptop for too long but easily get 8 hours or more on a charge .\n->battery life : i haven ' t had the laptop for too long but easily get 8 hours or more on a charge .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: latterly the best laptop i ' ve ever had , fast , powerful , stunning display , little problems with pairing bluetooth\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlatterly the best laptop i ' ve ever had , fast , powerful , stunning display , little problems with pairing bluetooth\n->", + "output": "{\"text\": \"latterly the best laptop i ' ve ever had , fast , powerful , stunning display , little problems with pairing bluetooth\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'pairing bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They have a huge selection of different cream cheeses and all of their salads are great .\n->They have a huge selection of different cream cheeses and all of their salads are great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: nice atmosphere , the service was very pleasant and the desert was good .\n->nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: five stars with no doubt , thanks apple for such product\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfive stars with no doubt , thanks apple for such product\n->", + "output": "{\"text\": \"five stars with no doubt , thanks apple for such product\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doubles as an android tablet and so far the experience with running android apps has been good .\n->it doubles as an android tablet and so far the experience with running android apps has been good .\n[{'aspect': 'android apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Service is not what one would expect from a joint in this price category .\n->Service is not what one would expect from a joint in this price category .\n[{'aspect': 'Service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i would highly recommend this product if you want to get into music production like myself .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would highly recommend this product if you want to get into music production like myself .\n->", + "output": "{\"text\": \"i would highly recommend this product if you want to get into music production like myself .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not sure when this feature was introduced , but it is very welcome .\n->i ' m not sure when this feature was introduced , but it is very welcome .\n[{'aspect': 'feature', 'opinion': 'welcome', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n->build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: elegant design combined good performance , this laptop is almost flawless .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nelegant design combined good performance , this laptop is almost flawless .\n->", + "output": "{\"text\": \"elegant design combined good performance , this laptop is almost flawless .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'this laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Taj Mahal offeres gret value and great food .\n->Taj Mahal offeres gret value and great food .\n[{'aspect': 'value', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve gotten more use out of this thing than i first envisioned .\n->i ' ve gotten more use out of this thing than i first envisioned .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsmall light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n->", + "output": "{\"text\": \"small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t like the fact that the battery go low very fast .\n->i don ' t like the fact that the battery go low very fast .\n[{'aspect': 'battery', 'opinion': 'fast', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: if you ' ve ever been along the river in weehawken you have an idea of the top of view the chart house has to offer .\n->if you ' ve ever been along the river in weehawken you have an idea of the top of view the chart house has to offer .\n[{'aspect': 'view', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: the battery lasts me days .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery lasts me days .\n->", + "output": "{\"text\": \"the battery lasts me days .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n->Anyways , if you 're in the neighborhood to eat good food , I would n't waste my time trying to find something , rather go across the street to Tamari .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The place 's decor and hidden bathrooms made for a good laugh .\n->The place 's decor and hidden bathrooms made for a good laugh .\n[{'aspect': 'decor', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hidden bathrooms', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is a lovely product that i recommend for general use as a student , less so for gaming and storing whatnot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a lovely product that i recommend for general use as a student , less so for gaming and storing whatnot .\n->", + "output": "{\"text\": \"this is a lovely product that i recommend for general use as a student , less so for gaming and storing whatnot .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n->the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n[{'aspect': 'NULL', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Mermaid Inn is an overall good restaurant with really good seafood .\n->Mermaid Inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove it .\n->", + "output": "{\"text\": \"love it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quality ingredients preparation all around , and a very fair price for NYC .\n->Quality ingredients preparation all around , and a very fair price for NYC .\n[{'aspect': 'ingredients', 'opinion': 'Quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'fair', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We were seated and ignored by waitstaff .\n->We were seated and ignored by waitstaff .\n[{'aspect': 'waitstaff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i love the mac laptops , they are durable , reliable , light with all day battery and have awesome designs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love the mac laptops , they are durable , reliable , light with all day battery and have awesome designs .\n->", + "output": "{\"text\": \"i love the mac laptops , they are durable , reliable , light with all day battery and have awesome designs .\", \"labels\": \"[{'aspect': 'mac laptops', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'mac laptops', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac laptops', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac laptops', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#DESIGN_FEATURES'}, {'aspect': 'designs', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I must say it 's a little pricey for the food because it was not as spectacular as the view .\n->I must say it 's a little pricey for the food because it was not as spectacular as the view .\n[{'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: All conveniently delivered right to the door .\n->All conveniently delivered right to the door .\n[{'aspect': 'delivered', 'opinion': 'conveniently', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love it .\n->", + "output": "{\"text\": \"i love it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , the power button placement is not very good .\n->also , the power button placement is not very good .\n[{'aspect': 'power button', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: also , i personally was n ' t a fan of the portobello and asparagus mole .\n->also , i personally was n ' t a fan of the portobello and asparagus mole .\n[{'aspect': 'portobello and asparagus mole', 'opinion': 'fan', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i am so excited about it and hope to see more of your products in the future !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am so excited about it and hope to see more of your products in the future !\n->", + "output": "{\"text\": \"i am so excited about it and hope to see more of your products in the future !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i figured out the day i got this laptop why the company was able to keep the price of this laptop even with the great hardware inside .\n->i figured out the day i got this laptop why the company was able to keep the price of this laptop even with the great hardware inside .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Good for casual dinner with jeans and sneakers .\n->Good for casual dinner with jeans and sneakers .\n[{'aspect': 'casual dinner', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the best product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe best product .\n->", + "output": "{\"text\": \"the best product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you 're looking for a great meal at a decent price , go to Del Frisco 's !\n->If you 're looking for a great meal at a decent price , go to Del Frisco 's !\n[{'aspect': 'meal', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: screen looks great .\n->screen looks great .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: excellent packaging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent packaging .\n->", + "output": "{\"text\": \"excellent packaging .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n->This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n[{'aspect': 'night scene', 'opinion': 'alive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spot', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The wine list is extensive and impressive .\n->The wine list is extensive and impressive .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: fast delivery , brand new as expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast delivery , brand new as expected .\n->", + "output": "{\"text\": \"fast delivery , brand new as expected .\", \"labels\": \"[{'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent packaging .\n->excellent packaging .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: the entrees were served with miso soup and rice .\n->the entrees were served with miso soup and rice .\n[{'aspect': 'entrees', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the keyboard and os takes some getting used to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard and os takes some getting used to .\n->", + "output": "{\"text\": \"the keyboard and os takes some getting used to .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Decor is charming .\n->Decor is charming .\n[{'aspect': 'Decor', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: crisp screen .\n->crisp screen .\n[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: good quality all around hardware + software , of course that is what apple is known for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood quality all around hardware + software , of course that is what apple is known for .\n->", + "output": "{\"text\": \"good quality all around hardware + software , of course that is what apple is known for .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'good', 'polarity': 'positive', 'category': 'HARDWARE#QUALITY'}, {'aspect': 'software', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#QUALITY'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n->not one of our meals was edible - bland and / or made with weird rosemary or orange flavoring .\n[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: very nicely packed in original box .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery nicely packed in original box .\n->", + "output": "{\"text\": \"very nicely packed in original box .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the seats are uncomfortable if you are sitting against the wall on wooden benches .\n->the seats are uncomfortable if you are sitting against the wall on wooden benches .\n[{'aspect': 'seats', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i ' m glad i got this .\n->i ' m glad i got this .\n[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: computer came in good condition and at a good price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomputer came in good condition and at a good price .\n->", + "output": "{\"text\": \"computer came in good condition and at a good price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n->While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Have frequented 'ino for several years and the food remains excellent .\n->Have frequented 'ino for several years and the food remains excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great value for money , the notebook is as good as new .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat value for money , the notebook is as good as new .\n->", + "output": "{\"text\": \"great value for money , the notebook is as good as new .\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'notebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer is nice , blah blah it has nice features but it stops working after a few months .\n->the computer is nice , blah blah it has nice features but it stops working after a few months .\n[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - track pad is accurate\n->- track pad is accurate\n[{'aspect': 'track pad', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: works great !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks great !\n->", + "output": "{\"text\": \"works great !\", \"labels\": \"[{'aspect': 'works', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now audio in and out are combined in just one port .\n->now audio in and out are combined in just one port .\n[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#DESIGN_FEATURES'}]\nExample:\ntext: it is very very disappointing .\n->it is very very disappointing .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i have had this laptop for about a month now and every time i turn it on , i am still blown away !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had this laptop for about a month now and every time i turn it on , i am still blown away !\n->", + "output": "{\"text\": \"i have had this laptop for about a month now and every time i turn it on , i am still blown away !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a painfully slow computer .\n->this is a painfully slow computer .\n[{'aspect': 'computer', 'opinion': 'painfully', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: by far the best salad i have had in a fast food restaurant .\n->by far the best salad i have had in a fast food restaurant .\n[{'aspect': 'salad', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: when i first got it in the mail and i opened it up and turned it on for the first time , i was a little speechless because the retina display looks so good !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i first got it in the mail and i opened it up and turned it on for the first time , i was a little speechless because the retina display looks so good !\n->", + "output": "{\"text\": \"when i first got it in the mail and i opened it up and turned it on for the first time , i was a little speechless because the retina display looks so good !\", \"labels\": \"[{'aspect': 'retina display', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is a little scatty at times but all is forgiven when the food arrives .\n->The service is a little scatty at times but all is forgiven when the food arrives .\n[{'aspect': 'service', 'opinion': 'scatty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'forgiven', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but the service was a bit slow .\n->but the service was a bit slow .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: very clean computer everything looks brand new !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery clean computer everything looks brand new !\n->", + "output": "{\"text\": \"very clean computer everything looks brand new !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n->i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n[{'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: this computer seemed very exciting but after having troubles with 3 of them i give up .\n->this computer seemed very exciting but after having troubles with 3 of them i give up .\n[{'aspect': 'computer', 'opinion': 'exciting', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: what ' s not to like , it ' s an amazing machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat ' s not to like , it ' s an amazing machine .\n->", + "output": "{\"text\": \"what ' s not to like , it ' s an amazing machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this because it had a good price .\n->i bought this because it had a good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: What is even better , is that the prices are very affordable as well , and the food is really good .\n->What is even better , is that the prices are very affordable as well , and the food is really good .\n[{'aspect': 'prices', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: also the battery life is great and lasts me an entire day ( i ' m usually on it all day too )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso the battery life is great and lasts me an entire day ( i ' m usually on it all day too )\n->", + "output": "{\"text\": \"also the battery life is great and lasts me an entire day ( i ' m usually on it all day too )\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , i feel this is the best laptop i ' ve ever purchased or used .\n->overall , i feel this is the best laptop i ' ve ever purchased or used .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: really happy with this laptop !\n->really happy with this laptop !\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great purchase , quick shipping .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat purchase , quick shipping .\n->", + "output": "{\"text\": \"great purchase , quick shipping .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'shipping', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not a great place for family or general dining .\n->Not a great place for family or general dining .\n[{'aspect': 'place', 'opinion': 'Not a great', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: customer service told me it ' s faulty\n->customer service told me it ' s faulty\n[{'aspect': 'customer service', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: excellent quality and very fast delivery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent quality and very fast delivery .\n->", + "output": "{\"text\": \"excellent quality and very fast delivery .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Always popular , always full , always a wait .\n->Always popular , always full , always a wait .\n[{'aspect': 'wait', 'opinion': 'always', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i am not a vegetarian but , almost all the dishes were great .\n->i am not a vegetarian but , almost all the dishes were great .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this laptop is absolutely everything i imagined .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is absolutely everything i imagined .\n->", + "output": "{\"text\": \"this laptop is absolutely everything i imagined .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had the thai style fried sea bass . . . which was very good .\n->i had the thai style fried sea bass . . . which was very good .\n[{'aspect': 'thai style fried sea bass', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: she loves it .\n->she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the computer is very beautiful and very light .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer is very beautiful and very light .\n->", + "output": "{\"text\": \"the computer is very beautiful and very light .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s completely quiet , no heat whatsoever , and very fast !\n->it ' s completely quiet , no heat whatsoever , and very fast !\n[{'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n->Thalia is a beautiful restaurant with beautiful people serving you , but the food does n't quite match up .\n[{'aspect': 'people serving', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"does n't quite match up\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: the retina display is stunning .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe retina display is stunning .\n->", + "output": "{\"text\": \"the retina display is stunning .\", \"labels\": \"[{'aspect': 'retina display', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place is a lot of fun .\n->the place is a lot of fun .\n[{'aspect': 'place', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n->the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n[{'aspect': 'hot dogs', 'opinion': 'juicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dogs', 'opinion': 'tender', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this computer is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer is amazing .\n->", + "output": "{\"text\": \"this computer is amazing .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best Reuben sandwich ever !\n->Best Reuben sandwich ever !\n[{'aspect': 'Reuben sandwich', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is very nice for me .\n->the keyboard is very nice for me .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the battery life is outstanding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is outstanding .\n->", + "output": "{\"text\": \"the battery life is outstanding .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent product and experience with the purchase .\n->excellent product and experience with the purchase .\n[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: in total , it took 4 updates to access the google play store - - completely unacceptable !\n->in total , it took 4 updates to access the google play store - - completely unacceptable !\n[{'aspect': 'google play store', 'opinion': 'unacceptable', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: the internal flash memory is like greased lightning .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe internal flash memory is like greased lightning .\n->", + "output": "{\"text\": \"the internal flash memory is like greased lightning .\", \"labels\": \"[{'aspect': 'flash memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Why do people rave about the ambience .\n->Why do people rave about the ambience .\n[{'aspect': 'ambience', 'opinion': 'rave', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: my main complain involves terrible battery life .\n->my main complain involves terrible battery life .\n[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\n->", + "output": "{\"text\": \"i was super worried that touching the keyboard when it was in tablet mode would cause it to switch back to laptop mode , but it looks like once you move the screen back past like 180 degrees , it goes into full touchscreen .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'worried', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am very happy with this laptop .\n->i am very happy with this laptop .\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this time around , asus released an absolutely refined masterpiece .\n->this time around , asus released an absolutely refined masterpiece .\n[{'aspect': 'asus', 'opinion': 'masterpiece', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: if you purchase this product , go into it with realistic expectations and patience getting familiar with the setup and you ' ll probably love it , too !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you purchase this product , go into it with realistic expectations and patience getting familiar with the setup and you ' ll probably love it , too !\n->", + "output": "{\"text\": \"if you purchase this product , go into it with realistic expectations and patience getting familiar with the setup and you ' ll probably love it , too !\", \"labels\": \"[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n->The owner is very friendly and a great guy , go try his pizza , you 'll like it !\n[{'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: in summer - eat outside on a terrace ( another great feature of suan ) ! ! !\n->in summer - eat outside on a terrace ( another great feature of suan ) ! ! !\n[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i bought this chromebook on the prime day deal , but even without $ 60 off it would still have been worth the money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this chromebook on the prime day deal , but even without $ 60 off it would still have been worth the money .\n->", + "output": "{\"text\": \"i bought this chromebook on the prime day deal , but even without $ 60 off it would still have been worth the money .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the key features that drew me to this chromebook was design , a quality keyboard that had backlit keys , and a good processor .\n->the key features that drew me to this chromebook was design , a quality keyboard that had backlit keys , and a good processor .\n[{'aspect': 'keyboard', 'opinion': 'quality', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'backlit keys', 'opinion': 'quality', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'processor', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\nExample:\ntext: Wine list is extensive without being over-priced .\n->Wine list is extensive without being over-priced .\n[{'aspect': 'Wine list', 'opinion': 'extensive without being over-priced', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it looks way better in person than in the pictures .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit looks way better in person than in the pictures .\n->", + "output": "{\"text\": \"it looks way better in person than in the pictures .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this item is two months old and the power button repeatedly does not work .\n->this item is two months old and the power button repeatedly does not work .\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'Out_Of_Scope#OPERATION_PERFORMANCE'}]\nExample:\ntext: the extra ram , however , is great for everyone , as it makes this device more capable of running many tabs , or handling higher demand tasks like streaming content , without tabs crashing out or caching / reloading .\n->the extra ram , however , is great for everyone , as it makes this device more capable of running many tabs , or handling higher demand tasks like streaming content , without tabs crashing out or caching / reloading .\n[{'aspect': 'ram', 'opinion': 'great', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: since you have access to android apps on the google play store , you don ' t have to rely solely on the chrome browser to access apps .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsince you have access to android apps on the google play store , you don ' t have to rely solely on the chrome browser to access apps .\n->", + "output": "{\"text\": \"since you have access to android apps on the google play store , you don ' t have to rely solely on the chrome browser to access apps .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the backlit keyboard was no longer lighting - up and the computer would no longer turn - on or boot .\n->but the backlit keyboard was no longer lighting - up and the computer would no longer turn - on or boot .\n[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: bad touchpad , jerky movement , imprecise , no controls to improve .\n->bad touchpad , jerky movement , imprecise , no controls to improve .\n[{'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'jerky', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'imprecise', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i haven ' t had issues with the track pad as others have .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni haven ' t had issues with the track pad as others have .\n->", + "output": "{\"text\": \"i haven ' t had issues with the track pad as others have .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the vivobook f510ua is a great laptop with fantastic specs .\n->the vivobook f510ua is a great laptop with fantastic specs .\n[{'aspect': 'vivobook f510ua', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'specs', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i ' ve never had bad service and the fish is fresh and delicious .\n->i ' ve never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i also bought a wireless mouse , which paired perfectly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni also bought a wireless mouse , which paired perfectly .\n->", + "output": "{\"text\": \"i also bought a wireless mouse , which paired perfectly .\", \"labels\": \"[{'aspect': 'wireless mouse', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was looking for banana tempura for dessert and they dont have .\n->i was looking for banana tempura for dessert and they dont have .\n[{'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: this is a good product based on my experience - i have used this for almost a whole month .\n->this is a good product based on my experience - i have used this for almost a whole month .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: do your research , learn how to optimize your experience , and you ' ll love it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo your research , learn how to optimize your experience , and you ' ll love it !\n->", + "output": "{\"text\": \"do your research , learn how to optimize your experience , and you ' ll love it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , the bluetooth is a nightmare .\n->however , the bluetooth is a nightmare .\n[{'aspect': 'bluetooth', 'opinion': 'nightmare', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: priced at upper intermediate range .\n->priced at upper intermediate range .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: well , i have to say , i ' m fairly impressed with my new chrome flipbook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwell , i have to say , i ' m fairly impressed with my new chrome flipbook !\n->", + "output": "{\"text\": \"well , i have to say , i ' m fairly impressed with my new chrome flipbook !\", \"labels\": \"[{'aspect': 'chrome flipbook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is ok , some of the people did n't get what they asked for .\n->The service is ok , some of the people did n't get what they asked for .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: also , chromeos does not allow color / temperature calibration of the display device .\n->also , chromeos does not allow color / temperature calibration of the display device .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\ntext: and the blue mail email app handles my multiple emails from multiple domains .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand the blue mail email app handles my multiple emails from multiple domains .\n->", + "output": "{\"text\": \"and the blue mail email app handles my multiple emails from multiple domains .\", \"labels\": \"[{'aspect': 'blue mail email app', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - memory is not easily upgradable anymore .\n->- memory is not easily upgradable anymore .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n->Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n[{'aspect': 'tanks', 'opinion': 'sad-looking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tanks', 'opinion': 'clear', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'healthy-looking', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i ' ve used it for 3 days and still have n ' t plugged it in !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve used it for 3 days and still have n ' t plugged it in !\n->", + "output": "{\"text\": \"i ' ve used it for 3 days and still have n ' t plugged it in !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the manager finally said he would com # # p the two glasses of wine ( which cost less than the food ) , and made it seem like a big concession .\n->the manager finally said he would com # # p the two glasses of wine ( which cost less than the food ) , and made it seem like a big concession .\n[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n->to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'teodora', 'opinion': 'deficiencies', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\n->", + "output": "{\"text\": \"it ' s fast , secure , easy to use ( even as a tech geek , it ' s nice knowing something ' s just going to work ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: none was made so i hung up .\n->none was made so i hung up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: and it was a very good price .\n->and it was a very good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n->", + "output": "{\"text\": \"i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'android apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tasty dog !\n->tasty dog !\n[{'aspect': 'dog', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s great other than the weak speakers and the touchpad .\n->it ' s great other than the weak speakers and the touchpad .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: the battery life is great ( i get about 9 hours ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is great ( i get about 9 hours ) .\n->", + "output": "{\"text\": \"the battery life is great ( i get about 9 hours ) .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bad battery , speaker and touchpad\n->bad battery , speaker and touchpad\n[{'aspect': 'battery', 'opinion': 'bad', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'speaker', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: $ 20 for all you can eat sushi can not be beaten .\n->$ 20 for all you can eat sushi can not be beaten .\n[{'aspect': 'sushi', 'opinion': 'can not be beaten', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\n->", + "output": "{\"text\": \"my only complaint is the click on the touchpad feels a little cheap , but i just use ` ` tap to click ` ` so it ' s not an issue .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n->myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n[{'aspect': 'myagi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: food was amazing - i love indian food and eat it quite regularly , but i can say this is one of the best i ' ve had .\n->food was amazing - i love indian food and eat it quite regularly , but i can say this is one of the best i ' ve had .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: overall i love this machine , and all my computers will probably be chromebooks in the future .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall i love this machine , and all my computers will probably be chromebooks in the future .\n->", + "output": "{\"text\": \"overall i love this machine , and all my computers will probably be chromebooks in the future .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff offers impeccable service .\n->The staff offers impeccable service .\n[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: regardless to the fact that the outside box was damaged , the inside box was fine .\n->regardless to the fact that the outside box was damaged , the inside box was fine .\n[{'aspect': 'outside box', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'inside box', 'opinion': 'fine', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: my desktop runs all my video editing software for my bigger and most demanding projects and my laptop is great for editing video and running pc games in places other than my home ( like school ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy desktop runs all my video editing software for my bigger and most demanding projects and my laptop is great for editing video and running pc games in places other than my home ( like school ! )\n->", + "output": "{\"text\": \"my desktop runs all my video editing software for my bigger and most demanding projects and my laptop is great for editing video and running pc games in places other than my home ( like school ! )\", \"labels\": \"[{'aspect': 'desktop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Pad Thai is excellent here , as well .\n->The Pad Thai is excellent here , as well .\n[{'aspect': 'Pad Thai', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: in an area sadly lacking in decent thai food , this is one of the best spots .\n->in an area sadly lacking in decent thai food , this is one of the best spots .\n[{'aspect': 'thai food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n->", + "output": "{\"text\": \"i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer hold its ground , but it has specs to be a killer .\n->the computer hold its ground , but it has specs to be a killer .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this laptop ' s construction is cheap and flimsy , the battery is not removable and the back case is nearly impossible to take off without damaging it .\n->this laptop ' s construction is cheap and flimsy , the battery is not removable and the back case is nearly impossible to take off without damaging it .\n[{'aspect': \"laptop ' s construction\", 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': \"laptop ' s construction\", 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'not removable', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}, {'aspect': 'back case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: when i recieved the item i was amazed at the quality of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i recieved the item i was amazed at the quality of it .\n->", + "output": "{\"text\": \"when i recieved the item i was amazed at the quality of it .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice keyboard .\n->nice keyboard .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: bottles of wine are cheap and good .\n->bottles of wine are cheap and good .\n[{'aspect': 'bottles of wine', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}, {'aspect': 'bottles of wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: but just at first glance , this thing is top quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut just at first glance , this thing is top quality .\n->", + "output": "{\"text\": \"but just at first glance , this thing is top quality .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the spicy shrimp appetizer ( again , not the greatest value in the world but worth the price ) and the lamb vindaloo is great .\n->try the spicy shrimp appetizer ( again , not the greatest value in the world but worth the price ) and the lamb vindaloo is great .\n[{'aspect': 'shrimp appetizer', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shrimp appetizer', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'shrimp appetizer', 'opinion': 'worth the price', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb vindaloo', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the owner truly caters to all your needs .\n->the owner truly caters to all your needs .\n[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: nothing on it feels cheap at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnothing on it feels cheap at all .\n->", + "output": "{\"text\": \"nothing on it feels cheap at all .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Acceptable prices .\n->Acceptable prices .\n[{'aspect': 'prices', 'opinion': 'Acceptable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we arrived and were seated immediately , which made us both happy .\n->we arrived and were seated immediately , which made us both happy .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: all of it feels very strudy ( within reason ) and has a nice , modern look and feel to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall of it feels very strudy ( within reason ) and has a nice , modern look and feel to it .\n->", + "output": "{\"text\": \"all of it feels very strudy ( within reason ) and has a nice , modern look and feel to it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'strudy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'modern', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall : the specs for this computer looked pretty good , but after using it for a few weeks , the negatives outweigh the positives , and i ' m going to have to return it .\n->overall : the specs for this computer looked pretty good , but after using it for a few weeks , the negatives outweigh the positives , and i ' m going to have to return it .\n[{'aspect': 'specs for this computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Horrible food and horrible service .\n->Horrible food and horrible service .\n[{'aspect': 'food', 'opinion': 'Horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i feel like this will look good in any decade .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni feel like this will look good in any decade .\n->", + "output": "{\"text\": \"i feel like this will look good in any decade .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: constantly got the blue screen , already tryed everything to fix it .\n->constantly got the blue screen , already tryed everything to fix it .\n[{'aspect': 'blue screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: $ 170 down the toilet . . .\n->$ 170 down the toilet . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: i boot it up and notice quickly the display seems to be 720p ( or something close ) but for what i need this thing to do .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni boot it up and notice quickly the display seems to be 720p ( or something close ) but for what i need this thing to do .\n->", + "output": "{\"text\": \"i boot it up and notice quickly the display seems to be 720p ( or something close ) but for what i need this thing to do .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s pretty fast even with heavy use and multiple applications running at once .\n->it ' s pretty fast even with heavy use and multiple applications running at once .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is fresh , delicious , and reasonably priced .\n->The food is fresh , delicious , and reasonably priced .\n[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the touchscreen and the hotkeys along the top take some getting used to but i was quickly in the google play store , downloading all the apps i have on my phone and linking them to my accounts .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchscreen and the hotkeys along the top take some getting used to but i was quickly in the google play store , downloading all the apps i have on my phone and linking them to my accounts .\n->", + "output": "{\"text\": \"the touchscreen and the hotkeys along the top take some getting used to but i was quickly in the google play store , downloading all the apps i have on my phone and linking them to my accounts .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'hotkeys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a weakness is the chicken in the salads .\n->a weakness is the chicken in the salads .\n[{'aspect': 'chicken in the salads', 'opinion': 'weakness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The portions are large and the servers always surprise us with a different starter .\n->The portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ` ` this thing is neat . ` `\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n` ` this thing is neat . ` `\n->", + "output": "{\"text\": \"` ` this thing is neat . ` `\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'neat', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is not consistent .\n->it is not consistent .\n[{'aspect': 'NULL', 'opinion': 'not consistent', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: our visit their to say the least , was an unpleasant and costly experience !\n->our visit their to say the least , was an unpleasant and costly experience !\n[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'costly', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: i was supposed to use this to write !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was supposed to use this to write !\n->", + "output": "{\"text\": \"i was supposed to use this to write !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n->If you are the type of person who likes being scared and entertained , this is a great place to go and eat .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the track pad has now stopped working .\n->the track pad has now stopped working .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i use some other apps but nothing is quite the same for my workflow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use some other apps but nothing is quite the same for my workflow .\n->", + "output": "{\"text\": \"i use some other apps but nothing is quite the same for my workflow .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worked fine until 3 months after i bought it .\n->worked fine until 3 months after i bought it .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: planet thai is great !\n->planet thai is great !\n[{'aspect': 'planet thai', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i thought the gizmo was neat but i was disappointed that i couldn ' t really use it the way i wanted to it that regards .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni thought the gizmo was neat but i was disappointed that i couldn ' t really use it the way i wanted to it that regards .\n->", + "output": "{\"text\": \"i thought the gizmo was neat but i was disappointed that i couldn ' t really use it the way i wanted to it that regards .\", \"labels\": \"[{'aspect': 'gizmo', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was OK - fish was cooked well .\n->Food was OK - fish was cooked well .\n[{'aspect': 'Food', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n->immediately after we paid , the waiter took the money and said , okay , you guys are outta here .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->", + "output": "{\"text\": \"i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: update - this thing frustrated me so much over the past month that i just threw it in the trash ( where it belongs ) .\n->update - this thing frustrated me so much over the past month that i just threw it in the trash ( where it belongs ) .\n[{'aspect': 'NULL', 'opinion': 'frustrated', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i am terribly disappointed with obviously a poor qc by samsung .\n->i am terribly disappointed with obviously a poor qc by samsung .\n[{'aspect': 'qc', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}, {'aspect': 'qc', 'opinion': 'poor', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\ntext: i could even watch videos and game from the laptop itself ( to an extent ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni could even watch videos and game from the laptop itself ( to an extent ) .\n->", + "output": "{\"text\": \"i could even watch videos and game from the laptop itself ( to an extent ) .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I love the fact that the pizza tastes so good and is so cheap .\n->I love the fact that the pizza tastes so good and is so cheap .\n[{'aspect': 'pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the charge cord is very short , about 1 / 2 the size of a regular charging cord\n->the charge cord is very short , about 1 / 2 the size of a regular charging cord\n[{'aspect': 'charge cord', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\ntext: that is just incredible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat is just incredible .\n->", + "output": "{\"text\": \"that is just incredible .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was friendly and the atmosphere was casual .\n->The service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: samsung makes garbage and treats their customers with no respect .\n->samsung makes garbage and treats their customers with no respect .\n[{'aspect': 'samsung', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: for $ 250 i can control my $ 3000 pc anywhere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor $ 250 i can control my $ 3000 pc anywhere .\n->", + "output": "{\"text\": \"for $ 250 i can control my $ 3000 pc anywhere .\", \"labels\": \"[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n->add to that great service and great food at a reasonable price and you have yourself the beginning of a great evening .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: skip this restaurant , it ' s a big disappointment .\n->skip this restaurant , it ' s a big disappointment .\n[{'aspect': 'restaurant', 'opinion': 'skip', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: this thing is the ultimate mobile workhorse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis thing is the ultimate mobile workhorse .\n->", + "output": "{\"text\": \"this thing is the ultimate mobile workhorse .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice view of river and nyc .\n->nice view of river and nyc .\n[{'aspect': 'view of river and nyc', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\nExample:\ntext: They 're also friendlier here , especially the owner , Kenny .\n->They 're also friendlier here , especially the owner , Kenny .\n[{'aspect': 'owner', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is the ultimate tablet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the ultimate tablet .\n->", + "output": "{\"text\": \"this is the ultimate tablet .\", \"labels\": \"[{'aspect': 'tablet', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we did tip , i guess the model / waitress just wanted more and complained to the manager .\n->we did tip , i guess the model / waitress just wanted more and complained to the manager .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i added an sd card which has expanded on the 16gb of storage .\n->i added an sd card which has expanded on the 16gb of storage .\n[{'aspect': 'sd card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\ntext: this thing is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis thing is amazing .\n->", + "output": "{\"text\": \"this thing is amazing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n->They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n[{'aspect': 'reservation', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: It takes forever to get a drink and they almost always forget to bring something ( although they dont forget to charge you for it .\n->It takes forever to get a drink and they almost always forget to bring something ( although they dont forget to charge you for it .\n[{'aspect': 'drink', 'opinion': 'forever', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: buy it for school , buy it for home , buy on for the grandkids .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuy it for school , buy it for home , buy on for the grandkids .\n->", + "output": "{\"text\": \"buy it for school , buy it for home , buy on for the grandkids .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the notebook is decent .\n->the notebook is decent .\n[{'aspect': 'notebook', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The service was excellent and the food was delicious .\n->The service was excellent and the food was delicious .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n->", + "output": "{\"text\": \"i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'larger', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'hdmi', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n->the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'storage', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'intel processor', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: Highly recommend this as great value for excellent sushi and service .\n->Highly recommend this as great value for excellent sushi and service .\n[{'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'value', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: you can right - click by using two fingers to tap the track pad at the same time ( or 2 fingers on the touchpad , using one of them to press press down / click the touchpad ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou can right - click by using two fingers to tap the track pad at the same time ( or 2 fingers on the touchpad , using one of them to press press down / click the touchpad ) .\n->", + "output": "{\"text\": \"you can right - click by using two fingers to tap the track pad at the same time ( or 2 fingers on the touchpad , using one of them to press press down / click the touchpad ) .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: open late ( well as late as i ever got there and i ' m a night person )\n->open late ( well as late as i ever got there and i ' m a night person )\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: You can not go wrong with this place .\n->You can not go wrong with this place .\n[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the feel of the touchpad is not as smooth ( frictionless ) as others i have used , but it ' s not terrible either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe feel of the touchpad is not as smooth ( frictionless ) as others i have used , but it ' s not terrible either .\n->", + "output": "{\"text\": \"the feel of the touchpad is not as smooth ( frictionless ) as others i have used , but it ' s not terrible either .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'not terrible', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very affordable and excellent ambient !\n->Very affordable and excellent ambient !\n[{'aspect': 'ambient', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambient', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my only other complaint is trackpad sensitivity .\n->my only other complaint is trackpad sensitivity .\n[{'aspect': 'trackpad sensitivity', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: however , it does feel like a sturdy hinge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , it does feel like a sturdy hinge .\n->", + "output": "{\"text\": \"however , it does feel like a sturdy hinge .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: save yourself the time and trouble and skip this one !\n->save yourself the time and trouble and skip this one !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it also has enough power to multi - task .\n->it also has enough power to multi - task .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the screen does not wobble as there is a good amount of friction holding it where you put it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen does not wobble as there is a good amount of friction holding it where you put it .\n->", + "output": "{\"text\": \"the screen does not wobble as there is a good amount of friction holding it where you put it .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n->i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n[{'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'inviting', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The food itself was just ok - nothing spectacular - but the service was awful .\n->The food itself was just ok - nothing spectacular - but the service was awful .\n[{'aspect': 'food', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\ntext: reserve your judgement until you ' ve updated the software entirely .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreserve your judgement until you ' ve updated the software entirely .\n->", + "output": "{\"text\": \"reserve your judgement until you ' ve updated the software entirely .\", \"labels\": \"[{'aspect': 'software', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was average or above including some surprising tasty dishes .\n->The food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 2 stars taken for horrible sound quality\n->2 stars taken for horrible sound quality\n[{'aspect': 'sound quality', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: giving lower stars for not realizing its limited capabilities is your own fault .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngiving lower stars for not realizing its limited capabilities is your own fault .\n->", + "output": "{\"text\": \"giving lower stars for not realizing its limited capabilities is your own fault .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A cool bar with great food , and tons of excellent beer .\n->A cool bar with great food , and tons of excellent beer .\n[{'aspect': 'bar', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s basically a useless brick , with shoddy hardware .\n->it ' s basically a useless brick , with shoddy hardware .\n[{'aspect': 'NULL', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'shoddy', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: i love the feel of a lighter os and can do many tasks using google / web based apps .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love the feel of a lighter os and can do many tasks using google / web based apps .\n->", + "output": "{\"text\": \"i love the feel of a lighter os and can do many tasks using google / web based apps .\", \"labels\": \"[{'aspect': 'os', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great laptop for a good price too !\n->great laptop for a good price too !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: We have been to this place many times , and always have great food , wine , and service .\n->We have been to this place many times , and always have great food , wine , and service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i can live with the so - so touchpad since the rest of it feels solid / sturdy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can live with the so - so touchpad since the rest of it feels solid / sturdy .\n->", + "output": "{\"text\": \"i can live with the so - so touchpad since the rest of it feels solid / sturdy .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'so - so', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'touchpad', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'touchpad', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you are indifferent about the screen , do not buy this !\n->if you are indifferent about the screen , do not buy this !\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the mouse area is responsive , and has a nice feel to it .\n->the mouse area is responsive , and has a nice feel to it .\n[{'aspect': 'mouse area', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: yes , this chromebook comes with the android app store pre - installed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyes , this chromebook comes with the android app store pre - installed .\n->", + "output": "{\"text\": \"yes , this chromebook comes with the android app store pre - installed .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was , from start to finish , a mind - bogglingly uncomfortable experience .\n->this was , from start to finish , a mind - bogglingly uncomfortable experience .\n[{'aspect': 'NULL', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: They are the best bagels I 've had .\n->They are the best bagels I 've had .\n[{'aspect': 'bagels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: although 4 gigs of ram doesn ' t seem like a lot to some of us , because of the simple software , it is very fast with no lag .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalthough 4 gigs of ram doesn ' t seem like a lot to some of us , because of the simple software , it is very fast with no lag .\n->", + "output": "{\"text\": \"although 4 gigs of ram doesn ' t seem like a lot to some of us , because of the simple software , it is very fast with no lag .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#QUALITY'}, {'aspect': 'software', 'opinion': 'simple', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n->this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'retina display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'compatibility', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: i will return it .\n->i will return it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: my wife upgraded from the 2 gigs of ram version to this , and it seems like there is more than twice the performance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy wife upgraded from the 2 gigs of ram version to this , and it seems like there is more than twice the performance .\n->", + "output": "{\"text\": \"my wife upgraded from the 2 gigs of ram version to this , and it seems like there is more than twice the performance .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n->i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n[{'aspect': 'performs', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: ive researched this and it is very common and apple says it ' s normal .\n->ive researched this and it is very common and apple says it ' s normal .\n[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: the screen is pleasantly satisfying with the touch screen and foldability .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is pleasantly satisfying with the touch screen and foldability .\n->", + "output": "{\"text\": \"the screen is pleasantly satisfying with the touch screen and foldability .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'pleasantly', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my cpu also runs over 3ghz most of the time with no heating issues either .\n->my cpu also runs over 3ghz most of the time with no heating issues either .\n[{'aspect': 'cpu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s beautiful and i love it , but i think i have to send it back .\n->it ' s beautiful and i love it , but i think i have to send it back .\n[{'aspect': 'NULL', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the power button is no longer on the keyboard , but is instead on the side of the machine which is fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe power button is no longer on the keyboard , but is instead on the side of the machine which is fine .\n->", + "output": "{\"text\": \"the power button is no longer on the keyboard , but is instead on the side of the machine which is fine .\", \"labels\": \"[{'aspect': 'power button', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Moderate prices .\n->Moderate prices .\n[{'aspect': 'prices', 'opinion': 'Moderate', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: By far , the best pizza in Manhattan .\n->By far , the best pizza in Manhattan .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this machine , through her minimum time of using it , has already been so much faster for her to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis machine , through her minimum time of using it , has already been so much faster for her to use .\n->", + "output": "{\"text\": \"this machine , through her minimum time of using it , has already been so much faster for her to use .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'faster', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: authentic pakistani food .\n->authentic pakistani food .\n[{'aspect': 'pakistani food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: battery life is good .\n->battery life is good .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: i ' m amazed at this product that has the same build as the other acer chromebooks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m amazed at this product that has the same build as the other acer chromebooks .\n->", + "output": "{\"text\": \"i ' m amazed at this product that has the same build as the other acer chromebooks .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: second where the heck is my other 8 gigs of ram ?\n->second where the heck is my other 8 gigs of ram ?\n[{'aspect': '8 gigs of ram', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: this is an amazing place to try some roti rolls .\n->this is an amazing place to try some roti rolls .\n[{'aspect': 'roti rolls', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\n->", + "output": "{\"text\": \"we are happy to be selling her old one we bought just a few months ago , in replacement of this new r11 !\", \"labels\": \"[{'aspect': 'r11', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice for one time special occasion .\n->nice for one time special occasion .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: open late ( well as late as i ever got there and i ' m a night person )\n->open late ( well as late as i ever got there and i ' m a night person )\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: 1st , i was shocked at how easy it was to set up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n1st , i was shocked at how easy it was to set up .\n->", + "output": "{\"text\": \"1st , i was shocked at how easy it was to set up .\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , the sandwiches ( nearing $ 7 ) did n ' t come with anything like chips or a side .\n->also , the sandwiches ( nearing $ 7 ) did n ' t come with anything like chips or a side .\n[{'aspect': 'sandwiches', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'sandwiches', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: those rolls were big , but not good and sashimi was n't fresh .\n->those rolls were big , but not good and sashimi was n't fresh .\n[{'aspect': 'sashimi', 'opinion': \"was n't fresh\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: in preparation for becoming a chromebook user i had purchased a 256 gig memory card and moved all my laptop files to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin preparation for becoming a chromebook user i had purchased a 256 gig memory card and moved all my laptop files to it .\n->", + "output": "{\"text\": \"in preparation for becoming a chromebook user i had purchased a 256 gig memory card and moved all my laptop files to it .\", \"labels\": \"[{'aspect': 'memory card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n->i loved this chromebook but i had to return it bevause it had sound issues .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n->Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n[{'aspect': 'Barbecued codfish', 'opinion': 'moist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seasoning', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spice rub', 'opinion': 'overwhelmed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'herb mix', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it worked great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit worked great .\n->", + "output": "{\"text\": \"it worked great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will return it .\n->i will return it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it ' s definately not a place to go if you want to impress someone .\n->it ' s definately not a place to go if you want to impress someone .\n[{'aspect': 'place', 'opinion': 'impress', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: every app i have downloaded from the google app store has worked perfectly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nevery app i have downloaded from the google app store has worked perfectly .\n->", + "output": "{\"text\": \"every app i have downloaded from the google app store has worked perfectly .\", \"labels\": \"[{'aspect': 'app', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: You should pass on the calamari .\n->You should pass on the calamari .\n[{'aspect': 'calamari', 'opinion': 'pass', 'polarity': 'negative', 'category': 'NULL'}]\ntext: amazon , as well as the google suite of docs , sheets , slides , and photos work great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \namazon , as well as the google suite of docs , sheets , slides , and photos work great .\n->", + "output": "{\"text\": \"amazon , as well as the google suite of docs , sheets , slides , and photos work great .\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google suite', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n->this is the first place i ' ve been that a runner remembers my order . . . hope he likes his job because i have half a mind to steal him for my restaurant .\n[{'aspect': 'runner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: but let me not recommend it .\n->but let me not recommend it .\n[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n->", + "output": "{\"text\": \"there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\", \"labels\": \"[{'aspect': 'screen resolution', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'not working well', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food - awesome .\n->food - awesome .\n[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The wine list is extensive and impressive .\n->The wine list is extensive and impressive .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this by far has been the easiest to set up and use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis by far has been the easiest to set up and use .\n->", + "output": "{\"text\": \"this by far has been the easiest to set up and use .\", \"labels\": \"[{'aspect': 'set up', 'opinion': 'easiest', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food now is inconsistent .\n->The food now is inconsistent .\n[{'aspect': 'food', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it ' s a perfect place to have a amazing indian food .\n->it ' s a perfect place to have a amazing indian food .\n[{'aspect': 'indian food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it is fast and simple .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is fast and simple .\n->", + "output": "{\"text\": \"it is fast and simple .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n->As a Japanese native , I 've lived in the Tristate area for over 8 years , but I was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: never fails to please .\n->never fails to please .\n[{'aspect': 'NULL', 'opinion': 'please', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: gates and this machine allows me to do this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngates and this machine allows me to do this .\n->", + "output": "{\"text\": \"gates and this machine allows me to do this .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu may be small , but everything on it is delicious .\n->The menu may be small , but everything on it is delicious .\n[{'aspect': 'menu', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: whether i ' m playing games or video editing , or web design , it doesn ' t hesitate .\n->whether i ' m playing games or video editing , or web design , it doesn ' t hesitate .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\ntext: of course , i have only had this acer for a week .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nof course , i have only had this acer for a week .\n->", + "output": "{\"text\": \"of course , i have only had this acer for a week .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is so cheap and the waiters are nice .\n->The food is so cheap and the waiters are nice .\n[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: works good but right click on mouse pad wont wok have to use external mouse\n->works good but right click on mouse pad wont wok have to use external mouse\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: if something happens within 30 days i will return it but i will get another one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif something happens within 30 days i will return it but i will get another one .\n->", + "output": "{\"text\": \"if something happens within 30 days i will return it but i will get another one .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n->as you can see , the cable is active , but the ` ` power led ` ` has never had any color .\n[{'aspect': 'cable', 'opinion': 'active', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'power led', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: it also has pretty decent i / o with two usb 3 .\n->it also has pretty decent i / o with two usb 3 .\n[{'aspect': 'i / o', 'opinion': 'decent', 'polarity': 'positive', 'category': 'PORTS#CONNECTIVITY'}]\ntext: i am very happy with my chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very happy with my chromebook !\n->", + "output": "{\"text\": \"i am very happy with my chromebook !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but after three charge cycles the screen started vibrating vigorously from side - to - side .\n->but after three charge cycles the screen started vibrating vigorously from side - to - side .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: so , i originally purchased this for the travel conveniences .\n->so , i originally purchased this for the travel conveniences .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PORTABILITY'}]\ntext: very poor experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery poor experience .\n->", + "output": "{\"text\": \"very poor experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'poor', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the audio for this laptop was poorly planned out .\n->- the audio for this laptop was poorly planned out .\n[{'aspect': 'audio for this laptop', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n->We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n[{'aspect': 'dinner specials', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner specials', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\ntext: customer service did not respond for almost a week , and i ended up paying + $ 20 in shipping to return the device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncustomer service did not respond for almost a week , and i ended up paying + $ 20 in shipping to return the device .\n->", + "output": "{\"text\": \"customer service did not respond for almost a week , and i ended up paying + $ 20 in shipping to return the device .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it will however win with substance .\n->it will however win with substance .\n[{'aspect': 'NULL', 'opinion': 'win', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard is very nice for me .\n->the keyboard is very nice for me .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the screen just went blank and would not shut down , stayed in a cycle that would bring up some gray screen and then just turn itself into sleep mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen just went blank and would not shut down , stayed in a cycle that would bring up some gray screen and then just turn itself into sleep mode .\n->", + "output": "{\"text\": \"the screen just went blank and would not shut down , stayed in a cycle that would bring up some gray screen and then just turn itself into sleep mode .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very affordable and excellent ambient !\n->Very affordable and excellent ambient !\n[{'aspect': 'ambient', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambient', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: so far , this chromebook is fantastic .\n->so far , this chromebook is fantastic .\n[{'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it has been just over 6 months since purchasing this and it already needs a 500 fix .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has been just over 6 months since purchasing this and it already needs a 500 fix .\n->", + "output": "{\"text\": \"it has been just over 6 months since purchasing this and it already needs a 500 fix .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while this thing is gorgeous and the perfect size for what i was looking for , my initial display died after not even 12 hours of use .\n->while this thing is gorgeous and the perfect size for what i was looking for , my initial display died after not even 12 hours of use .\n[{'aspect': 'NULL', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: it has a quality construction making it feel like a much more expensive laptop and the performance is perfect for those who use a chromebook for everyday computing and entertainment .\n->it has a quality construction making it feel like a much more expensive laptop and the performance is perfect for those who use a chromebook for everyday computing and entertainment .\n[{'aspect': 'performance', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this model has a defect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis model has a defect .\n->", + "output": "{\"text\": \"this model has a defect .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'defect', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n->they forgot a sandwich , did n ' t include plastic forks , and did n ' t include pita with the hummus platter .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The sushi is average and the prices are anything but .\n->The sushi is average and the prices are anything but .\n[{'aspect': 'sushi', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the screen blacks out and apple has refused to fix it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen blacks out and apple has refused to fix it .\n->", + "output": "{\"text\": \"the screen blacks out and apple has refused to fix it .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Should you happen to be impressed by the cuisine definitely try it .\n->Should you happen to be impressed by the cuisine definitely try it .\n[{'aspect': 'cuisine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they have refused to fix it to date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey have refused to fix it to date .\n->", + "output": "{\"text\": \"they have refused to fix it to date .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n->Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n[{'aspect': 'Quality of food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is great , and it ' s backlit !\n->the keyboard is great , and it ' s backlit !\n[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: not sure what the deal is but extremely disappointed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot sure what the deal is but extremely disappointed .\n->", + "output": "{\"text\": \"not sure what the deal is but extremely disappointed .\", \"labels\": \"[{'aspect': 'deal', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff ignored my friends and I the entire time we were there .\n->The staff ignored my friends and I the entire time we were there .\n[{'aspect': 'staff', 'opinion': 'ignored', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The $ 300 bill was a bit steep , but the experience was great .\n->The $ 300 bill was a bit steep , but the experience was great .\n[{'aspect': 'bill', 'opinion': 'steep', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the laptop is in great physical conditions , no scratches or anything , but certain actions run slowly , specifically any file read or write , like copying a document , uploading an image , creating a new file , etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop is in great physical conditions , no scratches or anything , but certain actions run slowly , specifically any file read or write , like copying a document , uploading an image , creating a new file , etc .\n->", + "output": "{\"text\": \"the laptop is in great physical conditions , no scratches or anything , but certain actions run slowly , specifically any file read or write , like copying a document , uploading an image , creating a new file , etc .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slowly', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i replug and restarted the laptop 3 times and it still does n ' t work .\n->i replug and restarted the laptop 3 times and it still does n ' t work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: When we sat , we got great and fast service .\n->When we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\n->", + "output": "{\"text\": \"the other weird thing is a ` ` popping ` ` noise i keep getting when i use the tracking pad .\", \"labels\": \"[{'aspect': 'tracking pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n->The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n[{'aspect': 'anti-pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the appetizers are also delicious !\n->the appetizers are also delicious !\n[{'aspect': 'appetizers', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it ' s a clicking or popping noise that comes from the left side speaker .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a clicking or popping noise that comes from the left side speaker .\n->", + "output": "{\"text\": \"it ' s a clicking or popping noise that comes from the left side speaker .\", \"labels\": \"[{'aspect': 'left side speaker', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the cable functions just fine .\n->the cable functions just fine .\n[{'aspect': 'cable', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: first went here to enjoy their garden terrace .\n->first went here to enjoy their garden terrace .\n[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: less than 90 days and the screen stopped working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nless than 90 days and the screen stopped working .\n->", + "output": "{\"text\": \"less than 90 days and the screen stopped working .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We all had the tasting menu and unlike some of the other reviews , I felt there was more than enough food .\n->We all had the tasting menu and unlike some of the other reviews , I felt there was more than enough food .\n[{'aspect': 'food', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is awesome .\n->the keyboard is awesome .\n[{'aspect': 'keyboard', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i contacted the seller and they seemed like they could care less .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni contacted the seller and they seemed like they could care less .\n->", + "output": "{\"text\": \"i contacted the seller and they seemed like they could care less .\", \"labels\": \"[{'aspect': 'seller', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer put together a solid package .\n->acer put together a solid package .\n[{'aspect': 'acer', 'opinion': 'solid', 'polarity': 'positive', 'category': 'COMPANY#QUALITY'}]\nExample:\ntext: As always we had a great glass of wine while we waited .\n->As always we had a great glass of wine while we waited .\n[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i bought this to save me money and now i wasted $ 700 to still have to go out and buy a new one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this to save me money and now i wasted $ 700 to still have to go out and buy a new one .\n->", + "output": "{\"text\": \"i bought this to save me money and now i wasted $ 700 to still have to go out and buy a new one .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s complementary , not revolutionary , which is much more intuitive and useful .\n->it ' s complementary , not revolutionary , which is much more intuitive and useful .\n[{'aspect': 'NULL', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\ntext: however there are many issues with this computer on start up that really bothered me and made my experience with a mac not that great\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever there are many issues with this computer on start up that really bothered me and made my experience with a mac not that great\n->", + "output": "{\"text\": \"however there are many issues with this computer on start up that really bothered me and made my experience with a mac not that great\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'bothered', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'mac', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n->My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n[{'aspect': 'crabmeat', 'opinion': 'unnecessarily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i plan on stopping by next week as well .\n->i plan on stopping by next week as well .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: as soon as i turned on the computer , it froze as i tried to sync information .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas soon as i turned on the computer , it froze as i tried to sync information .\n->", + "output": "{\"text\": \"as soon as i turned on the computer , it froze as i tried to sync information .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far , this chromebook is fantastic .\n->so far , this chromebook is fantastic .\n[{'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .\n->this is one of my favorite spot , very relaxing the food is great all the times , celebrated my engagement and my wedding here , it was very well organized .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: another problem i had was when i awakened my computer from sleeping , the wifi would not work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nanother problem i had was when i awakened my computer from sleeping , the wifi would not work .\n->", + "output": "{\"text\": \"another problem i had was when i awakened my computer from sleeping , the wifi would not work .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sandwiches are dry , tasteless and way overpriced .\n->The sandwiches are dry , tasteless and way overpriced .\n[{'aspect': 'sandwiches', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sandwiches', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this place survives on reputation alone .\n->this place survives on reputation alone .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i fixed it , however it took intensive research to fix it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni fixed it , however it took intensive research to fix it .\n->", + "output": "{\"text\": \"i fixed it , however it took intensive research to fix it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you must try the shrimp appetizers .\n->you must try the shrimp appetizers .\n[{'aspect': 'shrimp appetizers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n->", + "output": "{\"text\": \"so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: computer arrived doa .\n->computer arrived doa .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i see absolutely no lag on videos or streaming content .\n->i see absolutely no lag on videos or streaming content .\n[{'aspect': 'videos', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'streaming content', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i packaged it right back up and sent it back the same day i got it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni packaged it right back up and sent it back the same day i got it .\n->", + "output": "{\"text\": \"i packaged it right back up and sent it back the same day i got it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like the size of the laptop , and it ' s processing speed .\n->i like the size of the laptop , and it ' s processing speed .\n[{'aspect': 'size of the laptop', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the pizza is yummy and i like the atmoshpere .\n->the pizza is yummy and i like the atmoshpere .\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i had this laptop for a little over a year and it worked well at first .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had this laptop for a little over a year and it worked well at first .\n->", + "output": "{\"text\": \"i had this laptop for a little over a year and it worked well at first .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the trackpad is the ultimate difference - maker for me .\n->the trackpad is the ultimate difference - maker for me .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n->I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n[{'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i only used it work my college school work and light browsing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni only used it work my college school work and light browsing .\n->", + "output": "{\"text\": \"i only used it work my college school work and light browsing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: microphone is really low .\n->microphone is really low .\n[{'aspect': 'microphone', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: although the sound is not that good , this device replaced my ipad and i have never missed it !\n->although the sound is not that good , this device replaced my ipad and i have never missed it !\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: apple told me that they ca n ' t order those screws so i basically threw my money away on this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napple told me that they ca n ' t order those screws so i basically threw my money away on this laptop .\n->", + "output": "{\"text\": \"apple told me that they ca n ' t order those screws so i basically threw my money away on this laptop .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love the food .\n->love the food .\n[{'aspect': 'food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: unfortunately this macbook is part of the ` ` staingate ` ` series , i would not suggest to buy this as apple doesn ' t seem to care about customers buying a faulty product that is very expensive\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunfortunately this macbook is part of the ` ` staingate ` ` series , i would not suggest to buy this as apple doesn ' t seem to care about customers buying a faulty product that is very expensive\n->", + "output": "{\"text\": \"unfortunately this macbook is part of the ` ` staingate ` ` series , i would not suggest to buy this as apple doesn ' t seem to care about customers buying a faulty product that is very expensive\", \"labels\": \"[{'aspect': 'product', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was excellent , the food was excellent , but the entire experience was very cool .\n->The service was excellent , the food was excellent , but the entire experience was very cool .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a great laptop !\n->a great laptop !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: unfortunately , before i purchased it , i failed to research what the thunderbolt ports were .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunfortunately , before i purchased it , i failed to research what the thunderbolt ports were .\n->", + "output": "{\"text\": \"unfortunately , before i purchased it , i failed to research what the thunderbolt ports were .\", \"labels\": \"[{'aspect': 'thunderbolt ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The lox is always fresh too .\n->The lox is always fresh too .\n[{'aspect': 'lox', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you are looking for a good quality , cheap eats - this is the place .\n->if you are looking for a good quality , cheap eats - this is the place .\n[{'aspect': 'eats', 'opinion': 'good quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: as it turned out the thunderbolt ports are of no use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas it turned out the thunderbolt ports are of no use .\n->", + "output": "{\"text\": \"as it turned out the thunderbolt ports are of no use .\", \"labels\": \"[{'aspect': 'thunderbolt ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n->small light portable , excellent screen , very fast performance and soo quiet , does this have a fan ?\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n->very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n[{'aspect': 'sound volume', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: the machine looks amazing doesn ' t it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe machine looks amazing doesn ' t it !\n->", + "output": "{\"text\": \"the machine looks amazing doesn ' t it !\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n->The wait here is long for dim sum , but if you do n't like sharing tables or if the typical raucous dim sum atmosphere is not your gig , this is a sleek ( for Chinatown ) alternative .\n[{'aspect': 'wait', 'opinion': 'long', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'long', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dim sum atmosphere', 'opinion': 'typical raucous', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The ambience was nice , but service was n't so great .\n->The ambience was nice , but service was n't so great .\n[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': \"was n't so great\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: as the hours in your day wind down , they will tell you to go here and there , call you back , ultimately to find out that , ` ` i ' m sorry but we do not cover that ` ` .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas the hours in your day wind down , they will tell you to go here and there , call you back , ultimately to find out that , ` ` i ' m sorry but we do not cover that ` ` .\n->", + "output": "{\"text\": \"as the hours in your day wind down , they will tell you to go here and there , call you back , ultimately to find out that , ` ` i ' m sorry but we do not cover that ` ` .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i plugged it back in , let it fully charge as directed and have had no problems since .\n->i plugged it back in , let it fully charge as directed and have had no problems since .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n->your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n[{'aspect': 'retina screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: apple should ashamed to be associated with this cheap , lousy , clearly inferior , plastic nightmare - but then again , maybe that is exactly what they do want , so profits soar .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napple should ashamed to be associated with this cheap , lousy , clearly inferior , plastic nightmare - but then again , maybe that is exactly what they do want , so profits soar .\n->", + "output": "{\"text\": \"apple should ashamed to be associated with this cheap , lousy , clearly inferior , plastic nightmare - but then again , maybe that is exactly what they do want , so profits soar .\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'ashamed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'inferior', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it came in a regular brown box and the power cord was a bit scratched .\n->it came in a regular brown box and the power cord was a bit scratched .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: it works great !\n->it works great !\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i ' m pretty sure i ordered the biggest size , and i got the smaller one , but whatever it shipped fast and it works great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m pretty sure i ordered the biggest size , and i got the smaller one , but whatever it shipped fast and it works great .\n->", + "output": "{\"text\": \"i ' m pretty sure i ordered the biggest size , and i got the smaller one , but whatever it shipped fast and it works great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: rao is a good restaurant , but it ' s nothing special .\n->rao is a good restaurant , but it ' s nothing special .\n[{'aspect': 'rao', 'opinion': 'good', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'rao', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the display is brilliant , has the whitest whites and the blackest blacks , contrast is excellent and the colors are outstanding .\n->the display is brilliant , has the whitest whites and the blackest blacks , contrast is excellent and the colors are outstanding .\n[{'aspect': 'display', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'contrast', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'colors', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: have not heard any complaints about the product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhave not heard any complaints about the product .\n->", + "output": "{\"text\": \"have not heard any complaints about the product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n->there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n[{'aspect': 'screen resolution', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'not working well', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: i can ' t justify paying that kind of money for some ridiculous upgrade .\n->i can ' t justify paying that kind of money for some ridiculous upgrade .\n[{'aspect': 'NULL', 'opinion': 'ridiculous', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: apple told me to upgrade it to just buy the new one when they release it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napple told me to upgrade it to just buy the new one when they release it .\n->", + "output": "{\"text\": \"apple told me to upgrade it to just buy the new one when they release it .\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i expected quite a bit more from such an expensive menu .\n->i expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: we wo n ' t go to this place again for a good meal .\n->we wo n ' t go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: disappointed in reliability : used in a small business operated by my wife .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndisappointed in reliability : used in a small business operated by my wife .\n->", + "output": "{\"text\": \"disappointed in reliability : used in a small business operated by my wife .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely has issues with windows 10 .\n->definitely has issues with windows 10 .\n[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: love it !\n->love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: if known ahead of time , we would have not purchased this machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif known ahead of time , we would have not purchased this machine .\n->", + "output": "{\"text\": \"if known ahead of time , we would have not purchased this machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For years , I thought Tuscan cuisine was the best , but Salvatore converted me to the hearty Neapolitan fare on my first visit .\n->For years , I thought Tuscan cuisine was the best , but Salvatore converted me to the hearty Neapolitan fare on my first visit .\n[{'aspect': 'Neapolitan fare', 'opinion': 'hearty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n->The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'not very attentive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: apple used to make a wonderful product but my computer is now a useless paperweight until i have a new charger sent to me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napple used to make a wonderful product but my computer is now a useless paperweight until i have a new charger sent to me .\n->", + "output": "{\"text\": \"apple used to make a wonderful product but my computer is now a useless paperweight until i have a new charger sent to me .\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'wonderful', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'computer', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We both opted for a pasta dish and they were served timely and fresh .\n->We both opted for a pasta dish and they were served timely and fresh .\n[{'aspect': 'pasta dish', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i haven ' t had issues with the track pad as others have .\n->i haven ' t had issues with the track pad as others have .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#USABILITY'}]\ntext: stop working the macbook pro\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstop working the macbook pro\n->", + "output": "{\"text\": \"stop working the macbook pro\", \"labels\": \"[{'aspect': 'macbook pro', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n->there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n[{'aspect': 'screen resolution', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'not working well', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: 1 , bloatware from hell and 2 , a very cheap crappy 5400rpm hdd .\n->1 , bloatware from hell and 2 , a very cheap crappy 5400rpm hdd .\n[{'aspect': 'bloatware', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': '5400rpm hdd', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'HARD_DISC#PRICE'}, {'aspect': '5400rpm hdd', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'HARD_DISC#PRICE'}]\ntext: hold on before you decide to pay $ 2500 for this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhold on before you decide to pay $ 2500 for this laptop .\n->", + "output": "{\"text\": \"hold on before you decide to pay $ 2500 for this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quality ingredients preparation all around , and a very fair price for NYC .\n->Quality ingredients preparation all around , and a very fair price for NYC .\n[{'aspect': 'ingredients', 'opinion': 'Quality', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'fair', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop has an amazing price for the hardware it offers .\n->this laptop has an amazing price for the hardware it offers .\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'hardware', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'HARDWARE#PRICE'}]\ntext: it turns out that it ' s not a stain , but the anti - reflective coating coming off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit turns out that it ' s not a stain , but the anti - reflective coating coming off .\n->", + "output": "{\"text\": \"it turns out that it ' s not a stain , but the anti - reflective coating coming off .\", \"labels\": \"[{'aspect': 'anti - reflective coating', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apple told me to upgrade it to just buy the new one when they release it .\n->apple told me to upgrade it to just buy the new one when they release it .\n[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: has a genaric feel to it .\n->has a genaric feel to it .\n[{'aspect': 'NULL', 'opinion': 'genaric', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: i went to the apple store and they said it would be 900 to fix .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni went to the apple store and they said it would be 900 to fix .\n->", + "output": "{\"text\": \"i went to the apple store and they said it would be 900 to fix .\", \"labels\": \"[{'aspect': 'apple store', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop charger has sparked repeatedly .\n->the laptop charger has sparked repeatedly .\n[{'aspect': 'laptop charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n->With so many good restaurants on the UWS , I do n't need overpriced food , absurdly arrogant wait-staff who do n't recognize they work at a glorified diner , clumsy service , and management that does n't care .\n[{'aspect': 'food', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait-staff', 'opinion': 'arrogant', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'clumsy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: great aesthetics .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat aesthetics .\n->", + "output": "{\"text\": \"great aesthetics .\", \"labels\": \"[{'aspect': 'aesthetics', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they ' re like a little - known gem , practically unknown in my area .\n->they ' re like a little - known gem , practically unknown in my area .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: wifi was pretty bad .\n->wifi was pretty bad .\n[{'aspect': 'wifi was', 'opinion': 'bad', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\ntext: transferring files from a non - iphone phone , like android is extremely annoying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntransferring files from a non - iphone phone , like android is extremely annoying .\n->", + "output": "{\"text\": \"transferring files from a non - iphone phone , like android is extremely annoying .\", \"labels\": \"[{'aspect': 'android', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'SOFTWARE#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so before you get angry do your homework on why the laptop may be acting strange .\n->so before you get angry do your homework on why the laptop may be acting strange .\n[{'aspect': 'laptop', 'opinion': 'angry', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n->the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n->", + "output": "{\"text\": \"everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\", \"labels\": \"[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n->the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n[{'aspect': 'baked clams octopus', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: By far , the best pizza in Manhattan .\n->By far , the best pizza in Manhattan .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: ive researched this and it is very common and apple says it ' s normal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nive researched this and it is very common and apple says it ' s normal .\n->", + "output": "{\"text\": \"ive researched this and it is very common and apple says it ' s normal .\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: none of their android versions were what i would call usable .\n->none of their android versions were what i would call usable .\n[{'aspect': 'android versions', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: this is the best sushi in new york city - hands down .\n->this is the best sushi in new york city - hands down .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: - high resolution / dpi screen\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- high resolution / dpi screen\n->", + "output": "{\"text\": \"- high resolution / dpi screen\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , worst chromebook ever and i can ' t wait until it dies !\n->overall , worst chromebook ever and i can ' t wait until it dies !\n[{'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: trackpad is nice and quiet and responsive .\n->trackpad is nice and quiet and responsive .\n[{'aspect': 'trackpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: - thin and relatively lite weight\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- thin and relatively lite weight\n->", + "output": "{\"text\": \"- thin and relatively lite weight\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'lite', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i come here enjoy very much with husband .\n->i come here enjoy very much with husband .\n[{'aspect': 'NULL', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate decor will make you feel like you \u2019 re no longer in the city .\n->not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate decor will make you feel like you \u2019 re no longer in the city .\n[{'aspect': 'spot', 'opinion': 'hidden', 'polarity': 'neutral', 'category': 'LOCATION#GENERAL'}, {'aspect': 'unmarked wooden doors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: - fans run more often on the latest version .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- fans run more often on the latest version .\n->", + "output": "{\"text\": \"- fans run more often on the latest version .\", \"labels\": \"[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: noodles with shrimp and chicken and coconut juice is the MUST !\n->noodles with shrimp and chicken and coconut juice is the MUST !\n[{'aspect': 'noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n->i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n[{'aspect': 'NULL', 'opinion': 'biased', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: - for such a premium price , this is definitely the worst purchase quality wise that i have made .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- for such a premium price , this is definitely the worst purchase quality wise that i have made .\n->", + "output": "{\"text\": \"- for such a premium price , this is definitely the worst purchase quality wise that i have made .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'premium', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'two types of sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i go and eat out at many different restaurants and this is one place you have go and try .\n->i go and eat out at many different restaurants and this is one place you have go and try .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the quality is terrible ( both versions ) for such a pricey product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe quality is terrible ( both versions ) for such a pricey product .\n->", + "output": "{\"text\": \"the quality is terrible ( both versions ) for such a pricey product .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'product', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought an acer computer that did not work .\n->bought an acer computer that did not work .\n[{'aspect': 'acer computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: which , to be sure , is great , i had just hoped for something slightly more agile .\n->which , to be sure , is great , i had just hoped for something slightly more agile .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this is not a new unit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is not a new unit .\n->", + "output": "{\"text\": \"this is not a new unit .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i first got it in the mail and i opened it up and turned it on for the first time , i was a little speechless because the retina display looks so good !\n->when i first got it in the mail and i opened it up and turned it on for the first time , i was a little speechless because the retina display looks so good !\n[{'aspect': 'retina display', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: i have used this laptop only for work and the battery lasts and hour at most on mid performance .\n->i have used this laptop only for work and the battery lasts and hour at most on mid performance .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i made the mistake of thinking it was new .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni made the mistake of thinking it was new .\n->", + "output": "{\"text\": \"i made the mistake of thinking it was new .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook loads everything lightning fast and provides a simple user experience that gets you where you need to go quickly and efficiently .\n->the chromebook loads everything lightning fast and provides a simple user experience that gets you where you need to go quickly and efficiently .\n[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n->it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'screen', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had nothing but problems with my macbook connecting to my wifi from the beginning .\n->", + "output": "{\"text\": \"i have had nothing but problems with my macbook connecting to my wifi from the beginning .\", \"labels\": \"[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just got this chromebook last week and since then i ' ve had to do a hard reboot once and it has reset itself to factory settings about 5 times .\n->i just got this chromebook last week and since then i ' ve had to do a hard reboot once and it has reset itself to factory settings about 5 times .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n->the space is limited so be prepared to wait up to 45 minutes - 1 hour , but be richly rewarded when you savor the delicious indo - chinese food .\n[{'aspect': 'space', 'opinion': 'limited', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'indo - chinese food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: absolutly horrible product , cheap components make for unreliable products , but when the nearest service location is 50 + miles away i wonder how much apple truly cares ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nabsolutly horrible product , cheap components make for unreliable products , but when the nearest service location is 50 + miles away i wonder how much apple truly cares ?\n->", + "output": "{\"text\": \"absolutly horrible product , cheap components make for unreliable products , but when the nearest service location is 50 + miles away i wonder how much apple truly cares ?\", \"labels\": \"[{'aspect': 'product', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'components', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'products', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'service location', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: too bad i had paid an extra $ 2 for the stone bowl .\n->too bad i had paid an extra $ 2 for the stone bowl .\n[{'aspect': 'stone bowl', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it started throwing i / o errors ( log viewer ) and occasionally ` ` system error - 50 ` ` would show .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit started throwing i / o errors ( log viewer ) and occasionally ` ` system error - 50 ` ` would show .\n->", + "output": "{\"text\": \"it started throwing i / o errors ( log viewer ) and occasionally ` ` system error - 50 ` ` would show .\", \"labels\": \"[{'aspect': 'i / o', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything is very smooth and fast .\n->everything is very smooth and fast .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: I have been to Casimir over 5 times and I have always had a great time there .\n->I have been to Casimir over 5 times and I have always had a great time there .\n[{'aspect': 'Casimir', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it would power on , start to boot , then abruptly power down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit would power on , start to boot , then abruptly power down .\n->", + "output": "{\"text\": \"it would power on , start to boot , then abruptly power down .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the freshest , best variety , and the fastest delivery .\n->the freshest , best variety , and the fastest delivery .\n[{'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n->once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n[{'aspect': 'cosette', 'opinion': 'off - the - beaten', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: my macbook pro 15 \u201d can overheating when some do it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy macbook pro 15 \u201d can overheating when some do it !\n->", + "output": "{\"text\": \"my macbook pro 15 \u201d can overheating when some do it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but just at first glance , this thing is top quality .\n->but just at first glance , this thing is top quality .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i thought the gizmo was neat but i was disappointed that i couldn ' t really use it the way i wanted to it that regards .\n->i thought the gizmo was neat but i was disappointed that i couldn ' t really use it the way i wanted to it that regards .\n[{'aspect': 'gizmo', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyour new retina screen will get stains on it from the top layer seperating and or dead pixels .\n->", + "output": "{\"text\": \"your new retina screen will get stains on it from the top layer seperating and or dead pixels .\", \"labels\": \"[{'aspect': 'retina screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n->i am relatively new to the area and tried pick a bgel on 2nd and was disappointed with the service and i thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: While the prices are nothing special , the portions are huge .\n->While the prices are nothing special , the portions are huge .\n[{'aspect': 'prices', 'opinion': 'special', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overpriced low quality product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverpriced low quality product .\n->", + "output": "{\"text\": \"overpriced low quality product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'low', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you purchase this product , go into it with realistic expectations and patience getting familiar with the setup and you ' ll probably love it , too !\n->if you purchase this product , go into it with realistic expectations and patience getting familiar with the setup and you ' ll probably love it , too !\n[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: For the people who want great food plus great service , Roxy is a place to AVOID !\n->For the people who want great food plus great service , Roxy is a place to AVOID !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}]\ntext: my laptop is affected by the staingate issue and apple denied responsibility , saying that it ' s a cosmetic problem caused by improper cleaning , yet there are thousands of reports on this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy laptop is affected by the staingate issue and apple denied responsibility , saying that it ' s a cosmetic problem caused by improper cleaning , yet there are thousands of reports on this .\n->", + "output": "{\"text\": \"my laptop is affected by the staingate issue and apple denied responsibility , saying that it ' s a cosmetic problem caused by improper cleaning , yet there are thousands of reports on this .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked great .\n->it worked great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The main downside to the place is the nazi-like guy running it who constantly complains about the noise level .\n->The main downside to the place is the nazi-like guy running it who constantly complains about the noise level .\n[{'aspect': 'noise level', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'guy', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\ntext: apple should be embarrassed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napple should be embarrassed .\n->", + "output": "{\"text\": \"apple should be embarrassed .\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'embarrassed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: had it for a week now and still finding things it can not do .\n->had it for a week now and still finding things it can not do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n->the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n[{'aspect': 'startup time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: * excellent form factor , extremely portable while remaining a serious pro computer\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* excellent form factor , extremely portable while remaining a serious pro computer\n->", + "output": "{\"text\": \"* excellent form factor , extremely portable while remaining a serious pro computer\", \"labels\": \"[{'aspect': 'pro computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro computer', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve waited over one hour for food .\n->i ' ve waited over one hour for food .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n->I was here a few weeks back and we had the worst customer service experience at a restaurant ever .\n[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: * incredible display\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* incredible display\n->", + "output": "{\"text\": \"* incredible display\", \"labels\": \"[{'aspect': 'display', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n->bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n[{'aspect': 'specs', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the pro worked great until now .\n->the pro worked great until now .\n[{'aspect': 'pro', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: * good keyboard\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* good keyboard\n->", + "output": "{\"text\": \"* good keyboard\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I think I 've had some the best meals of my life at minnow .\n->I think I 've had some the best meals of my life at minnow .\n[{'aspect': 'meals', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: * decent selection of ports for its size\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* decent selection of ports for its size\n->", + "output": "{\"text\": \"* decent selection of ports for its size\", \"labels\": \"[{'aspect': 'ports', 'opinion': 'decent', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n->Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'creme brulee', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sugar', 'opinion': 'charred', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The ambiance is minimal the food is not phenomenal , but some dishes are quite good , such as the eggplant parmesan , veal in carozza chicken saltimbocca .\n->The ambiance is minimal the food is not phenomenal , but some dishes are quite good , such as the eggplant parmesan , veal in carozza chicken saltimbocca .\n[{'aspect': 'ambiance', 'opinion': 'minimal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not phenomenal', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'eggplant parmesan', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'veal in carozza chicken saltimbocca', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: * very weak wifi reception from the built - in antenna .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* very weak wifi reception from the built - in antenna .\n->", + "output": "{\"text\": \"* very weak wifi reception from the built - in antenna .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'weak', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there is a buzzing sound that comes from inside of the keyboard .\n->there is a buzzing sound that comes from inside of the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n->For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the machine is near - unusable out of the box .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe machine is near - unusable out of the box .\n->", + "output": "{\"text\": \"the machine is near - unusable out of the box .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambience is delightful , service impeccable .\n->Ambience is delightful , service impeccable .\n[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n->i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n[{'aspect': 'size', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n->", + "output": "{\"text\": \"it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n->The Steak Tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m seriously considering returning it !\n->i ' m seriously considering returning it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: you have to ` ` increase contrast ` ` and ` ` reduce transparency ` ` in the accessibility options to make it perform ok , but with a ui that looks like s * * * .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyou have to ` ` increase contrast ` ` and ` ` reduce transparency ` ` in the accessibility options to make it perform ok , but with a ui that looks like s * * * .\n->", + "output": "{\"text\": \"you have to ` ` increase contrast ` ` and ` ` reduce transparency ` ` in the accessibility options to make it perform ok , but with a ui that looks like s * * * .\", \"labels\": \"[{'aspect': 'ui', 'opinion': 's * * *', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is great and the environment is even better .\n->the food is great and the environment is even better .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'environment', 'opinion': 'better', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: this is exactly what i need and nothing more .\n->this is exactly what i need and nothing more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: paradoxically , this laptop is a ` ` pro ` ` .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nparadoxically , this laptop is a ` ` pro ` ` .\n->", + "output": "{\"text\": \"paradoxically , this laptop is a ` ` pro ` ` .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n->For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: cursor won ' t move and touchscreen won ' t work and from reading about this online i am not alone with this issue .\n->cursor won ' t move and touchscreen won ' t work and from reading about this online i am not alone with this issue .\n[{'aspect': 'cursor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: sadly , apple is moving into fashion - - your laptop is no longer a performing athlete .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsadly , apple is moving into fashion - - your laptop is no longer a performing athlete .\n->", + "output": "{\"text\": \"sadly , apple is moving into fashion - - your laptop is no longer a performing athlete .\", \"labels\": \"[{'aspect': 'apple', 'opinion': 'sadly', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: BE CAREFUL before you request extra spice .\n->BE CAREFUL before you request extra spice .\n[{'aspect': 'spice', 'opinion': 'CAREFUL', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The prices were fantastic .\n->The prices were fantastic .\n[{'aspect': 'prices', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: had it for a week now and still finding things it can not do .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhad it for a week now and still finding things it can not do .\n->", + "output": "{\"text\": \"had it for a week now and still finding things it can not do .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n->My entree of hot pot with seafood was full of imitation crabmeat with a couple pieces of shrimp and squid , and was unnecessarily heated with a burner .\n[{'aspect': 'crabmeat', 'opinion': 'unnecessarily', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The food was good .\n->The food was good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is a great computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great computer .\n->", + "output": "{\"text\": \"this is a great computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all in all , i ' m really glad i got this machine .\n->all in all , i ' m really glad i got this machine .\n[{'aspect': 'machine', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Made my dining experience uncomfortable .\n->Made my dining experience uncomfortable .\n[{'aspect': 'dining experience', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: but how expensive it is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut how expensive it is .\n->", + "output": "{\"text\": \"but how expensive it is .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: build quality seems excellent .\n->build quality seems excellent .\n[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the pic quality is pretty good , though not 4k , the sound is pretty good for a laptop that is less than a half inch thick and weighs less than 3 pounds .\n->the pic quality is pretty good , though not 4k , the sound is pretty good for a laptop that is less than a half inch thick and weighs less than 3 pounds .\n[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: i can not leave now because i need my computer fixed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can not leave now because i need my computer fixed .\n->", + "output": "{\"text\": \"i can not leave now because i need my computer fixed .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop is pretty lightweight .\n->the laptop is pretty lightweight .\n[{'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: touch - screen features are responsive .\n->touch - screen features are responsive .\n[{'aspect': 'touch - screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: beware that stains in the coating of the display have been detected in all of the macbook retina editions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeware that stains in the coating of the display have been detected in all of the macbook retina editions .\n->", + "output": "{\"text\": \"beware that stains in the coating of the display have been detected in all of the macbook retina editions .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n->i ' m a writer , and this has easily become my main computer for both work and play ( google docs easily replaces word , and android apps make it much better for gaming than i originally expected ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'android apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n->You do n't go to Mizu for excellent service , you go for the large amounts of food , the amiable atmosphere , and the hole-in-the-wall feeling of the place .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'amiable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: however , after a couple of months the keyboard case started to crack at the corner .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , after a couple of months the keyboard case started to crack at the corner .\n->", + "output": "{\"text\": \"however , after a couple of months the keyboard case started to crack at the corner .\", \"labels\": \"[{'aspect': 'keyboard case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n->this chromebook is easy to use , boot up quickly but it does occasionally crash and a push of the on / off button will quickly reset it .\n[{'aspect': 'chromebook', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'boot up', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: He served me an Uni Hand roll , which I never had before , and let me tell you ... IT WAS HEAVEN !\n->He served me an Uni Hand roll , which I never had before , and let me tell you ... IT WAS HEAVEN !\n[{'aspect': 'Uni Hand roll', 'opinion': 'HEAVEN', 'polarity': 'positive', 'category': 'NULL'}]\ntext: update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupdate - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n->", + "output": "{\"text\": \"update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\", \"labels\": \"[{'aspect': 'keyboard cover', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the device itself is light and handsome - but virtually useless for long documents .\n->the device itself is light and handsome - but virtually useless for long documents .\n[{'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'handsome', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: and you ca n ' t beat the prices .\n->and you ca n ' t beat the prices .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: this device would be a good choice if it weren ' t so poorly constructed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis device would be a good choice if it weren ' t so poorly constructed .\n->", + "output": "{\"text\": \"this device would be a good choice if it weren ' t so poorly constructed .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'poorly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but this asus c302ca has blown me away .\n->but this asus c302ca has blown me away .\n[{'aspect': 'asus c302ca', 'opinion': 'blown me away', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the service was excellent - friendly and attentive .\n->the service was excellent - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i originally purchased this at the beginning of april after about a week it stopped charging ( would receive ` ` low power charger ` ` connected message and not charge ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni originally purchased this at the beginning of april after about a week it stopped charging ( would receive ` ` low power charger ` ` connected message and not charge ) .\n->", + "output": "{\"text\": \"i originally purchased this at the beginning of april after about a week it stopped charging ( would receive ` ` low power charger ` ` connected message and not charge ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n->Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i think i will forever be a mac user from now on , it is an awesome product !\n->i think i will forever be a mac user from now on , it is an awesome product !\n[{'aspect': 'product', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ended up returning it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ended up returning it .\n->", + "output": "{\"text\": \"i ended up returning it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It was not above ordinary and the beef version had cheap ( undercooked ) beef .\n->It was not above ordinary and the beef version had cheap ( undercooked ) beef .\n[{'aspect': 'beef version', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'beef version', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n->service , however , was excellent . . . and i liked the setting / atmosphere a lot .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'setting / atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: my second asus chromebook has died .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy second asus chromebook has died .\n->", + "output": "{\"text\": \"my second asus chromebook has died .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: drawbacks : service is slow and they do n ' t toast !\n->drawbacks : service is slow and they do n ' t toast !\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Food is great .\n->Food is great .\n[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but asus chromebooks have a fatsl flaw .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut asus chromebooks have a fatsl flaw .\n->", + "output": "{\"text\": \"but asus chromebooks have a fatsl flaw .\", \"labels\": \"[{'aspect': 'asus chromebooks', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nha Trang , while being notorious for utter lack of comfort and decor , horribly slow wait staff and horribly quick meals , is one of the best vietnamese restaurants i 've ever been to . the pho is delicious and comes with very fresh vegtables .\n->Nha Trang , while being notorious for utter lack of comfort and decor , horribly slow wait staff and horribly quick meals , is one of the best vietnamese restaurants i 've ever been to . the pho is delicious and comes with very fresh vegtables .\n[{'aspect': 'comfort', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'lack', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'horribly slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'meals', 'opinion': 'horribly quick', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pho', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegtables', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Their pad penang is delicious and everything else is fantastic .\n->Their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i was so excited to buy the asus chromebook , and bought some for my grandchildren .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was so excited to buy the asus chromebook , and bought some for my grandchildren .\n->", + "output": "{\"text\": \"i was so excited to buy the asus chromebook , and bought some for my grandchildren .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n->the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n[{'aspect': 'touch screen', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: best restaurant in brooklyn\n->best restaurant in brooklyn\n[{'aspect': 'restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: but this asus , who offered so much & rated over 4 stars , turned to a black screen , just over a month after i received it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut this asus , who offered so much & rated over 4 stars , turned to a black screen , just over a month after i received it .\n->", + "output": "{\"text\": \"but this asus , who offered so much & rated over 4 stars , turned to a black screen , just over a month after i received it .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great toppings definitely a place you need to check out for late night munchies or a mid day boost !\n->great toppings definitely a place you need to check out for late night munchies or a mid day boost !\n[{'aspect': 'toppings', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Toons has recently been redone , so it 's now a very attractive space .\n->Toons has recently been redone , so it 's now a very attractive space .\n[{'aspect': 'Toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: lo and behold , i hadn ' t read quite all of the 1 * reviews : the worst was a teacher who had ordered so many for her several classes and said ` ` i think the problem is with the motherboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlo and behold , i hadn ' t read quite all of the 1 * reviews : the worst was a teacher who had ordered so many for her several classes and said ` ` i think the problem is with the motherboard .\n->", + "output": "{\"text\": \"lo and behold , i hadn ' t read quite all of the 1 * reviews : the worst was a teacher who had ordered so many for her several classes and said ` ` i think the problem is with the motherboard .\", \"labels\": \"[{'aspect': 'motherboard', 'opinion': 'worst', 'polarity': 'negative', 'category': 'MOTHERBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n->Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n[{'aspect': 'Chow fun', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pork shu mai', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: I took my girlfriend there for her birthday last night and we had a relaxing , really good meal .\n->I took my girlfriend there for her birthday last night and we had a relaxing , really good meal .\n[{'aspect': 'meal', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: we ' ve got android nougat on beta running pretty well now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ' ve got android nougat on beta running pretty well now .\n->", + "output": "{\"text\": \"we ' ve got android nougat on beta running pretty well now .\", \"labels\": \"[{'aspect': 'android nougat', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a great place to meet up for some food and drinks . . .\n->a great place to meet up for some food and drinks . . .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: It is so easy to get a reservation at a top place in NYC with a week 's notice .\n->It is so easy to get a reservation at a top place in NYC with a week 's notice .\n[{'aspect': 'reservation', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmost apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n->", + "output": "{\"text\": \"most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'ok', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor .\n->It 's boring on the inside , and our sushi was pretty below average ... the tuna was soggy and the other rolls had no flavor .\n[{'aspect': 'sushi', 'opinion': 'below average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tuna', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: When going out for a nice dinner , I like a nice ambiance as well as very good food .\n->When going out for a nice dinner , I like a nice ambiance as well as very good food .\n[{'aspect': 'dinner', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the stock video player misses audio on a lot of movies , and using the vlc app ( android ) is super buggy ( frequently freezes and shuts down ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe stock video player misses audio on a lot of movies , and using the vlc app ( android ) is super buggy ( frequently freezes and shuts down ) .\n->", + "output": "{\"text\": \"the stock video player misses audio on a lot of movies , and using the vlc app ( android ) is super buggy ( frequently freezes and shuts down ) .\", \"labels\": \"[{'aspect': 'stock video player', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'vlc app', 'opinion': 'buggy', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n->BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n[{'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai spiced curry noodles with shrimp', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was slow had to wait to order and get food although not crowded .\n->service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it ' s increasingly disappointing to see the much - hyped play access is still nonexistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s increasingly disappointing to see the much - hyped play access is still nonexistent .\n->", + "output": "{\"text\": \"it ' s increasingly disappointing to see the much - hyped play access is still nonexistent .\", \"labels\": \"[{'aspect': 'play access', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'SOFTWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n->the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n[{'aspect': 'apps', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google docs / drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: drawbacks : service is slow and they do n ' t toast !\n->drawbacks : service is slow and they do n ' t toast !\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i ' m still loving the * idea * of what the c302 is supposed to be , but the execution is feeling more and more of a miss .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m still loving the * idea * of what the c302 is supposed to be , but the execution is feeling more and more of a miss .\n->", + "output": "{\"text\": \"i ' m still loving the * idea * of what the c302 is supposed to be , but the execution is feeling more and more of a miss .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'c302', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speed at which this charges and reboots is amazing , and the battery life is long .\n->the speed at which this charges and reboots is amazing , and the battery life is long .\n[{'aspect': 'charges', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'reboots', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The pickles were great addition .\n->The pickles were great addition .\n[{'aspect': 'pickles', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: needless to say , android / play rollout is being managed very , very poorly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nneedless to say , android / play rollout is being managed very , very poorly .\n->", + "output": "{\"text\": \"needless to say , android / play rollout is being managed very , very poorly .\", \"labels\": \"[{'aspect': 'android / play rollout', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The seats are uncomfortable if you are sitting against the wall on wooden benches .\n->The seats are uncomfortable if you are sitting against the wall on wooden benches .\n[{'aspect': 'seats', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: in practice , the device is heavier than is comfortable for this .\n->in practice , the device is heavier than is comfortable for this .\n[{'aspect': 'device', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'device', 'opinion': 'than is comfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: for the moment , please note - * * * this product no longer works with the google play store in stable or beta * * * and there is no communication about what we can expect , or when .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the moment , please note - * * * this product no longer works with the google play store in stable or beta * * * and there is no communication about what we can expect , or when .\n->", + "output": "{\"text\": \"for the moment , please note - * * * this product no longer works with the google play store in stable or beta * * * and there is no communication about what we can expect , or when .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mem , hdd , screed , dvd drive are all easily accessible and removable .\n->mem , hdd , screed , dvd drive are all easily accessible and removable .\n[{'aspect': 'mem', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'mem', 'opinion': 'removable', 'polarity': 'positive', 'category': 'MEMORY#USABILITY'}, {'aspect': 'hdd', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'hdd', 'opinion': 'removable', 'polarity': 'positive', 'category': 'HARD_DISC#USABILITY'}, {'aspect': 'screed', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screed', 'opinion': 'removable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'accessible', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}, {'aspect': 'dvd drive', 'opinion': 'removable', 'polarity': 'positive', 'category': 'OPTICAL_DRIVES#USABILITY'}]\nExample:\ntext: The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n->The in-house lady DJ on Saturday nights has outrageously good taste in music , and moreover , takes requests .\n[{'aspect': 'in-house lady DJ', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n->", + "output": "{\"text\": \"i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'software', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The buffet had a nice selection .\n->The buffet had a nice selection .\n[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'selection', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: nice little notebook !\n->nice little notebook !\n[{'aspect': 'notebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsome of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n->", + "output": "{\"text\": \"some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'respectable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'builds', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard dock', 'opinion': 'superior', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only thing more wonderful than the food ( which is exceptional ) is the service .\n->The only thing more wonderful than the food ( which is exceptional ) is the service .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Thius is a must for anyone who loves Shabu-Shabu .\n->Thius is a must for anyone who loves Shabu-Shabu .\n[{'aspect': 'Shabu-Shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i remember them fondly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni remember them fondly .\n->", + "output": "{\"text\": \"i remember them fondly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fondly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n->the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: fast , beautiful display , comfortable keyboard , and the android apps work well ( not great yet , but well ) .\n->fast , beautiful display , comfortable keyboard , and the android apps work well ( not great yet , but well ) .\n[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'display', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'not great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: the asus c302 is , for me , the dream of that perfect asus tablet / keyboard combo made real .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe asus c302 is , for me , the dream of that perfect asus tablet / keyboard combo made real .\n->", + "output": "{\"text\": \"the asus c302 is , for me , the dream of that perfect asus tablet / keyboard combo made real .\", \"labels\": \"[{'aspect': 'asus c302', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is now my fastest - charging device .\n->this is now my fastest - charging device .\n[{'aspect': 'device', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: It is one the nicest outdoor restaurants I have ever seen in NY -- I am from Italy and this place rivals the ones in my country .\n->It is one the nicest outdoor restaurants I have ever seen in NY -- I am from Italy and this place rivals the ones in my country .\n[{'aspect': 'outdoor restaurants', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - the m3 processor is * plenty * of power for all chrome , video , and android requirements .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the m3 processor is * plenty * of power for all chrome , video , and android requirements .\n->", + "output": "{\"text\": \"- the m3 processor is * plenty * of power for all chrome , video , and android requirements .\", \"labels\": \"[{'aspect': 'm3 processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have known about this secret for the last 13 years , emilio ( the godfather ) has continued to serve food and wine for the gods at mortal prices .\n->i have known about this secret for the last 13 years , emilio ( the godfather ) has continued to serve food and wine for the gods at mortal prices .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'wine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: but they didn ' t make it $ 300 by wasting money , there are compromises .\n->but they didn ' t make it $ 300 by wasting money , there are compromises .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: that ' s how confident i am in the asus after 10 days .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat ' s how confident i am in the asus after 10 days .\n->", + "output": "{\"text\": \"that ' s how confident i am in the asus after 10 days .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'confident', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wo n ' t go back unless someone else is footing the bill .\n->i wo n ' t go back unless someone else is footing the bill .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: you ca n ' t go wrong with this place .\n->you ca n ' t go wrong with this place .\n[{'aspect': 'place', 'opinion': \"ca n ' t go wrong\", 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: - android apps work rather well , even if not perfectly yet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- android apps work rather well , even if not perfectly yet .\n->", + "output": "{\"text\": \"- android apps work rather well , even if not perfectly yet .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'not perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer hold its ground , but it has specs to be a killer .\n->the computer hold its ground , but it has specs to be a killer .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: msi used to have a driver installer by disk that would install all the proper drivers in the right order , so you ' d have the perfect configuration .\n->msi used to have a driver installer by disk that would install all the proper drivers in the right order , so you ' d have the perfect configuration .\n[{'aspect': 'driver installer', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: i ' m still using chrome for almost all native google apps , however , since chrome tabs / apps simply work better / faster / easier than their android counterparts .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m still using chrome for almost all native google apps , however , since chrome tabs / apps simply work better / faster / easier than their android counterparts .\n->", + "output": "{\"text\": \"i ' m still using chrome for almost all native google apps , however , since chrome tabs / apps simply work better / faster / easier than their android counterparts .\", \"labels\": \"[{'aspect': 'chrome tabs / apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chrome tabs / apps', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chrome tabs / apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}, {'aspect': 'chrome tabs / apps', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one , the display is gorgeous ; watching video is a treat .\n->one , the display is gorgeous ; watching video is a treat .\n[{'aspect': 'display', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'treat', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: quite frankly , this is some of the worst sushi i have ever tried .\n->quite frankly , this is some of the worst sushi i have ever tried .\n[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the c302 is clean and feels solid .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe c302 is clean and feels solid .\n->", + "output": "{\"text\": \"the c302 is clean and feels solid .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'c302', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was the only thing good about this restaurant .\n->The service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n->- although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen ratio', 'opinion': 'not optimal', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: this is a nice perk to save battery life and for folks who like to read / / browse late at night in bed and do n ' t want the backlighting on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a nice perk to save battery life and for folks who like to read / / browse late at night in bed and do n ' t want the backlighting on .\n->", + "output": "{\"text\": \"this is a nice perk to save battery life and for folks who like to read / / browse late at night in bed and do n ' t want the backlighting on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n->i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'boot speed', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'OS#GENERAL'}, {'aspect': 'cooling fan', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: beautiful display and runs fast !\n->beautiful display and runs fast !\n[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this is now my fastest - charging device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is now my fastest - charging device .\n->", + "output": "{\"text\": \"this is now my fastest - charging device .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ' ll be there for every anniversary , birthday , valentines day . . .\n->you ' ll be there for every anniversary , birthday , valentines day . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: i ' ve called twice to try to connect it to printer , first call they told me i had to get a cloud ready printer after getting said printer it still doesn ' t connect .\n->i ' ve called twice to try to connect it to printer , first call they told me i had to get a cloud ready printer after getting said printer it still doesn ' t connect .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: it charges insanely fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit charges insanely fast .\n->", + "output": "{\"text\": \"it charges insanely fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Faan is sooo good .\n->Faan is sooo good .\n[{'aspect': 'Faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i had fish and my husband had the filet - both of which exceeded our expectations .\n->i had fish and my husband had the filet - both of which exceeded our expectations .\n[{'aspect': 'fish', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'filet', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: - jstorrent works nicely for any torrenting needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- jstorrent works nicely for any torrenting needs .\n->", + "output": "{\"text\": \"- jstorrent works nicely for any torrenting needs .\", \"labels\": \"[{'aspect': 'jstorrent', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most of the servers are very attentive , friendly and quite attractive .\n->most of the servers are very attentive , friendly and quite attractive .\n[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: will prob . not return but it is a great dinning experience to try at least once .\n->will prob . not return but it is a great dinning experience to try at least once .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: - android support is only in beta mode as of february 5 , 2017 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- android support is only in beta mode as of february 5 , 2017 .\n->", + "output": "{\"text\": \"- android support is only in beta mode as of february 5 , 2017 .\", \"labels\": \"[{'aspect': 'android support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n->i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n[{'aspect': 'mizu', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n->this was the perfect quiet , relaxing , and delicious accompaniment to our afternoon of theater .\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i ' ll end the review simply saying i ' m very happy overall with my purchase !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ll end the review simply saying i ' m very happy overall with my purchase !\n->", + "output": "{\"text\": \"i ' ll end the review simply saying i ' m very happy overall with my purchase !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: on a recent sunday afternoon , a friend and i accidently found this great restaurant on our way to see the pulitzer prize winning play doubt .\n->on a recent sunday afternoon , a friend and i accidently found this great restaurant on our way to see the pulitzer prize winning play doubt .\n[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i can bring it anywhere because of how small it is : $\n->i can bring it anywhere because of how small it is : $\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: great machine out of the box .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat machine out of the box .\n->", + "output": "{\"text\": \"great machine out of the box .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mac is life , but i take one star away for the price .\n->mac is life , but i take one star away for the price .\n[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: Fantastic place .\n->Fantastic place .\n[{'aspect': 'place', 'opinion': 'Fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but after only a couple of days the thing just turned off and won ' t turn back on again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut after only a couple of days the thing just turned off and won ' t turn back on again .\n->", + "output": "{\"text\": \"but after only a couple of days the thing just turned off and won ' t turn back on again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you are indifferent about the screen , do not buy this !\n->if you are indifferent about the screen , do not buy this !\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: And the fried clams had just enough kick to them to make 'em worth eating .\n->And the fried clams had just enough kick to them to make 'em worth eating .\n[{'aspect': 'fried clams', 'opinion': 'enough', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i tired every remedy found online , and even got in touch with asus tech support who just said ` ` send it back ` ` .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni tired every remedy found online , and even got in touch with asus tech support who just said ` ` send it back ` ` .\n->", + "output": "{\"text\": \"i tired every remedy found online , and even got in touch with asus tech support who just said ` ` send it back ` ` .\", \"labels\": \"[{'aspect': 'asus tech support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first the volume does not get very loud .\n->first the volume does not get very loud .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: mistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine . . . but not the grunt that we received from the al di la staff .\n->mistakes happen , but they are usually accompanied by an apology , perhaps even a glass of wine . . . but not the grunt that we received from the al di la staff .\n[{'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\n->", + "output": "{\"text\": \"and apparently amazon is experiencing a supply shortage because my replacement isn ' t expected until sometime between jan 11th to feb 5th !\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n->we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'tired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: and it was quick which is very important .\n->and it was quick which is very important .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'important', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the c302 is a great machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe c302 is a great machine .\n->", + "output": "{\"text\": \"the c302 is a great machine .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The buffet had a nice selection .\n->The buffet had a nice selection .\n[{'aspect': 'buffet', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'selection', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n->6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n[{'aspect': '4gb', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\ntext: as of today , they can not give me a date on the return of my chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas of today , they can not give me a date on the return of my chromebook .\n->", + "output": "{\"text\": \"as of today , they can not give me a date on the return of my chromebook .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the playstore its immature .\n->the playstore its immature .\n[{'aspect': 'playstore', 'opinion': 'immature', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: This place would be so much better served by being run by a group that actually understands customer service .\n->This place would be so much better served by being run by a group that actually understands customer service .\n[{'aspect': 'service', 'opinion': 'would be so much better', 'polarity': 'negative', 'category': 'NULL'}]\ntext: clearly , this is a company that can not handle repairs in a timely matter .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nclearly , this is a company that can not handle repairs in a timely matter .\n->", + "output": "{\"text\": \"clearly , this is a company that can not handle repairs in a timely matter .\", \"labels\": \"[{'aspect': 'company', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i should also mention that i got this laptop when it was 100 dollars off .\n->i should also mention that i got this laptop when it was 100 dollars off .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The place itself is beautiful the bar scene seems to be happening .\n->The place itself is beautiful the bar scene seems to be happening .\n[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar scene', 'opinion': 'happening', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is a dreadful little piece of machinery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a dreadful little piece of machinery .\n->", + "output": "{\"text\": \"this is a dreadful little piece of machinery .\", \"labels\": \"[{'aspect': 'machinery', 'opinion': 'dreadful', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n->and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n[{'aspect': 'backlit keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: With the theater 2 blocks away we had a delicious meal in a beautiful room .\n->With the theater 2 blocks away we had a delicious meal in a beautiful room .\n[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'room', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' ve had mine 10 months and the motherboard has crapped out twice already .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had mine 10 months and the motherboard has crapped out twice already .\n->", + "output": "{\"text\": \"i ' ve had mine 10 months and the motherboard has crapped out twice already .\", \"labels\": \"[{'aspect': 'motherboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MOTHERBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of my favorite things i own\n->one of my favorite things i own\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Ambiance and music funky , which I enjoy .\n->Ambiance and music funky , which I enjoy .\n[{'aspect': 'Ambiance', 'opinion': 'funky', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'funky', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: bought it for a xmas gift , dead in less than 3 months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought it for a xmas gift , dead in less than 3 months .\n->", + "output": "{\"text\": \"bought it for a xmas gift , dead in less than 3 months .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought the 4gb model which will hopefully last me a few years but it ' s nice and snappy now .\n->i bought the 4gb model which will hopefully last me a few years but it ' s nice and snappy now .\n[{'aspect': '4gb model', 'opinion': 'hopefully', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': '4gb model', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': '4gb model', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: He offers subpar service and has no personality .\n->He offers subpar service and has no personality .\n[{'aspect': 'service', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it was an amazing laptop , until the graphics card started rotting after a month .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was an amazing laptop , until the graphics card started rotting after a month .\n->", + "output": "{\"text\": \"it was an amazing laptop , until the graphics card started rotting after a month .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'graphics card', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have eaten at saul , many times , the food is always consistently , outrageously good .\n->i have eaten at saul , many times , the food is always consistently , outrageously good .\n[{'aspect': 'food', 'opinion': 'outrageously good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this place has the best chinese style bbq ribs in the city .\n->this place has the best chinese style bbq ribs in the city .\n[{'aspect': 'bbq ribs', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bbq ribs', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: edit : just shy of owning 2 for 9 months now and both are experiencing touch screen failure problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nedit : just shy of owning 2 for 9 months now and both are experiencing touch screen failure problems .\n->", + "output": "{\"text\": \"edit : just shy of owning 2 for 9 months now and both are experiencing touch screen failure problems .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just because it ' s cheap does not mean the portions are small or the food is nasty , it is great !\n->just because it ' s cheap does not mean the portions are small or the food is nasty , it is great !\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'nasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: the seafood dynamite is also otherworldly .\n->the seafood dynamite is also otherworldly .\n[{'aspect': 'seafood dynamite', 'opinion': 'otherworldly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\n->", + "output": "{\"text\": \"one of them keeps sliding the touchscreen randomly back and forth , and the other one disables a top portion of the screen randomly .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n->i bought this because i have a desk job overnight that usually has a lot of down time so i when i have the ability to i can open this up and catch up on my news , watch movies , and even play video games and the graphics card in it is amazing .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i loved this chromebook , it worked great for six months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved this chromebook , it worked great for six months .\n->", + "output": "{\"text\": \"i loved this chromebook , it worked great for six months .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's a great place to pick up a cheap lunch or dinner .\n->It 's a great place to pick up a cheap lunch or dinner .\n[{'aspect': 'lunch', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this made me realize that arm processors are not ready for desktop - class browsing .\n->this made me realize that arm processors are not ready for desktop - class browsing .\n[{'aspect': 'arm processors', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\ntext: now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n->", + "output": "{\"text\": \"now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: oh and this is a beautiful machine and the lid is amazing .\n->oh and this is a beautiful machine and the lid is amazing .\n[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'lid', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n->I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n[{'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the build quality is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is fantastic .\n->", + "output": "{\"text\": \"the build quality is fantastic .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do not buy this machine if you ' re hoping to run android apps .\n->do not buy this machine if you ' re hoping to run android apps .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: this place has ruined me for neighborhood sushi .\n->this place has ruined me for neighborhood sushi .\n[{'aspect': 'sushi', 'opinion': 'ruined', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the thing that made me return it was the trackpad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe thing that made me return it was the trackpad .\n->", + "output": "{\"text\": \"the thing that made me return it was the trackpad .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n->as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n[{'aspect': 'audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i have never been disappointed in the red eye .\n->i have never been disappointed in the red eye .\n[{'aspect': 'red eye', 'opinion': 'never been disappointed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the trackpad was very glitchy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe trackpad was very glitchy .\n->", + "output": "{\"text\": \"the trackpad was very glitchy .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'glitchy', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re coming from a pc you ' ll love the battery .\n->if you ' re coming from a pc you ' ll love the battery .\n[{'aspect': 'battery', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: My wife had the fried shrimp which are huge and loved it .\n->My wife had the fried shrimp which are huge and loved it .\n[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i had the trackpad speed maxed out and it still was bugging out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had the trackpad speed maxed out and it still was bugging out .\n->", + "output": "{\"text\": \"i had the trackpad speed maxed out and it still was bugging out .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n->The calamari comes with an incredible sauce , and the duck noodles are yummy as well .\n[{'aspect': 'sauce', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'duck noodles', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this was a splurge for myself , which i rarely do , and i ' m so disappointed that it turned out like this .\n->this was a splurge for myself , which i rarely do , and i ' m so disappointed that it turned out like this .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: overall i like the portability and battery life on this device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall i like the portability and battery life on this device .\n->", + "output": "{\"text\": \"overall i like the portability and battery life on this device .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'battery life', 'opinion': 'like', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would not recommend you buying this laptop , the specs are fine for the price , but the hardware is rubbish .\n->i would not recommend you buying this laptop , the specs are fine for the price , but the hardware is rubbish .\n[{'aspect': 'laptop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'specs', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'hardware', 'opinion': 'rubbish', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: if we were to move from the upper east side , we would genuinely miss this restaurant .\n->if we were to move from the upper east side , we would genuinely miss this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: my disappointment begins with the fact that chromeos is not touch - ready and most websites will not consider touch interface for desktop mode websites .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy disappointment begins with the fact that chromeos is not touch - ready and most websites will not consider touch interface for desktop mode websites .\n->", + "output": "{\"text\": \"my disappointment begins with the fact that chromeos is not touch - ready and most websites will not consider touch interface for desktop mode websites .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unable to contact asus support for help .\n->unable to contact asus support for help .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: LOVE the atmosphere - felt like I was in Paris .\n->LOVE the atmosphere - felt like I was in Paris .\n[{'aspect': 'atmosphere', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoogle ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\n->", + "output": "{\"text\": \"google ' s own services like google docs , drive , and gmail don ' t respond well to touch screen gestures often leaving me in despair .\", \"labels\": \"[{'aspect': \"google ' s own services\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it also has much better performance , including an easily upgraded m .\n->it also has much better performance , including an easily upgraded m .\n[{'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: we were worried we would have trouble getting in , but somehow managed to have a short wait .\n->we were worried we would have trouble getting in , but somehow managed to have a short wait .\n[{'aspect': 'wait', 'opinion': 'short', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: aside from the larger capacity , it hasn ' t lived up to some of the other hardware i ' ve used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \naside from the larger capacity , it hasn ' t lived up to some of the other hardware i ' ve used .\n->", + "output": "{\"text\": \"aside from the larger capacity , it hasn ' t lived up to some of the other hardware i ' ve used .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great laptop .\n->this is a great laptop .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Big Wong gets big Ups for a fine establishment .\n->Big Wong gets big Ups for a fine establishment .\n[{'aspect': 'Big Wong', 'opinion': 'big Ups', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Big Wong', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: \u2022 super thin and light weight\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n\u2022 super thin and light weight\n->", + "output": "{\"text\": \"\u2022 super thin and light weight\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the place has a nice fit - out , some attractive furnishings and , from what i could tell , a reasonable wine list ( i was given the food menu when i asked for the carte des vins )\n->the place has a nice fit - out , some attractive furnishings and , from what i could tell , a reasonable wine list ( i was given the food menu when i asked for the carte des vins )\n[{'aspect': 'fit - out', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'furnishings', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'wine list', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n->i could n ' t ignore the fact that she reach over the plate of one of my friends , who was in mid bite , to clear the table .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: \u2022 bright screen with good colors\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n\u2022 bright screen with good colors\n->", + "output": "{\"text\": \"\u2022 bright screen with good colors\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing more wonderful than the food ( which is exceptional ) is the service .\n->the only thing more wonderful than the food ( which is exceptional ) is the service .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i purchased this asus chromebook in may of 2018 and initially loved it .\n->i purchased this asus chromebook in may of 2018 and initially loved it .\n[{'aspect': 'asus chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: \u2022 occasionally hangs for 10 - 30 seconds with no response from keyboard or trackpad\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n\u2022 occasionally hangs for 10 - 30 seconds with no response from keyboard or trackpad\n->", + "output": "{\"text\": \"\u2022 occasionally hangs for 10 - 30 seconds with no response from keyboard or trackpad\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n->The decor is vibrant and eye-pleasing with several semi-private boths on the right side of the dining hall , which are great for a date .\n[{'aspect': 'decor', 'opinion': 'vibrant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'eye-pleasing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining hall', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The pizza is delicious and the proprietor is one of the nicest in NYC .\n->The pizza is delicious and the proprietor is one of the nicest in NYC .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i also decided that despite the advantages of the asus , i can not live with the sharp edges and middling volume .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni also decided that despite the advantages of the asus , i can not live with the sharp edges and middling volume .\n->", + "output": "{\"text\": \"i also decided that despite the advantages of the asus , i can not live with the sharp edges and middling volume .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: greatest thing i ' ve bought myself in a long time .\n->greatest thing i ' ve bought myself in a long time .\n[{'aspect': 'NULL', 'opinion': 'greatest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: They also have a back garden open in the summer - cute and French with outdoor seating - what more could you ask for ?\n->They also have a back garden open in the summer - cute and French with outdoor seating - what more could you ask for ?\n[{'aspect': 'back garden', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'back garden', 'opinion': 'French', 'polarity': 'positive', 'category': 'NULL'}]\ntext: at first i was totally stoked on this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat first i was totally stoked on this chromebook .\n->", + "output": "{\"text\": \"at first i was totally stoked on this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'stoked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n->the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n[{'aspect': '1tb included drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'processing power', 'opinion': 'great', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\ntext: aluminum outer body , usb c charging from either side of the chrombooke , backit keyboard , nice screen resolution and fast processor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \naluminum outer body , usb c charging from either side of the chrombooke , backit keyboard , nice screen resolution and fast processor .\n->", + "output": "{\"text\": \"aluminum outer body , usb c charging from either side of the chrombooke , backit keyboard , nice screen resolution and fast processor .\", \"labels\": \"[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been using this notebook for a month and i absolutely love it !\n->i have been using this notebook for a month and i absolutely love it !\n[{'aspect': 'notebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - love the trackpad .\n->- love the trackpad .\n[{'aspect': 'trackpad', 'opinion': 'trackpad', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: then about 3 months in , it quit charging from the supplied fast charger .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen about 3 months in , it quit charging from the supplied fast charger .\n->", + "output": "{\"text\": \"then about 3 months in , it quit charging from the supplied fast charger .\", \"labels\": \"[{'aspect': 'fast charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great display .\n->great display .\n[{'aspect': 'display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n->even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: i can plug it in and put it back to sleep and it will charge , but if i haven ' t woken it up it won ' t begin to charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can plug it in and put it back to sleep and it will charge , but if i haven ' t woken it up it won ' t begin to charge .\n->", + "output": "{\"text\": \"i can plug it in and put it back to sleep and it will charge , but if i haven ' t woken it up it won ' t begin to charge .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n->i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n[{'aspect': 'pizza', 'opinion': 'ashamed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: that is just incredible .\n->that is just incredible .\n[{'aspect': 'NULL', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the speaker crackling i can live with as i just need to logout and re - log in to fix it for about a week .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speaker crackling i can live with as i just need to logout and re - log in to fix it for about a week .\n->", + "output": "{\"text\": \"the speaker crackling i can live with as i just need to logout and re - log in to fix it for about a week .\", \"labels\": \"[{'aspect': 'speaker', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * track pad * - the trackpad is well done .\n->* track pad * - the trackpad is well done .\n[{'aspect': 'track pad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: Ask for Usha , the nicest bartender in manhattan .\n->Ask for Usha , the nicest bartender in manhattan .\n[{'aspect': 'Usha', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the charging issue i can live with as well , even though it is annoying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe charging issue i can live with as well , even though it is annoying .\n->", + "output": "{\"text\": \"the charging issue i can live with as well , even though it is annoying .\", \"labels\": \"[{'aspect': 'charging', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n->the have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if i must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: up until this point , asus chromebooks have been my favorite .\n->up until this point , asus chromebooks have been my favorite .\n[{'aspect': 'asus chromebooks', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i would look elsewhere for a convertible chrombook , this one just doesn ' t quite live up expectactions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would look elsewhere for a convertible chrombook , this one just doesn ' t quite live up expectactions .\n->", + "output": "{\"text\": \"i would look elsewhere for a convertible chrombook , this one just doesn ' t quite live up expectactions .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n->some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n[{'aspect': 'NULL', 'opinion': 'respectable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'builds', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard dock', 'opinion': 'superior', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n->we ate out in the back patio , which is worth it as it ' s cool and the music is hear well there .\n[{'aspect': 'back patio', 'opinion': 'worth', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'back patio', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'music', 'opinion': 'well', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the design is very cool .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe design is very cool .\n->", + "output": "{\"text\": \"the design is very cool .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: otherwise i really do love it\n->otherwise i really do love it\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the real problem i had with this place was the complete lack of service .\n->the real problem i had with this place was the complete lack of service .\n[{'aspect': 'service', 'opinion': 'lack of', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it has a good blend of functionality and performance at a great price point .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has a good blend of functionality and performance at a great price point .\n->", + "output": "{\"text\": \"it has a good blend of functionality and performance at a great price point .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love the fact that it can extend and be flat .\n->i love the fact that it can extend and be flat .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: so far , it all works well .\n->so far , it all works well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i especially loved the high ( er ) resolution display , compared to most other chromebooks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni especially loved the high ( er ) resolution display , compared to most other chromebooks .\n->", + "output": "{\"text\": \"i especially loved the high ( er ) resolution display , compared to most other chromebooks .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'loved', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it arrived as promised and was exactly as described .\n->it arrived as promised and was exactly as described .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\nExample:\ntext: - android apps work rather well , even if not perfectly yet .\n->- android apps work rather well , even if not perfectly yet .\n[{'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'not perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: wifi was pretty bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwifi was pretty bad .\n->", + "output": "{\"text\": \"wifi was pretty bad .\", \"labels\": \"[{'aspect': 'wifi was', 'opinion': 'bad', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ca n ' t believe that it was , but please put the bag down before delivering food !\n->i ca n ' t believe that it was , but please put the bag down before delivering food !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n->the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n[{'aspect': 'service', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: linux worked fairly well , and i was pretty pleased with it overall .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlinux worked fairly well , and i was pretty pleased with it overall .\n->", + "output": "{\"text\": \"linux worked fairly well , and i was pretty pleased with it overall .\", \"labels\": \"[{'aspect': 'linux', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portions are large and the servers always surprise us with a different starter .\n->The portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'surprise', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'starter', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great atmosphere\n->great atmosphere\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n->", + "output": "{\"text\": \"as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\", \"labels\": \"[{'aspect': 'audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The quality of food at this restaurant accompanied by fantastic live jazz makes this place a perfect 10 !\n->The quality of food at this restaurant accompanied by fantastic live jazz makes this place a perfect 10 !\n[{'aspect': 'quality of food', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'live jazz', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n->I fell in love with the egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork .\n[{'aspect': 'egg noodles in the beef broth with shrimp dumplings and slices of BBQ roast pork', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\n->", + "output": "{\"text\": \"chromeos did well on this device , but i didn ' t like the scaling it did at higher resolutions .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chromeos', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what a tragic mistake buying a computer with windows 10 !\n->what a tragic mistake buying a computer with windows 10 !\n[{'aspect': 'computer with windows 10', 'opinion': 'tragic', 'polarity': 'negative', 'category': 'OS#GENERAL'}, {'aspect': 'computer with windows 10', 'opinion': 'tragic', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: lenovo has not disappointed with their products .\n->lenovo has not disappointed with their products .\n[{'aspect': 'lenovo', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'products', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: but it lost the coil whine roulette - - badly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut it lost the coil whine roulette - - badly .\n->", + "output": "{\"text\": \"but it lost the coil whine roulette - - badly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'badly', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n->This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n[{'aspect': 'night scene', 'opinion': 'alive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spot', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' d say the only drawback might be the speakers .\n->i ' d say the only drawback might be the speakers .\n[{'aspect': 'speakers', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: long story short , i loved asus and have been buying them for years .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlong story short , i loved asus and have been buying them for years .\n->", + "output": "{\"text\": \"long story short , i loved asus and have been buying them for years .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'loved', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is great , and it ' s backlit !\n->the keyboard is great , and it ' s backlit !\n[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: the fingerprint sensor is a nice touch and the color and feel of the laptop material is also nice .\n->the fingerprint sensor is a nice touch and the color and feel of the laptop material is also nice .\n[{'aspect': 'fingerprint sensor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'laptop material', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i ' m in the us and asus wanted to charge me $ 60 cad for diagnosis only , and then said it would be an additional estimated $ 315 for repair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m in the us and asus wanted to charge me $ 60 cad for diagnosis only , and then said it would be an additional estimated $ 315 for repair .\n->", + "output": "{\"text\": \"i ' m in the us and asus wanted to charge me $ 60 cad for diagnosis only , and then said it would be an additional estimated $ 315 for repair .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speaker crackling i can live with as i just need to logout and re - log in to fix it for about a week .\n->the speaker crackling i can live with as i just need to logout and re - log in to fix it for about a week .\n[{'aspect': 'speaker', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\n->this is a nice restaurant if you are looking for a good place to host an intimate dinner meeting with business associates .\n[{'aspect': 'restaurant', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n->", + "output": "{\"text\": \"it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\", \"labels\": \"[{'aspect': 'customer service and support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: up until this point , asus chromebooks have been my favorite .\n->up until this point , asus chromebooks have been my favorite .\n[{'aspect': 'asus chromebooks', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the e15 has a bright , 1080p screen - text is extremely sharp .\n->the e15 has a bright , 1080p screen - text is extremely sharp .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: it just is missing a lot of other features that i wanted in a notebook\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit just is missing a lot of other features that i wanted in a notebook\n->", + "output": "{\"text\": \"it just is missing a lot of other features that i wanted in a notebook\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i started out with a bombay beer which was big enough for two .\n->i started out with a bombay beer which was big enough for two .\n[{'aspect': 'bombay beer', 'opinion': 'big', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: As always we had a great glass of wine while we waited .\n->As always we had a great glass of wine while we waited .\n[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: vlc media player from the chrome app store did not have the capability to play any media from my external ssd that was in a usb 3 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvlc media player from the chrome app store did not have the capability to play any media from my external ssd that was in a usb 3 .\n->", + "output": "{\"text\": \"vlc media player from the chrome app store did not have the capability to play any media from my external ssd that was in a usb 3 .\", \"labels\": \"[{'aspect': 'vlc media player', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a decent laptop no thanks to asus support .\n->this is a decent laptop no thanks to asus support .\n[{'aspect': 'laptop', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The appetizers are just OK and the main courses were decidedly subpar .\n->The appetizers are just OK and the main courses were decidedly subpar .\n[{'aspect': 'appetizers', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'main courses', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the machine is easy to use , snappy , and everything the reviewers say .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe machine is easy to use , snappy , and everything the reviewers say .\n->", + "output": "{\"text\": \"the machine is easy to use , snappy , and everything the reviewers say .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'machine', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just wanted to make it known my personal experiences with the device .\n->i just wanted to make it known my personal experiences with the device .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: great price - i always buy the warranty .\n->great price - i always buy the warranty .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'warranty', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'WARRANTY#GENERAL'}]\ntext: the main bad for this particular variant is that google is no longer going to support it with updates .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe main bad for this particular variant is that google is no longer going to support it with updates .\n->", + "output": "{\"text\": \"the main bad for this particular variant is that google is no longer going to support it with updates .\", \"labels\": \"[{'aspect': 'google', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Thai food is good .\n->The Thai food is good .\n[{'aspect': 'Thai food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Metrazur has a beautiful spot overlooking the main terminal .\n->Metrazur has a beautiful spot overlooking the main terminal .\n[{'aspect': 'spot', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so be forwarded , if you buy this , you are jumping right into a machine that won ' t get updates anymore ( which is something most chromebook owners want and a feature that makes them better than android and it ' s fragmented market ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso be forwarded , if you buy this , you are jumping right into a machine that won ' t get updates anymore ( which is something most chromebook owners want and a feature that makes them better than android and it ' s fragmented market ) .\n->", + "output": "{\"text\": \"so be forwarded , if you buy this , you are jumping right into a machine that won ' t get updates anymore ( which is something most chromebook owners want and a feature that makes them better than android and it ' s fragmented market ) .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was authentic .\n->The food was authentic .\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: fast , thin , great battery life .\n->fast , thin , great battery life .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: many apps won ' t download and work on it like they do on an ellipsis .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmany apps won ' t download and work on it like they do on an ellipsis .\n->", + "output": "{\"text\": \"many apps won ' t download and work on it like they do on an ellipsis .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n->this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the unit is sleek , nice and the keyboard feels tactily right .\n->the unit is sleek , nice and the keyboard feels tactily right .\n[{'aspect': 'unit', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'unit', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'right', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: asus has not responded to numerous request for an update of the status of repair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nasus has not responded to numerous request for an update of the status of repair .\n->", + "output": "{\"text\": \"asus has not responded to numerous request for an update of the status of repair .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , i feel this is the best laptop i ' ve ever purchased or used .\n->overall , i feel this is the best laptop i ' ve ever purchased or used .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The atmosphere was crowded but it was a great bistro-type vibe .\n->The atmosphere was crowded but it was a great bistro-type vibe .\n[{'aspect': 'bistro-type vibe', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: while under the warranty , asus sent me a fedex account with no instructions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile under the warranty , asus sent me a fedex account with no instructions .\n->", + "output": "{\"text\": \"while under the warranty , asus sent me a fedex account with no instructions .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery lasts me all day , it ' s big screen is easy on the eyes .\n->battery lasts me all day , it ' s big screen is easy on the eyes .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: mouse and wi - fi never functioned correctly .\n->mouse and wi - fi never functioned correctly .\n[{'aspect': 'mouse', 'opinion': 'never functioned correctly', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'wi - fi', 'opinion': 'never functioned correctly', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: but today i noticed it ' s cracking ( ref : pics ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut today i noticed it ' s cracking ( ref : pics ) .\n->", + "output": "{\"text\": \"but today i noticed it ' s cracking ( ref : pics ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cracking', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: absolutly horrible product , cheap components make for unreliable products , but when the nearest service location is 50 + miles away i wonder how much apple truly cares ?\n->absolutly horrible product , cheap components make for unreliable products , but when the nearest service location is 50 + miles away i wonder how much apple truly cares ?\n[{'aspect': 'product', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'components', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'products', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'service location', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: from the moment i opened it , i was thoroughly pleased .\n->from the moment i opened it , i was thoroughly pleased .\n[{'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i bought this as a gift and was incredibly disappointed as it did n ' t even turn on after the initial charging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this as a gift and was incredibly disappointed as it did n ' t even turn on after the initial charging .\n->", + "output": "{\"text\": \"i bought this as a gift and was incredibly disappointed as it did n ' t even turn on after the initial charging .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'getting a table', 'opinion': 'never had a problem', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n->this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n[{'aspect': 'performance', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'happy', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: very disappointed and i have n ' t even had the product for 12 hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery disappointed and i have n ' t even had the product for 12 hours .\n->", + "output": "{\"text\": \"very disappointed and i have n ' t even had the product for 12 hours .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n->the keyboard is a delight with large , nearly flush , keys set to just the right level of resistance and sensitivity .\n[{'aspect': 'keyboard', 'opinion': 'large', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the camera sucks .\n->the camera sucks .\n[{'aspect': 'camera', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n->", + "output": "{\"text\": \"i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only complaint that i took a star off for is that the edge you rest your arms on is not rounded off and it is uncomfortable to rest on them for while .\n->only complaint that i took a star off for is that the edge you rest your arms on is not rounded off and it is uncomfortable to rest on them for while .\n[{'aspect': 'edge', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'edge', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: the price was great during prime days .\n->the price was great during prime days .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the product came highly recommended .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe product came highly recommended .\n->", + "output": "{\"text\": \"the product came highly recommended .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n->I went to Areo on a Sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n[{'aspect': 'Areo', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n->and if you ' re concerned about the system , i would tell you that as a person who has never used a macbook before actually buying it , do not worry you will get used to it in no time as i did .\n[{'aspect': 'system', 'opinion': 'not worry', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n->", + "output": "{\"text\": \"this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'not an inexpensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service here was great , food was fantastic .\n->service here was great , food was fantastic .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: very easy to set up and light to carry .\n->very easy to set up and light to carry .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: yay amazon customer service they replaced it immediately for me no questions asked .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyay amazon customer service they replaced it immediately for me no questions asked .\n->", + "output": "{\"text\": \"yay amazon customer service they replaced it immediately for me no questions asked .\", \"labels\": \"[{'aspect': 'amazon customer service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n->its so fast just click and 1 to 2 seconds it shuffles through youtube , videos , web pages , any thing .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: loved it\n->loved it\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the second one arrived , i had it from end of may june , to september started to notice the a key was a bit unresponsive at times .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe second one arrived , i had it from end of may june , to september started to notice the a key was a bit unresponsive at times .\n->", + "output": "{\"text\": \"the second one arrived , i had it from end of may june , to september started to notice the a key was a bit unresponsive at times .\", \"labels\": \"[{'aspect': 'a key', 'opinion': 'unresponsive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n->well , i did n ' t find it there , and trust , i have told everyone i can think of about my experience .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i also bought a wireless mouse , which paired perfectly .\n->i also bought a wireless mouse , which paired perfectly .\n[{'aspect': 'wireless mouse', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: it gets rave reviews both on the ' net and amazon .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit gets rave reviews both on the ' net and amazon .\n->", + "output": "{\"text\": \"it gets rave reviews both on the ' net and amazon .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'rave', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were drawn into the belly dancing show that captivated the crowd .\n->we were drawn into the belly dancing show that captivated the crowd .\n[{'aspect': 'belly dancing show', 'opinion': 'captivated', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the fan blows like crazy and it makes so much noise .\n->the fan blows like crazy and it makes so much noise .\n[{'aspect': 'fan', 'opinion': 'crazy', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: in many ways , the computer has lived up to my expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin many ways , the computer has lived up to my expectations .\n->", + "output": "{\"text\": \"in many ways , the computer has lived up to my expectations .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\n->When you 're sitting in their main dining room ( which has a spectacular , hand-painted high ceiling ) you 'd never know there was a world outside .\n[{'aspect': 'main dining room', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceiling', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceiling', 'opinion': 'hand-painted high', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The portions are large and the servers always surprise us with a different starter .\n->The portions are large and the servers always surprise us with a different starter .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is the nicest chrome computer i have ever owned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is the nicest chrome computer i have ever owned .\n->", + "output": "{\"text\": \"it is the nicest chrome computer i have ever owned .\", \"labels\": \"[{'aspect': 'chrome computer', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything works great .\n->everything works great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i can not imagine a friendlier staff working in a restaurant .\n->i can not imagine a friendlier staff working in a restaurant .\n[{'aspect': 'staff', 'opinion': 'friendlier', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: it has one serious flaw though which is that the audio out can not drive headphones ( or earbuds ) adequately .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has one serious flaw though which is that the audio out can not drive headphones ( or earbuds ) adequately .\n->", + "output": "{\"text\": \"it has one serious flaw though which is that the audio out can not drive headphones ( or earbuds ) adequately .\", \"labels\": \"[{'aspect': 'audio out', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n->It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n[{'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The waitresses are nice -- also you can just get counter service sit .\n->The waitresses are nice -- also you can just get counter service sit .\n[{'aspect': 'waitresses', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: even with the volume turned up all the way , the sound is very low which means that you have a poor soundstage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven with the volume turned up all the way , the sound is very low which means that you have a poor soundstage .\n->", + "output": "{\"text\": \"even with the volume turned up all the way , the sound is very low which means that you have a poor soundstage .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it has a good blend of functionality and performance at a great price point .\n->it has a good blend of functionality and performance at a great price point .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: on that scale , it ' s a world - beater .\n->on that scale , it ' s a world - beater .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the one star is for warranty support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe one star is for warranty support .\n->", + "output": "{\"text\": \"the one star is for warranty support .\", \"labels\": \"[{'aspect': 'warranty support', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: What is even better , is that the prices are very affordable as well , and the food is really good .\n->What is even better , is that the prices are very affordable as well , and the food is really good .\n[{'aspect': 'prices', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Top spot in town for Vietnamese classics , better than places that cost a lot more .\n->Top spot in town for Vietnamese classics , better than places that cost a lot more .\n[{'aspect': 'Vietnamese classics', 'opinion': 'Top', 'polarity': 'positive', 'category': 'NULL'}]\ntext: after about 60 days use the power adapter / charger stopped working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter about 60 days use the power adapter / charger stopped working .\n->", + "output": "{\"text\": \"after about 60 days use the power adapter / charger stopped working .\", \"labels\": \"[{'aspect': 'power adapter / charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m still using chrome for almost all native google apps , however , since chrome tabs / apps simply work better / faster / easier than their android counterparts .\n->i ' m still using chrome for almost all native google apps , however , since chrome tabs / apps simply work better / faster / easier than their android counterparts .\n[{'aspect': 'chrome tabs / apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chrome tabs / apps', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'chrome tabs / apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}, {'aspect': 'chrome tabs / apps', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n->the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: - i love the chromebook overall\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- i love the chromebook overall\n->", + "output": "{\"text\": \"- i love the chromebook overall\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: issue summary : frequent crashing\n->issue summary : frequent crashing\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the mac works like a new one .\n->the mac works like a new one .\n[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: - it freezes up depending on the program you use and you have to restart it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- it freezes up depending on the program you use and you have to restart it .\n->", + "output": "{\"text\": \"- it freezes up depending on the program you use and you have to restart it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n->i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n[{'aspect': 'usb - c charger', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: - battery life is a bit short after some gaming\n->- battery life is a bit short after some gaming\n[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: - battery sucks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- battery sucks .\n->", + "output": "{\"text\": \"- battery sucks .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the drag and drop works poorly which is very annoying .\n->the drag and drop works poorly which is very annoying .\n[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n->chromebooks are extremely light , easy to type on and come with complete integration into google cloud services like drive , docs and spreadsheets .\n[{'aspect': 'chromebooks', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: - horrible customer service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- horrible customer service .\n->", + "output": "{\"text\": \"- horrible customer service .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the Tom Kha soup was pathetic .\n->And the Tom Kha soup was pathetic .\n[{'aspect': 'Tom Kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n->most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n[{'aspect': 'apps', 'opinion': 'ok', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: build quality is excellent ( was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuild quality is excellent ( was .\n->", + "output": "{\"text\": \"build quality is excellent ( was .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keep up the good work .\n->keep up the good work .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Please try the Filet Mignon , its just the most tender piece ever .\n->Please try the Filet Mignon , its just the most tender piece ever .\n[{'aspect': 'Filet Mignon', 'opinion': 'tender', 'polarity': 'positive', 'category': 'NULL'}]\ntext: online support from asus says they only repair not replace .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonline support from asus says they only repair not replace .\n->", + "output": "{\"text\": \"online support from asus says they only repair not replace .\", \"labels\": \"[{'aspect': 'online support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: reliable , fresh sushi\n->reliable , fresh sushi\n[{'aspect': 'sushi', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n->i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: this is my 3rd chromebook and it is , by far , the flakiest one i ' ve had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my 3rd chromebook and it is , by far , the flakiest one i ' ve had .\n->", + "output": "{\"text\": \"this is my 3rd chromebook and it is , by far , the flakiest one i ' ve had .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'flakiest', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fans can get loud .\n->fans can get loud .\n[{'aspect': 'fans', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is one of my favorite restaurants and it is not to be missed .\n->this is one of my favorite restaurants and it is not to be missed .\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the bluetooth on it drops connections on a regular basis and the wifi is slow , slow , slow to connect on a regular basis .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe bluetooth on it drops connections on a regular basis and the wifi is slow , slow , slow to connect on a regular basis .\n->", + "output": "{\"text\": \"the bluetooth on it drops connections on a regular basis and the wifi is slow , slow , slow to connect on a regular basis .\", \"labels\": \"[{'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'wifi', 'opinion': 'slow', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'wifi', 'opinion': 'slow', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the other one had the same issue after 9 months of use , wasn ' t used right away when purchased .\n->the other one had the same issue after 9 months of use , wasn ' t used right away when purchased .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: it came in a regular brown box and the power cord was a bit scratched .\n->it came in a regular brown box and the power cord was a bit scratched .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: the space bar on the keyboard is inconsistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe space bar on the keyboard is inconsistent .\n->", + "output": "{\"text\": \"the space bar on the keyboard is inconsistent .\", \"labels\": \"[{'aspect': 'space bar', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food !\n->great food !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: we had half / half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !\n->we had half / half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !\n[{'aspect': 'half / half pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: works great as a chromebook but chromebooks are still very limited for android compatibility , at least this one is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks great as a chromebook but chromebooks are still very limited for android compatibility , at least this one is .\n->", + "output": "{\"text\": \"works great as a chromebook but chromebooks are still very limited for android compatibility , at least this one is .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'limited', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i only used it work my college school work and light browsing .\n->i only used it work my college school work and light browsing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s pretty light , too , so it ' s easy to travel with .\n->it ' s pretty light , too , so it ' s easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: my main gripe is incompatibility with amazon prime videos and gogo .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy main gripe is incompatibility with amazon prime videos and gogo .\n->", + "output": "{\"text\": \"my main gripe is incompatibility with amazon prime videos and gogo .\", \"labels\": \"[{'aspect': 'amazon prime videos and gogo', 'opinion': 'gripe', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved this place ! !\n->i loved this place ! !\n[{'aspect': 'place', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it ' s a very fast laptop .\n->it ' s a very fast laptop .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: about half of my home automation apps also do not work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nabout half of my home automation apps also do not work .\n->", + "output": "{\"text\": \"about half of my home automation apps also do not work .\", \"labels\": \"[{'aspect': 'home automation apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my main complaint is the battery life , i see many positive reviews about the battery life .\n->my main complaint is the battery life , i see many positive reviews about the battery life .\n[{'aspect': 'battery life', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: out of the box , the asus c302ca is a powerhouse for stock chromeos usage .\n->out of the box , the asus c302ca is a powerhouse for stock chromeos usage .\n[{'aspect': 'asus c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it works well for internet browsing and e - mail but i was hoping for much more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit works well for internet browsing and e - mail but i was hoping for much more .\n->", + "output": "{\"text\": \"it works well for internet browsing and e - mail but i was hoping for much more .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try it !\n->try it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the entree was also very good .\n->the entree was also very good .\n[{'aspect': 'entree', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i also hate the square rather than rounded edges .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni also hate the square rather than rounded edges .\n->", + "output": "{\"text\": \"i also hate the square rather than rounded edges .\", \"labels\": \"[{'aspect': 'edges', 'opinion': 'hate', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 10 months in my battery will no longer charge .\n->10 months in my battery will no longer charge .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: pros - slim , lightweight laptop due to 8th gen core - i5 .\n->pros - slim , lightweight laptop due to 8th gen core - i5 .\n[{'aspect': 'laptop', 'opinion': 'pros', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '8th gen core - i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\ntext: this makes it uncomfortable holding it in tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis makes it uncomfortable holding it in tablet mode .\n->", + "output": "{\"text\": \"this makes it uncomfortable holding it in tablet mode .\", \"labels\": \"[{'aspect': 'tablet mode', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: unique apppetizers .\n->unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n->There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $ 8 !\n[{'aspect': 'Blue Point oysters', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'beer', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmaybe i just got a lemon , but i decided to return this product after having it for less than two hours .\n->", + "output": "{\"text\": \"maybe i just got a lemon , but i decided to return this product after having it for less than two hours .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pro is by far the best .\n->the pro is by far the best .\n[{'aspect': 'pro', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food was actually aweful .\n->the food was actually aweful .\n[{'aspect': 'food', 'opinion': 'aweful', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: as a computer , for typing and using internet in general , this computer is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas a computer , for typing and using internet in general , this computer is good .\n->", + "output": "{\"text\": \"as a computer , for typing and using internet in general , this computer is good .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent sashimi , and the millennium roll is beyond delicious .\n->excellent sashimi , and the millennium roll is beyond delicious .\n[{'aspect': 'sashimi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'millennium roll', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i bought this to save me money and now i wasted $ 700 to still have to go out and buy a new one .\n->i bought this to save me money and now i wasted $ 700 to still have to go out and buy a new one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: however , for using it as a tablet / computer with streaming , it seems to only work half the time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , for using it as a tablet / computer with streaming , it seems to only work half the time .\n->", + "output": "{\"text\": \"however , for using it as a tablet / computer with streaming , it seems to only work half the time .\", \"labels\": \"[{'aspect': 'tablet / computer with streaming', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: well . . . they can run but they ca n ' t hide .\n->well . . . they can run but they ca n ' t hide .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it is very overpriced and not very tasty .\n->it is very overpriced and not very tasty .\n[{'aspect': 'NULL', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: netflix and hulu seem to be working for the most part so far , but amazon prime and xfinity stream are both having issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnetflix and hulu seem to be working for the most part so far , but amazon prime and xfinity stream are both having issues .\n->", + "output": "{\"text\": \"netflix and hulu seem to be working for the most part so far , but amazon prime and xfinity stream are both having issues .\", \"labels\": \"[{'aspect': 'netflix', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'hulu seem', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'amazon prime', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'xfinity stream', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: liking it a lot .\n->liking it a lot .\n[{'aspect': 'NULL', 'opinion': 'liking', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n->i was using my whole $ 3k desktop on this little $ 250 laptop and it ran flawlessly !\n[{'aspect': 'laptop', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i have had the computer for four months , and my computer suddenly wo n ' t turn .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had the computer for four months , and my computer suddenly wo n ' t turn .\n->", + "output": "{\"text\": \"i have had the computer for four months , and my computer suddenly wo n ' t turn .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i absolutely love this place ! ! !\n->i absolutely love this place ! ! !\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n->it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\ntext: so i call asus customer support , and received some of the worst customer service ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso i call asus customer support , and received some of the worst customer service ever .\n->", + "output": "{\"text\": \"so i call asus customer support , and received some of the worst customer service ever .\", \"labels\": \"[{'aspect': 'asus customer support', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have to say that if this what makes it easier to get a saet a lunch -- I dont mind .\n->I have to say that if this what makes it easier to get a saet a lunch -- I dont mind .\n[{'aspect': 'lunch', 'opinion': 'easier', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: and the screen was changing like creazy .\n->and the screen was changing like creazy .\n[{'aspect': 'screen', 'opinion': 'creazy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the computer is great , but the charger is garbage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer is great , but the charger is garbage .\n->", + "output": "{\"text\": \"the computer is great , but the charger is garbage .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'charger', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will be going back very soon .\n->i will be going back very soon .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: as to my comment about the food , no apology or acknowledgment was made .\n->as to my comment about the food , no apology or acknowledgment was made .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it was terrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was terrible .\n->", + "output": "{\"text\": \"it was terrible .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as soon as i learned about c302 , i decided top bought it .\n->as soon as i learned about c302 , i decided top bought it .\n[{'aspect': 'c302', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i think biggest reason i like it is that it ' s sturdy and i don ' t mind throwing it in and out of my backpack whereas with a $ 1000 mb air i might be a whole lot more careful in how i handle it .\n->i think biggest reason i like it is that it ' s sturdy and i don ' t mind throwing it in and out of my backpack whereas with a $ 1000 mb air i might be a whole lot more careful in how i handle it .\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: so a for the computer , f for the charger , and a d for the customer support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso a for the computer , f for the charger , and a d for the customer support .\n->", + "output": "{\"text\": \"so a for the computer , f for the charger , and a d for the customer support .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'a', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'charger', 'opinion': 'f', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'customer support', 'opinion': 'd', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n->it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n[{'aspect': 'tablet', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: We wo n't go to this place again for a good meal .\n->We wo n't go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: very odd operating system .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery odd operating system .\n->", + "output": "{\"text\": \"very odd operating system .\", \"labels\": \"[{'aspect': 'operating system', 'opinion': 'odd', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: But the staff was so horrible to us .\n->But the staff was so horrible to us .\n[{'aspect': 'staff', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: great except came with a bad left hinge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat except came with a bad left hinge .\n->", + "output": "{\"text\": \"great except came with a bad left hinge .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hinge', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you have to ` ` increase contrast ` ` and ` ` reduce transparency ` ` in the accessibility options to make it perform ok , but with a ui that looks like s * * * .\n->you have to ` ` increase contrast ` ` and ` ` reduce transparency ` ` in the accessibility options to make it perform ok , but with a ui that looks like s * * * .\n[{'aspect': 'ui', 'opinion': 's * * *', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: i like it , good construction , can load android apps .\n->i like it , good construction , can load android apps .\n[{'aspect': 'construction', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: asus support seems to want to make this an accidental damage instead of warranty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nasus support seems to want to make this an accidental damage instead of warranty .\n->", + "output": "{\"text\": \"asus support seems to want to make this an accidental damage instead of warranty .\", \"labels\": \"[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only thing i ' d change would be the hard drive .\n->only thing i ' d change would be the hard drive .\n[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: this asus chromebook c302ca does not skip a beat .\n->this asus chromebook c302ca does not skip a beat .\n[{'aspect': 'asus chromebook c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i can ' t recommend purchasing this based on what happens if you need support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can ' t recommend purchasing this based on what happens if you need support .\n->", + "output": "{\"text\": \"i can ' t recommend purchasing this based on what happens if you need support .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': \"' t recommend\", 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , I think this place is a good hang out spot .\n->However , I think this place is a good hang out spot .\n[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: All I can say is $ 2 pints during happy hour and the some of the cheapest oysters you 'll find in the city , though the quality is some of the best .\n->All I can say is $ 2 pints during happy hour and the some of the cheapest oysters you 'll find in the city , though the quality is some of the best .\n[{'aspect': 'oysters', 'opinion': 'cheapest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the build quality , screen and keyboard are terrific .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality , screen and keyboard are terrific .\n->", + "output": "{\"text\": \"the build quality , screen and keyboard are terrific .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'terrific', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'terrific', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'terrific', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food , great decor , great service .\n->Great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: so easy to use and is not that slow like some say works just fine for casual use .\n->so easy to use and is not that slow like some say works just fine for casual use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'not that slow', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: unfortunately , the downfall for me are the speakers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunfortunately , the downfall for me are the speakers .\n->", + "output": "{\"text\": \"unfortunately , the downfall for me are the speakers .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the portion sizes here are huge , and the sushi is good .\n->the portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Until you realize that their five minutes is meaningless and your wait may be anywhere from two to twenty minutes it may be frustrating .\n->Until you realize that their five minutes is meaningless and your wait may be anywhere from two to twenty minutes it may be frustrating .\n[{'aspect': 'wait', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'NULL'}]\ntext: they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n->", + "output": "{\"text\": \"they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'and', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'with', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: prices are very good .\n->prices are very good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: it surfs the internet fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit surfs the internet fast .\n->", + "output": "{\"text\": \"it surfs the internet fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , if you want great food at a great price and do n ' t mind the decor , you ca n ' t beat this place .\n->however , if you want great food at a great price and do n ' t mind the decor , you ca n ' t beat this place .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'decor', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the surface texture of the case is a satiny paper - like touch , which is both clean and easy to wipe down , but also not so smooth that you have to be concerned with it slipping out of your hand when carrying it .\n->the surface texture of the case is a satiny paper - like touch , which is both clean and easy to wipe down , but also not so smooth that you have to be concerned with it slipping out of your hand when carrying it .\n[{'aspect': 'surface texture of the case', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'surface texture of the case', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: the touch screen broke four months after i purchased it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touch screen broke four months after i purchased it .\n->", + "output": "{\"text\": \"the touch screen broke four months after i purchased it .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'broke', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thius is a must for anyone who loves shabu - shabu .\n->thius is a must for anyone who loves shabu - shabu .\n[{'aspect': 'shabu - shabu', 'opinion': 'loves', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: outstanding laptop .\n->outstanding laptop .\n[{'aspect': 'laptop', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the key board and mouse pad are not very sensitive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe key board and mouse pad are not very sensitive .\n->", + "output": "{\"text\": \"the key board and mouse pad are not very sensitive .\", \"labels\": \"[{'aspect': 'key board', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mouse pad', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Given the incredible architecture surrounding it , this place has no character .\n->Given the incredible architecture surrounding it , this place has no character .\n[{'aspect': 'architecture', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'no character', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: - 3 months after purchase , the chromebook has issues with the screen flickering constantly and has been sent in to asus for repairs\n->- 3 months after purchase , the chromebook has issues with the screen flickering constantly and has been sent in to asus for repairs\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the sound and screen quality is low .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sound and screen quality is low .\n->", + "output": "{\"text\": \"the sound and screen quality is low .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'screen quality', 'opinion': 'low', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was authentic .\n->The food was authentic .\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I got an excellent piece of cheesecake and we had several other nice pastries .\n->I got an excellent piece of cheesecake and we had several other nice pastries .\n[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: disappointing battery life , even with light use i have to recharge every 4 - 5 hours ( at best ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndisappointing battery life , even with light use i have to recharge every 4 - 5 hours ( at best ) .\n->", + "output": "{\"text\": \"disappointing battery life , even with light use i have to recharge every 4 - 5 hours ( at best ) .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n->now i ' m really bummed that i have a very nice looking chromebook with a beautiful screen that is totally unusable .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'bummed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: relatively small screen with high resolution makes reading the screen difficult .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nrelatively small screen with high resolution makes reading the screen difficult .\n->", + "output": "{\"text\": \"relatively small screen with high resolution makes reading the screen difficult .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a good product based on my experience - i have used this for almost a whole month .\n->this is a good product based on my experience - i have used this for almost a whole month .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the pricing was too good to pass up and i ' ve really been pleased with the product .\n->the pricing was too good to pass up and i ' ve really been pleased with the product .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: android ` ` the computer will recognize your cellphone ` ` system never works .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nandroid ` ` the computer will recognize your cellphone ` ` system never works .\n->", + "output": "{\"text\": \"android ` ` the computer will recognize your cellphone ` ` system never works .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n->the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n[{'aspect': 'build quality', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'back lit keys', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: Nice atmosphere , the service was very pleasant and the desert was good .\n->Nice atmosphere , the service was very pleasant and the desert was good .\n[{'aspect': 'atmosphere', 'opinion': 'Nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'desert', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: otherwise , build quality is very good , starts quickly , very light .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \notherwise , build quality is very good , starts quickly , very light .\n->", + "output": "{\"text\": \"otherwise , build quality is very good , starts quickly , very light .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was wonderful .\n->it was wonderful .\n[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i also decided that despite the advantages of the asus , i can not live with the sharp edges and middling volume .\n->i also decided that despite the advantages of the asus , i can not live with the sharp edges and middling volume .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: when it works it ' s a great device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen it works it ' s a great device .\n->", + "output": "{\"text\": \"when it works it ' s a great device .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: upon accepting a graphics driver update , the whole laptop froze .\n->upon accepting a graphics driver update , the whole laptop froze .\n[{'aspect': 'laptop', 'opinion': 'froze', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food was good .\n->The food was good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: sadly after barely a year old it crashes all the time , the touch screen rarely works , and the track pad stops working until a reboot on occasion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsadly after barely a year old it crashes all the time , the touch screen rarely works , and the track pad stops working until a reboot on occasion .\n->", + "output": "{\"text\": \"sadly after barely a year old it crashes all the time , the touch screen rarely works , and the track pad stops working until a reboot on occasion .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sound sort of sucks but i don ' t use it for music .\n->sound sort of sucks but i don ' t use it for music .\n[{'aspect': 'sound', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: Food is great .\n->Food is great .\n[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: asus support is responsive but ineffective .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nasus support is responsive but ineffective .\n->", + "output": "{\"text\": \"asus support is responsive but ineffective .\", \"labels\": \"[{'aspect': 'asus support', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus support', 'opinion': 'ineffective', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as far as gaming performance , the m370x does quite well .\n->as far as gaming performance , the m370x does quite well .\n[{'aspect': 'm370x', 'opinion': 'well', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The flavors are great , and the menu is extensive .\n->The flavors are great , and the menu is extensive .\n[{'aspect': 'flavors', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i will never buy another asus after this experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will never buy another asus after this experience .\n->", + "output": "{\"text\": \"i will never buy another asus after this experience .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff offers impeccable service .\n->the staff offers impeccable service .\n[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: we did n ' t look like the other patrons in there so unfortunately i think that may have been part of the problem .\n->we did n ' t look like the other patrons in there so unfortunately i think that may have been part of the problem .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n->", + "output": "{\"text\": \"i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n->However , if you want great food at a great price and do n't mind the decor , you ca n't beat this place .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': \"do n't mind\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: great bagels made the old - fashioned way .\n->great bagels made the old - fashioned way .\n[{'aspect': 'bagels', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: it ' s basically a useless brick , with shoddy hardware .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s basically a useless brick , with shoddy hardware .\n->", + "output": "{\"text\": \"it ' s basically a useless brick , with shoddy hardware .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'shoddy', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - display glass not glued well on one side .\n->- display glass not glued well on one side .\n[{'aspect': 'display glass', 'opinion': 'not glued well', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: good creative rolls !\n->good creative rolls !\n[{'aspect': 'rolls', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'creative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the computer is nice , blah blah it has nice features but it stops working after a few months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer is nice , blah blah it has nice features but it stops working after a few months .\n->", + "output": "{\"text\": \"the computer is nice , blah blah it has nice features but it stops working after a few months .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chromebooks are a waste of time / money .\n->chromebooks are a waste of time / money .\n[{'aspect': 'chromebooks', 'opinion': 'waste', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i used this laptop for 2 months without upgrading it .\n->i used this laptop for 2 months without upgrading it .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: don ' t let them scam you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndon ' t let them scam you .\n->", + "output": "{\"text\": \"don ' t let them scam you .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'scam', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everythig about it - especially the shows and actors .\n->i loved everythig about it - especially the shows and actors .\n[{'aspect': 'shows', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'actors', 'opinion': 'loved', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n->replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: oh and the customer service is garbage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noh and the customer service is garbage .\n->", + "output": "{\"text\": \"oh and the customer service is garbage .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'garbage', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google play support is lip service only .\n->google play support is lip service only .\n[{'aspect': 'google play support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: the mbp trackpad is best in class and if you are not using a mouse , this makes a huge difference .\n->the mbp trackpad is best in class and if you are not using a mouse , this makes a huge difference .\n[{'aspect': 'mbp trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: great little machine while it was functioning 100 % .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat little machine while it was functioning 100 % .\n->", + "output": "{\"text\": \"great little machine while it was functioning 100 % .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food\n->great food\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: love it .\n->love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: very unsatisfied with warranty service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery unsatisfied with warranty service .\n->", + "output": "{\"text\": \"very unsatisfied with warranty service .\", \"labels\": \"[{'aspect': 'warranty service', 'opinion': 'unsatisfied', 'polarity': 'negative', 'category': 'WARRANTY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n->Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n->downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n[{'aspect': 'appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: my shiny speedy asus chromebook froze after only one month of use and i am returning it today for a full refund .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy shiny speedy asus chromebook froze after only one month of use and i am returning it today for a full refund .\n->", + "output": "{\"text\": \"my shiny speedy asus chromebook froze after only one month of use and i am returning it today for a full refund .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , do n ' t plan on asking for your favorite roll , if it ' s not on the menu , you ca n ' t have it .\n->also , do n ' t plan on asking for your favorite roll , if it ' s not on the menu , you ca n ' t have it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i ' m a programmer and it can run all programs perfectly without delay and i don ' t have to worry about it .\n->i ' m a programmer and it can run all programs perfectly without delay and i don ' t have to worry about it .\n[{'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: cursor won ' t move and touchscreen won ' t work and from reading about this online i am not alone with this issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncursor won ' t move and touchscreen won ' t work and from reading about this online i am not alone with this issue .\n->", + "output": "{\"text\": \"cursor won ' t move and touchscreen won ' t work and from reading about this online i am not alone with this issue .\", \"labels\": \"[{'aspect': 'cursor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: it feels very nice and i also really like the backlit keys in the dark .\n->it feels very nice and i also really like the backlit keys in the dark .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'backlit keys', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: product worked great until it randomly stopped charging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nproduct worked great until it randomly stopped charging .\n->", + "output": "{\"text\": \"product worked great until it randomly stopped charging .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: usb ports are too hard to plug and unplug\n->usb ports are too hard to plug and unplug\n[{'aspect': 'usb ports', 'opinion': 'hard', 'polarity': 'negative', 'category': 'PORTS#USABILITY'}]\nExample:\ntext: love this took place of my laptop .\n->love this took place of my laptop .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: extremely disappointed as this was a gift to my husband .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nextremely disappointed as this was a gift to my husband .\n->", + "output": "{\"text\": \"extremely disappointed as this was a gift to my husband .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: be prepared to wait , because the place is pretty tiny .\n->be prepared to wait , because the place is pretty tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: beware this product seems to have no quality control .\n->beware this product seems to have no quality control .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i bought this chrome book based on the high ratings , however i feel the quality just is n ' t up to par for the price i paid .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this chrome book based on the high ratings , however i feel the quality just is n ' t up to par for the price i paid .\n->", + "output": "{\"text\": \"i bought this chrome book based on the high ratings , however i feel the quality just is n ' t up to par for the price i paid .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great chromebook .\n->great chromebook .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n->The cuisine from what I 've gathered is authentic Taiwanese , though its very different from what I 've been accustomed to in Taipei .\n[{'aspect': 'cuisine', 'opinion': 'different', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: today , it stopped powering on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntoday , it stopped powering on .\n->", + "output": "{\"text\": \"today , it stopped powering on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is outstanding and my crab-cake eggs benedict could not have been better .\n->The service is outstanding and my crab-cake eggs benedict could not have been better .\n[{'aspect': 'service', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab-cake eggs benedict', 'opinion': 'could not have been better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: linux worked fairly well , and i was pretty pleased with it overall .\n->linux worked fairly well , and i was pretty pleased with it overall .\n[{'aspect': 'linux', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\n->", + "output": "{\"text\": \"i ' ve been chatting with asus support for the past half hour and they make the sloth from zootopia look speedy .\", \"labels\": \"[{'aspect': 'asus support', 'opinion': 'sloth', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the side dishes were passable , and i did get a refill upon request .\n->the side dishes were passable , and i did get a refill upon request .\n[{'aspect': 'side dishes', 'opinion': 'passable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Normally that would be improper , however they were all delicious and my host did not complain .\n->Normally that would be improper , however they were all delicious and my host did not complain .\n[{'aspect': 'host', 'opinion': 'delicious', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso sad to report that after only a few weeks my asus chromebook has a hairline fracture .\n->", + "output": "{\"text\": \"so sad to report that after only a few weeks my asus chromebook has a hairline fracture .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the hinges are just perfect .\n->- the hinges are just perfect .\n[{'aspect': 'hinges', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: when it does run it runs great .\n->when it does run it runs great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the touch screen never seemed to work properly and now i understand why .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touch screen never seemed to work properly and now i understand why .\n->", + "output": "{\"text\": \"the touch screen never seemed to work properly and now i understand why .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: although we were looking for regular lettuce and some walnuts the salads we got were great .\n->although we were looking for regular lettuce and some walnuts the salads we got were great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n->the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n[{'aspect': 'restaurant', 'opinion': 'family feel', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'portions', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'veal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: this one is pretty , but obviously not sturdy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis one is pretty , but obviously not sturdy .\n->", + "output": "{\"text\": \"this one is pretty , but obviously not sturdy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not sturdy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazing laptop !\n->amazing laptop !\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The pizza is yummy and I like the atmoshpere .\n->The pizza is yummy and I like the atmoshpere .\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this was a splurge for myself , which i rarely do , and i ' m so disappointed that it turned out like this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was a splurge for myself , which i rarely do , and i ' m so disappointed that it turned out like this .\n->", + "output": "{\"text\": \"this was a splurge for myself , which i rarely do , and i ' m so disappointed that it turned out like this .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - power button next to delete button ?\n->- power button next to delete button ?\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n->i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: this is a great machine in many ways - - and with crouton could run full ubuntu , which made it a great little machine to write code on and deploy to a server .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great machine in many ways - - and with crouton could run full ubuntu , which made it a great little machine to write code on and deploy to a server .\n->", + "output": "{\"text\": \"this is a great machine in many ways - - and with crouton could run full ubuntu , which made it a great little machine to write code on and deploy to a server .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: that machine couldn ' t handle all the tabs i wanted to keep open , so after much research , i jumped into this acer .\n->that machine couldn ' t handle all the tabs i wanted to keep open , so after much research , i jumped into this acer .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: another problem i had was when i awakened my computer from sleeping , the wifi would not work .\n->another problem i had was when i awakened my computer from sleeping , the wifi would not work .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: nice screen , nice feel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice screen , nice feel .\n->", + "output": "{\"text\": \"nice screen , nice feel .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try it !\n->try it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: unhygienic\n->unhygienic\n[{'aspect': 'NULL', 'opinion': 'unhygienic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i love the way it looks and feels the only thing i can complain about is the fact that none of these electronics come with printed manuels anymore also dont know anything about the camera only see me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love the way it looks and feels the only thing i can complain about is the fact that none of these electronics come with printed manuels anymore also dont know anything about the camera only see me .\n->", + "output": "{\"text\": \"i love the way it looks and feels the only thing i can complain about is the fact that none of these electronics come with printed manuels anymore also dont know anything about the camera only see me .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'camera', 'opinion': 'complain', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n->this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n[{'aspect': 'chromebook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: stable , long battery life , and great build .\n->stable , long battery life , and great build .\n[{'aspect': 'battery life', 'opinion': 'stable', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: overall this chromebook worked well and was reliable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall this chromebook worked well and was reliable .\n->", + "output": "{\"text\": \"overall this chromebook worked well and was reliable .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: taking hot dogs to the next level\n->taking hot dogs to the next level\n[{'aspect': 'hot dogs', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The service is awful .\n->The service is awful .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the wifi card recently died after 14 months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe wifi card recently died after 14 months .\n->", + "output": "{\"text\": \"the wifi card recently died after 14 months .\", \"labels\": \"[{'aspect': 'wifi card', 'opinion': 'died', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ended the dinner with a surprisingly light and flaky apple tarte tatin .\n->We ended the dinner with a surprisingly light and flaky apple tarte tatin .\n[{'aspect': 'apple tarte tatin', 'opinion': 'flaky', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n->Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'raw vegatables in side orders', 'opinion': 'wondered about freshmess', 'polarity': 'negative', 'category': 'NULL'}]\ntext: now , my wifi connectivity goes up and down with regularity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow , my wifi connectivity goes up and down with regularity .\n->", + "output": "{\"text\": \"now , my wifi connectivity goes up and down with regularity .\", \"labels\": \"[{'aspect': 'wifi connectivity', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bagels are ok , but be sure not to make any special requests !\n->bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: 6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n->6 - inch hd touch , intel celeron n3150 , 4gb ddr3l , 32gb , chrome , cb5 - 132t - c1lk\n[{'aspect': 'hd touch', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'intel celeron n3150', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'CPU#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#GENERAL'}, {'aspect': 'cb5 - 132t - c1lk', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: not super confident this asus unit will last half that long .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot super confident this asus unit will last half that long .\n->", + "output": "{\"text\": \"not super confident this asus unit will last half that long .\", \"labels\": \"[{'aspect': 'asus unit', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - trackpad is too finicky and not my favorite\n->- trackpad is too finicky and not my favorite\n[{'aspect': 'trackpad', 'opinion': 'finicky', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: amazing !\n->amazing !\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it ' s not worth what i paid for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s not worth what i paid for it .\n->", + "output": "{\"text\": \"it ' s not worth what i paid for it .\", \"labels\": \"[{'aspect': 'it', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n->i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n[{'aspect': 'acer monitors', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: one of my favorite places in manhattan .\n->one of my favorite places in manhattan .\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i will never buy an apple product again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will never buy an apple product again .\n->", + "output": "{\"text\": \"i will never buy an apple product again .\", \"labels\": \"[{'aspect': 'apple product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff there is very attentive and down to earth .\n->The staff there is very attentive and down to earth .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: youtube works well on here .\n->youtube works well on here .\n[{'aspect': 'youtube', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: beautiful machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeautiful machine .\n->", + "output": "{\"text\": \"beautiful machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The production is a symphony , alot of fun to experience.The food sublime for the most part .\n->The production is a symphony , alot of fun to experience.The food sublime for the most part .\n[{'aspect': 'food', 'opinion': 'sublime', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: there was a small wait , but shorter than i expected .\n->there was a small wait , but shorter than i expected .\n[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: app game ( simpsons tapped out ) ( i know don ' t judge ) lags at every moment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napp game ( simpsons tapped out ) ( i know don ' t judge ) lags at every moment .\n->", + "output": "{\"text\": \"app game ( simpsons tapped out ) ( i know don ' t judge ) lags at every moment .\", \"labels\": \"[{'aspect': 'app game', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n->i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: this place has great indian chinese food .\n->this place has great indian chinese food .\n[{'aspect': 'indian chinese food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: chromebook is 2 months old and charger stopped working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchromebook is 2 months old and charger stopped working .\n->", + "output": "{\"text\": \"chromebook is 2 months old and charger stopped working .\", \"labels\": \"[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had a huge pastrami sandwich on a roll .\n->I had a huge pastrami sandwich on a roll .\n[{'aspect': 'pastrami sandwich', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: called customer service twice only to learn that i need to return old charger ( i pay $ 15 to return ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncalled customer service twice only to learn that i need to return old charger ( i pay $ 15 to return ) .\n->", + "output": "{\"text\": \"called customer service twice only to learn that i need to return old charger ( i pay $ 15 to return ) .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I LOVE their Thai\n->I LOVE their Thai\n[{'aspect': 'Thai', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n->Over the years the host , Vittorio , and his crew , have always treated me as family -- although with all the business this not-so-little gem does , it amazing he 's even able to remember a consistent but not-so-frequent visitor .\n[{'aspect': 'crew', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'host', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: there is a buzzing sound that comes from inside of the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is a buzzing sound that comes from inside of the keyboard .\n->", + "output": "{\"text\": \"there is a buzzing sound that comes from inside of the keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: beautiful machine .\n->beautiful machine .\n[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: baluchi ' s has solid food and a nice decor at reasonable prices .\n->baluchi ' s has solid food and a nice decor at reasonable prices .\n[{'aspect': \"baluchi ' s\", 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: excellent product , feels like quality all the way around .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent product , feels like quality all the way around .\n->", + "output": "{\"text\": \"excellent product , feels like quality all the way around .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: beware this product seems to have no quality control .\n->beware this product seems to have no quality control .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: overall , worst chromebook ever and i can ' t wait until it dies !\n->overall , worst chromebook ever and i can ' t wait until it dies !\n[{'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nproduct keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n->", + "output": "{\"text\": \"product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n->However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n[{'aspect': 'management', 'opinion': 'changed', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'door', 'opinion': 'great big', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i recommend the thai popcorn : )\n->i recommend the thai popcorn : )\n[{'aspect': 'thai popcorn', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the chromebook seems to be working fine now and my daughter does love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook seems to be working fine now and my daughter does love it .\n->", + "output": "{\"text\": \"the chromebook seems to be working fine now and my daughter does love it .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely the best chromebook out there .\n->definitely the best chromebook out there .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i love this laptop !\n->i love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this is exactly what i needed in a laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is exactly what i needed in a laptop .\n->", + "output": "{\"text\": \"this is exactly what i needed in a laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then it would not boot up .\n->then it would not boot up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s completely quiet , no heat whatsoever , and very fast !\n->it ' s completely quiet , no heat whatsoever , and very fast !\n[{'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: however , the speakers are already starting to crackle , and i haven ' t even had the thing for a week .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , the speakers are already starting to crackle , and i haven ' t even had the thing for a week .\n->", + "output": "{\"text\": \"however , the speakers are already starting to crackle , and i haven ' t even had the thing for a week .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard feels nice to use , the keys have a satisfying travel .\n->the keyboard feels nice to use , the keys have a satisfying travel .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keys', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: good , fast service .\n->good , fast service .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\n->", + "output": "{\"text\": \"i still love the laptop , i ' m just a bit disappointed by this and i hope it ' s the only issue i see .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I think the stuff was better than Disney .\n->I think the stuff was better than Disney .\n[{'aspect': 'stuff', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the battery is far below what i expected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery is far below what i expected .\n->", + "output": "{\"text\": \"the battery is far below what i expected .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Mizu is home to creative and unique rolls not to found anywhere else .\n->Mizu is home to creative and unique rolls not to found anywhere else .\n[{'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n->One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n[{'aspect': 'menu', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this computer seemed very exciting but after having troubles with 3 of them i give up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer seemed very exciting but after having troubles with 3 of them i give up .\n->", + "output": "{\"text\": \"this computer seemed very exciting but after having troubles with 3 of them i give up .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'exciting', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n->i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: on a recent sunday afternoon , a friend and i accidently found this great restaurant on our way to see the pulitzer prize winning play doubt .\n->on a recent sunday afternoon , a friend and i accidently found this great restaurant on our way to see the pulitzer prize winning play doubt .\n[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: moreover i ' m quite upset because it seems amazon will not pay me back the shipping fees , which for me amount to about 100 $ as i live in france .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmoreover i ' m quite upset because it seems amazon will not pay me back the shipping fees , which for me amount to about 100 $ as i live in france .\n->", + "output": "{\"text\": \"moreover i ' m quite upset because it seems amazon will not pay me back the shipping fees , which for me amount to about 100 $ as i live in france .\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n->what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Not the typical NYC gimmick theme restaurant .\n->Not the typical NYC gimmick theme restaurant .\n[{'aspect': 'restaurant', 'opinion': 'Not the typical', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the track pad has now stopped working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe track pad has now stopped working .\n->", + "output": "{\"text\": \"the track pad has now stopped working .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while the device is awesome , and there are lots of other reviews that will tell you exactly how awesome , i am just so impressed by how amazon ' s customer service handled the issue .\n->while the device is awesome , and there are lots of other reviews that will tell you exactly how awesome , i am just so impressed by how amazon ' s customer service handled the issue .\n[{'aspect': 'device', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': \"amazon ' s customer service\", 'opinion': 'impressed', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is my only device with this issue in my home .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is my only device with this issue in my home .\n->", + "output": "{\"text\": \"it is my only device with this issue in my home .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it does n ' t look appetizing as it ' s covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\n->it does n ' t look appetizing as it ' s covered in squid ink and it turns your lips and teeth black , but the taste was phenomenal .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: nice keyboard\n->nice keyboard\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: i think it works well except for the wifi which is spotty .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think it works well except for the wifi which is spotty .\n->", + "output": "{\"text\": \"i think it works well except for the wifi which is spotty .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'wifi', 'opinion': 'spotty', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: As much as I like the food there , I ca n't bring myself to go back .\n->As much as I like the food there , I ca n't bring myself to go back .\n[{'aspect': 'food', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n->We a menu that rarely changes , e xcept for one or two specials , the quality and care they put in thier food in evident .\n[{'aspect': 'quality', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'care', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it keeps disconnecting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit keeps disconnecting .\n->", + "output": "{\"text\": \"it keeps disconnecting .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the design is very cool .\n->the design is very cool .\n[{'aspect': 'design', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: had no flavor and the staff is rude and not attentive .\n->had no flavor and the staff is rude and not attentive .\n[{'aspect': 'staff', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'not attentive', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'no flavor', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: in total , it took 4 updates to access the google play store - - completely unacceptable !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin total , it took 4 updates to access the google play store - - completely unacceptable !\n->", + "output": "{\"text\": \"in total , it took 4 updates to access the google play store - - completely unacceptable !\", \"labels\": \"[{'aspect': 'google play store', 'opinion': 'unacceptable', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was very good - prompt , attentive and non-intrusive .\n->Service was very good - prompt , attentive and non-intrusive .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->we ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: up until this point , asus chromebooks have been my favorite .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nup until this point , asus chromebooks have been my favorite .\n->", + "output": "{\"text\": \"up until this point , asus chromebooks have been my favorite .\", \"labels\": \"[{'aspect': 'asus chromebooks', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n->i ' m a friendly person , so i would n ' t mind had she not been so nasty and gotten so personal .\n[{'aspect': 'NULL', 'opinion': 'nasty', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: and where does patis go wrong ; no where .\n->and where does patis go wrong ; no where .\n[{'aspect': 'patis', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i chose this device because of its power , app store , and a few other features trading off the option for an onboard stylus and improved screen resolution .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni chose this device because of its power , app store , and a few other features trading off the option for an onboard stylus and improved screen resolution .\n->", + "output": "{\"text\": \"i chose this device because of its power , app store , and a few other features trading off the option for an onboard stylus and improved screen resolution .\", \"labels\": \"[{'aspect': 'power', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'app store', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'screen resolution', 'opinion': 'improved', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mousepad was not very responsive .\n->the mousepad was not very responsive .\n[{'aspect': 'mousepad', 'opinion': 'not very responsive', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: The fried rice is amazing here .\n->The fried rice is amazing here .\n[{'aspect': 'fried rice', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it also has a great backlit keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit also has a great backlit keyboard .\n->", + "output": "{\"text\": \"it also has a great backlit keyboard .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Really cool stauff inside .\n->Really cool stauff inside .\n[{'aspect': 'stauff', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n->But the best part about LS is the late night atmosphere , delightfully free of the BTs .\n[{'aspect': 'late night atmosphere', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love how i can charge my laptop on both sides of the device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love how i can charge my laptop on both sides of the device .\n->", + "output": "{\"text\": \"i love how i can charge my laptop on both sides of the device .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n->i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n[{'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: overall , i am very happy with this purchase , and i am in love with the simplicity of the google ecosystem .\n->overall , i am very happy with this purchase , and i am in love with the simplicity of the google ecosystem .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'google ecosystem', 'opinion': 'love', 'polarity': 'positive', 'category': 'Out_Of_Scope#USABILITY'}]\ntext: wifi sketchy to nonexistent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwifi sketchy to nonexistent .\n->", + "output": "{\"text\": \"wifi sketchy to nonexistent .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'sketchy', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no backlit keyboard is kinda a bummer but i digress .\n->no backlit keyboard is kinda a bummer but i digress .\n[{'aspect': 'backlit keyboard', 'opinion': 'bummer', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n->was n ' t going to share but i feel obligated . . . while sitting at the sushi bar dining we watched the chef accidentally drop a piece of unagi on the floor and upon retrieving it from the floor proceed to use the piece in the delivery order he was preparing .\n[{'aspect': 'chef', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i much suspect that google ' s half baked sustem is at fault .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni much suspect that google ' s half baked sustem is at fault .\n->", + "output": "{\"text\": \"i much suspect that google ' s half baked sustem is at fault .\", \"labels\": \"[{'aspect': \"google ' s half baked sustem\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the m3 processor is pretty good ( decent speedometer score ) .\n->the m3 processor is pretty good ( decent speedometer score ) .\n[{'aspect': 'm3 processor', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'm3 processor', 'opinion': 'decent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\nExample:\ntext: the keyboard is difficult to get used to due to the placement and spacing of the keys compared to a regular keyboard .\n->the keyboard is difficult to get used to due to the placement and spacing of the keys compared to a regular keyboard .\n[{'aspect': 'keyboard', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n->", + "output": "{\"text\": \"i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\", \"labels\": \"[{'aspect': 'look', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'speed', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just that it seems that the hard drive doesn ' t work properly .\n->just that it seems that the hard drive doesn ' t work properly .\n[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: The music is the best among all the Indian restaurants I have visited .\n->The music is the best among all the Indian restaurants I have visited .\n[{'aspect': 'music', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s great other than the weak speakers and the touchpad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s great other than the weak speakers and the touchpad .\n->", + "output": "{\"text\": \"it ' s great other than the weak speakers and the touchpad .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s also worth noting my unit came with a floating trackpad , with initial play before the actually click .\n->it ' s also worth noting my unit came with a floating trackpad , with initial play before the actually click .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i recommend the thai popcorn : )\n->i recommend the thai popcorn : )\n[{'aspect': 'thai popcorn', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: not only is the touchpad not great in use but it also feels poorly made .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only is the touchpad not great in use but it also feels poorly made .\n->", + "output": "{\"text\": \"not only is the touchpad not great in use but it also feels poorly made .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'not great', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n->this new generation macbook has a fast processor , great retina display , 13 inch high resolution screen , great compatibility and many more .\n[{'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'retina display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'compatibility', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: beware this product seems to have no quality control .\n->beware this product seems to have no quality control .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i have been happy with this purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been happy with this purchase .\n->", + "output": "{\"text\": \"i have been happy with this purchase .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: seating is always prompt , though the restaurant does fill up in the evening .\n->seating is always prompt , though the restaurant does fill up in the evening .\n[{'aspect': 'seating', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: gross food \u2013 wow -\n->gross food \u2013 wow -\n[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: 10 months in my battery will no longer charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n10 months in my battery will no longer charge .\n->", + "output": "{\"text\": \"10 months in my battery will no longer charge .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n->to be completely fair , the only redeeming factor was the food , which was above average , but could n ' t make up for all the other deficiencies of teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'teodora', 'opinion': 'deficiencies', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The first time the sushi was outstanding , the second time it was a little bland .\n->The first time the sushi was outstanding , the second time it was a little bland .\n[{'aspect': 'sushi', 'opinion': 'outstanding', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the first week i had my chromebook it locked up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe first week i had my chromebook it locked up .\n->", + "output": "{\"text\": \"the first week i had my chromebook it locked up .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->the food is a diamond in rough - - the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'and', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'is', 'opinion': 'with', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the crust is thin , the ingredients are fresh and the staff is friendly .\n->the crust is thin , the ingredients are fresh and the staff is friendly .\n[{'aspect': 'crust', 'opinion': 'thin', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ingredients', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\n->", + "output": "{\"text\": \"bought this specific unit because quality is well reviewed here and online albeit at 2x average chrome book price .\", \"labels\": \"[{'aspect': 'specific unit', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'specific unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do n ' t think 16 gb is enough .\n->i do n ' t think 16 gb is enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\nExample:\ntext: this is the perfect date spot for williamsburg couples .\n->this is the perfect date spot for williamsburg couples .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: look and feel of asus unit seems high quality but keyboard failed in 45 days .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlook and feel of asus unit seems high quality but keyboard failed in 45 days .\n->", + "output": "{\"text\": \"look and feel of asus unit seems high quality but keyboard failed in 45 days .\", \"labels\": \"[{'aspect': 'asus unit', 'opinion': 'high', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'failed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is excellent .\n->The wine list is excellent .\n[{'aspect': 'wine list', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n->From beginning appetizers , the scallops were incredible , to the delicious chocolate souffle with rasberry mint sorbet , we were delighted by the taste sensations .\n[{'aspect': 'beginning appetizers', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'scallops', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chocolate souffle with rasberry mint sorbet', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'taste', 'opinion': 'delighted', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i opened the box after ordering on black friday , and the machine wouldn ' t charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni opened the box after ordering on black friday , and the machine wouldn ' t charge .\n->", + "output": "{\"text\": \"i opened the box after ordering on black friday , and the machine wouldn ' t charge .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pros : lightweight , fast , portable , great battery life ( 12 .\n->pros : lightweight , fast , portable , great battery life ( 12 .\n[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: it cost 8 dollars and shipping is not cheap .\n->it cost 8 dollars and shipping is not cheap .\n[{'aspect': 'shipping', 'opinion': 'not cheap', 'polarity': 'negative', 'category': 'SHIPPING#PRICE'}]\ntext: the battery stopped charging after 3 months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery stopped charging after 3 months .\n->", + "output": "{\"text\": \"the battery stopped charging after 3 months .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it starts up right away and has decent battery life that looks nice .\n->it starts up right away and has decent battery life that looks nice .\n[{'aspect': 'starts up', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'decent', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'nice', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: only problem i have had , which was from the moment i started using it is the audio .\n->only problem i have had , which was from the moment i started using it is the audio .\n[{'aspect': 'audio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: customer service is difficult .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncustomer service is difficult .\n->", + "output": "{\"text\": \"customer service is difficult .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'difficult', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hardware is still pretty sound .\n->hardware is still pretty sound .\n[{'aspect': 'hardware', 'opinion': 'sound', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: everything works fast and smooth .\n->everything works fast and smooth .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: also battery life is max 8 hours , not 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso battery life is max 8 hours , not 10 .\n->", + "output": "{\"text\": \"also battery life is max 8 hours , not 10 .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have used this laptop only for work and the battery lasts and hour at most on mid performance .\n->i have used this laptop only for work and the battery lasts and hour at most on mid performance .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n->i do n ' t usually visit the same establishment more than once , what more twice , but i ' ll come to zenkichi anytime for a quiet , unhurried and memorable dinner .\n[{'aspect': 'zenkichi', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'unhurried', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'zenkichi', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: however , the resolution could be higher , the scaling is bad , the colors are a bit washy , the weight is off as said earlier , the keys are kinda awkward , travel could be more , and the keys start squeaking soon after use\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , the resolution could be higher , the scaling is bad , the colors are a bit washy , the weight is off as said earlier , the keys are kinda awkward , travel could be more , and the keys start squeaking soon after use\n->", + "output": "{\"text\": \"however , the resolution could be higher , the scaling is bad , the colors are a bit washy , the weight is off as said earlier , the keys are kinda awkward , travel could be more , and the keys start squeaking soon after use\", \"labels\": \"[{'aspect': 'resolution', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'scaling', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'colors', 'opinion': 'washy', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keys', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after dinner the manager grabbed my boyfriend , asked him : where are you from . . . maybe you dont know how things work in america . . . and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .\n->after dinner the manager grabbed my boyfriend , asked him : where are you from . . . maybe you dont know how things work in america . . . and in the end stormed away almost teareyed yelling that tips are the only thing they survive on .\n[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i got this laptop 2 days ago and it says plugged in , not charged .\n->i got this laptop 2 days ago and it says plugged in , not charged .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: this product is built well , but it won ' t run the one android app i hoped it would run , and for whatever reason the app and os update download speeds are abysmal .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis product is built well , but it won ' t run the one android app i hoped it would run , and for whatever reason the app and os update download speeds are abysmal .\n->", + "output": "{\"text\": \"this product is built well , but it won ' t run the one android app i hoped it would run , and for whatever reason the app and os update download speeds are abysmal .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'app and os update download speeds', 'opinion': 'abysmal', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s exactly as i wanted it\n->it ' s exactly as i wanted it\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n->The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n[{'aspect': 'plain slice', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but , now i realize the design is flawed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut , now i realize the design is flawed .\n->", + "output": "{\"text\": \"but , now i realize the design is flawed .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'flawed', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it boots up instantaneously .\n->it boots up instantaneously .\n[{'aspect': 'boots up', 'opinion': 'instantaneously', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s beautiful and i love it , but i think i have to send it back .\n->it ' s beautiful and i love it , but i think i have to send it back .\n[{'aspect': 'NULL', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: and the worst customer service ever !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand the worst customer service ever !\n->", + "output": "{\"text\": \"and the worst customer service ever !\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: LOVE the atmosphere - felt like I was in Paris .\n->LOVE the atmosphere - felt like I was in Paris .\n[{'aspect': 'atmosphere', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this does exactly what i need , writing on google docs .\n->this does exactly what i need , writing on google docs .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\n->", + "output": "{\"text\": \"this laptop was supposed to be in ` ` like new ` ` condition but unfortunately , it was not .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m okay with that given that the body is thinner .\n->i ' m okay with that given that the body is thinner .\n[{'aspect': 'body', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Food was very good , but not what I would consider out of this world .\n->Food was very good , but not what I would consider out of this world .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: after updating the software i noticed that the was a lot of backlight bleeding from the display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter updating the software i noticed that the was a lot of backlight bleeding from the display .\n->", + "output": "{\"text\": \"after updating the software i noticed that the was a lot of backlight bleeding from the display .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hostess was very pleasant .\n->the hostess was very pleasant .\n[{'aspect': 'hostess', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: stepped on my foot on the second time he reached over me to adjust lighting .\n->stepped on my foot on the second time he reached over me to adjust lighting .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: upon further inspection , i had noticed that one of the side speakers was pushed in and the plastic surrounding it had a crack .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupon further inspection , i had noticed that one of the side speakers was pushed in and the plastic surrounding it had a crack .\n->", + "output": "{\"text\": \"upon further inspection , i had noticed that one of the side speakers was pushed in and the plastic surrounding it had a crack .\", \"labels\": \"[{'aspect': 'side speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - speakers can ` ` chatter ` ` after playing youtube videos for a long period of time .\n->- speakers can ` ` chatter ` ` after playing youtube videos for a long period of time .\n[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n->i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n[{'aspect': 'computer', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'computer', 'opinion': 'defective', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: only three months in , and the laptop won ' t charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly three months in , and the laptop won ' t charge .\n->", + "output": "{\"text\": \"only three months in , and the laptop won ' t charge .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list is interesting and has many good values .\n->the wine list is interesting and has many good values .\n[{'aspect': 'wine list', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine list', 'opinion': 'good values', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n->if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: something wrong with the battery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsomething wrong with the battery .\n->", + "output": "{\"text\": \"something wrong with the battery .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The rest of the menu is limited by everything is good eats .\n->The rest of the menu is limited by everything is good eats .\n[{'aspect': 'eats', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we wo n ' t go to this place again for a good meal .\n->we wo n ' t go to this place again for a good meal .\n[{'aspect': 'meal', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the keyboard broke after three months and it has been difficult to get any help from the manufacturer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard broke after three months and it has been difficult to get any help from the manufacturer .\n->", + "output": "{\"text\": \"the keyboard broke after three months and it has been difficult to get any help from the manufacturer .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'broke', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the wine list is interesting and has many good values .\n->the wine list is interesting and has many good values .\n[{'aspect': 'wine list', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine list', 'opinion': 'good values', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: the menu has so many fish items and oysters .\n->the menu has so many fish items and oysters .\n[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the second the screen did not rotate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe second the screen did not rotate .\n->", + "output": "{\"text\": \"the second the screen did not rotate .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve been waiting for this chromebook for some time now and having only had it for about two weeks , i can say that the device and purchase were well worth it .\n->i ' ve been waiting for this chromebook for some time now and having only had it for about two weeks , i can say that the device and purchase were well worth it .\n[{'aspect': 'chromebook', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the ssd is not work after 4 month\n->the ssd is not work after 4 month\n[{'aspect': 'ssd', 'opinion': 'not work', 'polarity': 'negative', 'category': 'HARD_DISC#QUALITY'}]\ntext: and the screen was changing like creazy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand the screen was changing like creazy .\n->", + "output": "{\"text\": \"and the screen was changing like creazy .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'creazy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n->- i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n[{'aspect': 'chromebooks', 'opinion': 'worried', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: worked great for a couple of months , but now the sound goes in and out after a couple minutes of use like watching a video or playing music .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworked great for a couple of months , but now the sound goes in and out after a couple minutes of use like watching a video or playing music .\n->", + "output": "{\"text\": \"worked great for a couple of months , but now the sound goes in and out after a couple minutes of use like watching a video or playing music .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you are indifferent about the screen , do not buy this !\n->if you are indifferent about the screen , do not buy this !\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n->We recently decided to try this location , and to our delight , they have outdoor seating , perfect since I had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but right out the box the battery will not charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut right out the box the battery will not charge .\n->", + "output": "{\"text\": \"but right out the box the battery will not charge .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's easy to get a table for a large group and you do n't get hustled out .\n->It 's easy to get a table for a large group and you do n't get hustled out .\n[{'aspect': 'table', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i must give it yon out of yon stars !\n->i must give it yon out of yon stars !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i loved this chromebook but i had to return it bevause it had sound issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved this chromebook but i had to return it bevause it had sound issues .\n->", + "output": "{\"text\": \"i loved this chromebook but i had to return it bevause it had sound issues .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: worst place on smith street in brooklyn\n->worst place on smith street in brooklyn\n[{'aspect': 'place', 'opinion': 'worst', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we both opted for a pasta dish and they were served timely and fresh .\n->we both opted for a pasta dish and they were served timely and fresh .\n[{'aspect': 'pasta dish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'served timely', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: this worked ok for about a year and then just totally died .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis worked ok for about a year and then just totally died .\n->", + "output": "{\"text\": \"this worked ok for about a year and then just totally died .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'died', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is good .\n->the keyboard is good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n->While the room is not particularly comfortable , once you 're seated you 'll forget about everything except what 's on your plate .\n[{'aspect': 'room', 'opinion': 'not particularly comfortable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: had the chromebook for 1month and 5 days and it stopped charging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhad the chromebook for 1month and 5 days and it stopped charging .\n->", + "output": "{\"text\": \"had the chromebook for 1month and 5 days and it stopped charging .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 1 ) the delete button is right next to the power button\n->1 ) the delete button is right next to the power button\n[{'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: Toons has recently been redone , so it 's now a very attractive space .\n->Toons has recently been redone , so it 's now a very attractive space .\n[{'aspect': 'Toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n->", + "output": "{\"text\": \"very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\", \"labels\": \"[{'aspect': 'sound volume', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a light - use business laptop that we ' ve had for a month .\n->this is a light - use business laptop that we ' ve had for a month .\n[{'aspect': 'laptop', 'opinion': 'light - use', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: i use my chromebook a lot in the dark and it ' s a real treat .\n->i use my chromebook a lot in the dark and it ' s a real treat .\n[{'aspect': 'chromebook', 'opinion': 'treat', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\n->", + "output": "{\"text\": \"on the higher end for chromebook , but it looked really nice and had a good features and reviews , was in love with it until the power failed last november .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power', 'opinion': 'failed', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: note that this laptop is only 45 daysish old .\n->note that this laptop is only 45 daysish old .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: thank you to amazon for taking this brick of acer garbage back .\n->thank you to amazon for taking this brick of acer garbage back .\n[{'aspect': 'amazon', 'opinion': 'thank you', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'acer garbage', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: a thoroughly disappointing machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na thoroughly disappointing machine .\n->", + "output": "{\"text\": \"a thoroughly disappointing machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m no expert on screens but i personally think the panel looks very nice .\n->i ' m no expert on screens but i personally think the panel looks very nice .\n[{'aspect': 'panel', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: while the product worked decently for about a month , it went downhill soon after .\n->while the product worked decently for about a month , it went downhill soon after .\n[{'aspect': 'product', 'opinion': 'decently', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: mouse and wi - fi never functioned correctly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmouse and wi - fi never functioned correctly .\n->", + "output": "{\"text\": \"mouse and wi - fi never functioned correctly .\", \"labels\": \"[{'aspect': 'mouse', 'opinion': 'never functioned correctly', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'wi - fi', 'opinion': 'never functioned correctly', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If it 's just a quick martini at the bar ( which I recommend Jeffery 's ) or a mind blowing Roast Chicken , go to Village !\n->If it 's just a quick martini at the bar ( which I recommend Jeffery 's ) or a mind blowing Roast Chicken , go to Village !\n[{'aspect': 'martini', 'opinion': 'quick', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Roast Chicken', 'opinion': 'mind blowing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We were looking forward to nice glass of Sangria when we arrived .\n->We were looking forward to nice glass of Sangria when we arrived .\n[{'aspect': 'glass of Sangria', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: machine periodically crashed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmachine periodically crashed .\n->", + "output": "{\"text\": \"machine periodically crashed .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n->returning it into a standard laptop position did not help , the screen actually turned dark and only closing and opening the lid helped return it to normal state .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the power _ supply is awesome .\n->the power _ supply is awesome .\n[{'aspect': 'power _ supply is', 'opinion': '.', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: after seven months , the usb - c ports stopped charging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter seven months , the usb - c ports stopped charging .\n->", + "output": "{\"text\": \"after seven months , the usb - c ports stopped charging .\", \"labels\": \"[{'aspect': 'usb - c ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am actually offended to have spent so much money on such a bad experience .\n->i am actually offended to have spent so much money on such a bad experience .\n[{'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'offended', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n->it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n[{'aspect': 'equipment', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'equipment', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: now the hole thing crashed out of nowhere and i ' m going to lose everything i had on it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow the hole thing crashed out of nowhere and i ' m going to lose everything i had on it .\n->", + "output": "{\"text\": \"now the hole thing crashed out of nowhere and i ' m going to lose everything i had on it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n->it stutters and pauses as you switch windows or use the ` ` show all windows ` ` or ` ` show desktop ` ` functions .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is one of the best purchases i have made in years .\n->this is one of the best purchases i have made in years .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: + nice , large screen\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n+ nice , large screen\n->", + "output": "{\"text\": \"+ nice , large screen\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but this asus c302ca has blown me away .\n->but this asus c302ca has blown me away .\n[{'aspect': 'asus c302ca', 'opinion': 'blown me away', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - even so those are forgivable offenses compared to the next and worst thing , the touchpad .\n->- even so those are forgivable offenses compared to the next and worst thing , the touchpad .\n[{'aspect': 'touchpad', 'opinion': 'worst', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: + good battery life\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n+ good battery life\n->", + "output": "{\"text\": \"+ good battery life\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a little crowded but they move that line really fast !\n->a little crowded but they move that line really fast !\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the charge cord is very short , about 1 / 2 the size of a regular charging cord\n->the charge cord is very short , about 1 / 2 the size of a regular charging cord\n[{'aspect': 'charge cord', 'opinion': 'short', 'polarity': 'negative', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\ntext: + play store compatibility is available now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n+ play store compatibility is available now .\n->", + "output": "{\"text\": \"+ play store compatibility is available now .\", \"labels\": \"[{'aspect': 'play store', 'opinion': 'available', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice little notebook !\n->nice little notebook !\n[{'aspect': 'notebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I recommend the garlic shrimp , okra ( bindi ) , and anything with lamb .\n->I recommend the garlic shrimp , okra ( bindi ) , and anything with lamb .\n[{'aspect': 'garlic shrimp', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'okra ( bindi )', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: some apps don ' t play well yet , but should with time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsome apps don ' t play well yet , but should with time .\n->", + "output": "{\"text\": \"some apps don ' t play well yet , but should with time .\", \"labels\": \"[{'aspect': 'some apps', 'opinion': \"' t play well\", 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n->Overall , I 'm still impressed that the place even exists and the prices are quite decent but then again , its Chinatown .\n[{'aspect': 'prices', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the first time i went , and was completely taken by the live jazz band and atmosphere , i ordered the lobster cobb salad .\n->the first time i went , and was completely taken by the live jazz band and atmosphere , i ordered the lobster cobb salad .\n[{'aspect': 'live jazz band', 'opinion': 'taken', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'taken', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: - although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- although the screen is nice , the screen ratio is not optimal when using it as a tablet .\n->", + "output": "{\"text\": \"- although the screen is nice , the screen ratio is not optimal when using it as a tablet .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen ratio', 'opinion': 'not optimal', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But , nothing stands out about the cooking .\n->But , nothing stands out about the cooking .\n[{'aspect': 'cooking', 'opinion': 'nothing stands out', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: note : i haven ' t had any issues with the touchscreen at all .\n->note : i haven ' t had any issues with the touchscreen at all .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: - 3 months after purchase , the chromebook has issues with the screen flickering constantly and has been sent in to asus for repairs\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- 3 months after purchase , the chromebook has issues with the screen flickering constantly and has been sent in to asus for repairs\n->", + "output": "{\"text\": \"- 3 months after purchase , the chromebook has issues with the screen flickering constantly and has been sent in to asus for repairs\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place has great indian chinese food .\n->this place has great indian chinese food .\n[{'aspect': 'indian chinese food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: however , there is screen glare , even in a normally lit room , not just a brightly lit room .\n->however , there is screen glare , even in a normally lit room , not just a brightly lit room .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: - the rma process needs improvement - buyer must pay to return the product for repair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the rma process needs improvement - buyer must pay to return the product for repair .\n->", + "output": "{\"text\": \"- the rma process needs improvement - buyer must pay to return the product for repair .\", \"labels\": \"[{'aspect': 'rma process', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: THe Pizza and wine were excellent -the service too -- but what really MADE this place was the backyard dining area .\n->THe Pizza and wine were excellent -the service too -- but what really MADE this place was the backyard dining area .\n[{'aspect': 'Pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: stable , long battery life , and great build .\n->stable , long battery life , and great build .\n[{'aspect': 'battery life', 'opinion': 'stable', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: chromebooks are a waste of time / money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchromebooks are a waste of time / money .\n->", + "output": "{\"text\": \"chromebooks are a waste of time / money .\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'waste', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They are the best bagels I 've had .\n->They are the best bagels I 've had .\n[{'aspect': 'bagels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i picked up the stylus and it fell apart , no drops no damage .\n->i picked up the stylus and it fell apart , no drops no damage .\n[{'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\ntext: so disappointing to receive the laptop and it wouldn ' t even power up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso disappointing to receive the laptop and it wouldn ' t even power up .\n->", + "output": "{\"text\": \"so disappointing to receive the laptop and it wouldn ' t even power up .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I do not recommend lunch specials just because it tasts the same with other regular chinese restaurant .\n->I do not recommend lunch specials just because it tasts the same with other regular chinese restaurant .\n[{'aspect': 'lunch specials', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Ambience is delightful , service impeccable .\n->Ambience is delightful , service impeccable .\n[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: when in use , the lower screen is flickering .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen in use , the lower screen is flickering .\n->", + "output": "{\"text\": \"when in use , the lower screen is flickering .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good for students that carry it from class to class .\n->good for students that carry it from class to class .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n->the one drawback is that it uses more energy than most apps on the mbp ( especially word , not so much with excel or ppt ) , so i ' ve been using google docs / drive in safari ( chrome is also an energy hog ) if i ' m away from a power source and the task is light enough .\n[{'aspect': 'apps', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google docs / drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n->", + "output": "{\"text\": \"i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\", \"labels\": \"[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but when we looked at the menu , there were n't a lot of choices , most of them were dumplings in the appetizer section .\n->but when we looked at the menu , there were n't a lot of choices , most of them were dumplings in the appetizer section .\n[{'aspect': 'menu', 'opinion': \"were n't a lot of choices\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i would highly recommend this product if you want to get into music production like myself .\n->i would highly recommend this product if you want to get into music production like myself .\n[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the performance of my chromebook is the issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe performance of my chromebook is the issue .\n->", + "output": "{\"text\": \"the performance of my chromebook is the issue .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so $ 500 bucks down the drain as i ' m sure that isn ' t covered by any warranties .\n->so $ 500 bucks down the drain as i ' m sure that isn ' t covered by any warranties .\n[{'aspect': 'warranties', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'WARRANTY#QUALITY'}]\nExample:\ntext: This is such a lovely , peaceful place to eat outside .\n->This is such a lovely , peaceful place to eat outside .\n[{'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'peaceful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: video playback is poor with the amazon prime video player .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvideo playback is poor with the amazon prime video player .\n->", + "output": "{\"text\": \"video playback is poor with the amazon prime video player .\", \"labels\": \"[{'aspect': 'amazon prime video player', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was slow , but the people were friendly .\n->Service was slow , but the people were friendly .\n[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Most importantly , food is excellent .\n->Most importantly , food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: great device until battery won ' t charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat device until battery won ' t charge .\n->", + "output": "{\"text\": \"great device until battery won ' t charge .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In addition , the food is very good and the prices are reasonable .\n->In addition , the food is very good and the prices are reasonable .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: really disappointing results with sound volume and volume consistency .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreally disappointing results with sound volume and volume consistency .\n->", + "output": "{\"text\": \"really disappointing results with sound volume and volume consistency .\", \"labels\": \"[{'aspect': 'sound volume', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'volume consistency', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n->Their sake list was extensive , but we were looking for Purple Haze , which was n't listed but made for us upon request !\n[{'aspect': 'sake list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the aluminum body looks great , but feels cheap and thin .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe aluminum body looks great , but feels cheap and thin .\n->", + "output": "{\"text\": \"the aluminum body looks great , but feels cheap and thin .\", \"labels\": \"[{'aspect': 'aluminum body', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'aluminum body', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'aluminum body', 'opinion': 'thin', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s all about google but my kids really like it .\n->it ' s all about google but my kids really like it .\n[{'aspect': 'google', 'opinion': 'like', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: no back light keyboard\n->no back light keyboard\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: speakers are weak and the volume range tops off half way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeakers are weak and the volume range tops off half way .\n->", + "output": "{\"text\": \"speakers are weak and the volume range tops off half way .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'weak', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is the BEST Shabu-Shabu Restaurant in the Try-State Area .\n->This is the BEST Shabu-Shabu Restaurant in the Try-State Area .\n[{'aspect': 'Shabu-Shabu Restaurant', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i recommend buying it .\n->i recommend buying it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i just fear for the long term ruggedness of the exterior .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just fear for the long term ruggedness of the exterior .\n->", + "output": "{\"text\": \"i just fear for the long term ruggedness of the exterior .\", \"labels\": \"[{'aspect': 'exterior', 'opinion': 'fear', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: im thrilled with my mac .\n->im thrilled with my mac .\n[{'aspect': 'mac', 'opinion': 'thrilled', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this unit is a great compromise between powerful cpu and gpu with good battery life .\n->this unit is a great compromise between powerful cpu and gpu with good battery life .\n[{'aspect': 'cpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'gpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}, {'aspect': 'unit', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\ntext: after 3 weeks using this flip , i am quite happy with its performance , design .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter 3 weeks using this flip , i am quite happy with its performance , design .\n->", + "output": "{\"text\": \"after 3 weeks using this flip , i am quite happy with its performance , design .\", \"labels\": \"[{'aspect': 'flip', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'flip', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is incredibly helpful and attentive .\n->The staff is incredibly helpful and attentive .\n[{'aspect': 'staff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this thing boots up , you log in and you ' re ready to go !\n->this thing boots up , you log in and you ' re ready to go !\n[{'aspect': 'boots up', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it ' s easy to use , convenient .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s easy to use , convenient .\n->", + "output": "{\"text\": \"it ' s easy to use , convenient .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: prices are fair across the board for both food and bev .\n->prices are fair across the board for both food and bev .\n[{'aspect': 'food', 'opinion': 'fair', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bev', 'opinion': 'fair', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: the entree was also very good .\n->the entree was also very good .\n[{'aspect': 'entree', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the keyboard is comfortable to type with backlit , screen quality is good enough for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is comfortable to type with backlit , screen quality is good enough for me .\n->", + "output": "{\"text\": \"the keyboard is comfortable to type with backlit , screen quality is good enough for me .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'screen quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n->chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n[{'aspect': 'chrome', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i am not a vegetarian but , almost all the dishes were great .\n->i am not a vegetarian but , almost all the dishes were great .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: thin , light , cool are what i feel when holding it and carry around .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthin , light , cool are what i feel when holding it and carry around .\n->", + "output": "{\"text\": \"thin , light , cool are what i feel when holding it and carry around .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the in - house lady dj on saturday nights has outrageously good taste in music , and moreover , takes requests .\n->the in - house lady dj on saturday nights has outrageously good taste in music , and moreover , takes requests .\n[{'aspect': 'in - house lady dj', 'opinion': 'good taste', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: 8 + lbs , this one is right under 5 so it makes it nice and portable .\n->8 + lbs , this one is right under 5 so it makes it nice and portable .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n->", + "output": "{\"text\": \"the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\", \"labels\": \"[{'aspect': 'tablet mode', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n->some of you will remember the asus \u201c transformer \u201d line of android tablets - pretty respectable tablets with tegra processors , decent builds , and most importantly - a keyboard dock that was far , far superior to any on - screen keyboard options .\n[{'aspect': 'NULL', 'opinion': 'respectable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'builds', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard dock', 'opinion': 'superior', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n->this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n[{'aspect': 'hardware', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the screen is nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is nice .\n->", + "output": "{\"text\": \"the screen is nice .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have the iced tea .\n->have the iced tea .\n[{'aspect': 'iced tea', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: good battery life\n->good battery life\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: google play support is lip service only .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoogle play support is lip service only .\n->", + "output": "{\"text\": \"google play support is lip service only .\", \"labels\": \"[{'aspect': 'google play support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , it ' s a great machine .\n->overall , it ' s a great machine .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: stay far away from this laptop !\n->stay far away from this laptop !\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the problem dinvolves the headphone jack .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe problem dinvolves the headphone jack .\n->", + "output": "{\"text\": \"the problem dinvolves the headphone jack .\", \"labels\": \"[{'aspect': 'headphone jack', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , i ' d be super upset if that were my employee .\n->again , i ' d be super upset if that were my employee .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: we ' ve got android nougat on beta running pretty well now .\n->we ' ve got android nougat on beta running pretty well now .\n[{'aspect': 'android nougat', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: it would not charge at all !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit would not charge at all !\n->", + "output": "{\"text\": \"it would not charge at all !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is awesome - definitely try the striped bass .\n->The food is awesome - definitely try the striped bass .\n[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'striped bass', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Just because it 's cheap does NOT mean the portions are small or the food is nasty , IT IS GREAT !\n->Just because it 's cheap does NOT mean the portions are small or the food is nasty , IT IS GREAT !\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'nasty', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is a great laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great laptop .\n->", + "output": "{\"text\": \"this is a great laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was the only thing good about this restaurant .\n->The service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: ( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n->( * cough ' quizlet ' cough * ) i use this hooked up to my mobile hotspot from my cell phone mainly , and it allows me access anywhere i have cell service .\n[{'aspect': 'it', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: it has a weird smell that ' s why i ' m giving it 3 stars .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has a weird smell that ' s why i ' m giving it 3 stars .\n->", + "output": "{\"text\": \"it has a weird smell that ' s why i ' m giving it 3 stars .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'weird', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their sushi , Kamikaze and other Rolls are fresh and well presented .\n->Their sushi , Kamikaze and other Rolls are fresh and well presented .\n[{'aspect': 'sushi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'well presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Kamikaze', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Kamikaze', 'opinion': 'well presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Rolls', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Rolls', 'opinion': 'well presented', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I am not a vegetarian but , almost all the dishes were great .\n->I am not a vegetarian but , almost all the dishes were great .\n[{'aspect': 'dishes', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n->", + "output": "{\"text\": \"i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the people that i bring there go back on their own and bring their friends !\n->all the people that i bring there go back on their own and bring their friends !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: i ' ve used this daily for nearly eight months and have been very happy with .\n->i ' ve used this daily for nearly eight months and have been very happy with .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the notebook is decent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe notebook is decent .\n->", + "output": "{\"text\": \"the notebook is decent .\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nevertheless the food itself is pretty good .\n->Nevertheless the food itself is pretty good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: it is easy to use , light , and you have the ability to download apps for just about any need .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is easy to use , light , and you have the ability to download apps for just about any need .\n->", + "output": "{\"text\": \"it is easy to use , light , and you have the ability to download apps for just about any need .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speakers are also not great and the max volume on watching netflix or other videos is rather quite .\n->the speakers are also not great and the max volume on watching netflix or other videos is rather quite .\n[{'aspect': 'speakers', 'opinion': 'not great', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: amazon is 2 - day shipping me a replacement .\n->amazon is 2 - day shipping me a replacement .\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\ntext: however , the bluetooth is a nightmare .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , the bluetooth is a nightmare .\n->", + "output": "{\"text\": \"however , the bluetooth is a nightmare .\", \"labels\": \"[{'aspect': 'bluetooth', 'opinion': 'nightmare', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: plug - in or usb microphones seem to work fine , so it ' s not a terribly big issue and i don ' t really even use a microphone that often , but it ' s annoying to buy a product and not have it working .\n->plug - in or usb microphones seem to work fine , so it ' s not a terribly big issue and i don ' t really even use a microphone that often , but it ' s annoying to buy a product and not have it working .\n[{'aspect': 'product', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: fish was overdone .\n->fish was overdone .\n[{'aspect': 'fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: it just stopped working in the middle of my paper i was writing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit just stopped working in the middle of my paper i was writing .\n->", + "output": "{\"text\": \"it just stopped working in the middle of my paper i was writing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this because it had a good price .\n->i bought this because it had a good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n->my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the screen is good , the tablet mode is nice , and the keyboard has a good feel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is good , the tablet mode is nice , and the keyboard has a good feel .\n->", + "output": "{\"text\": \"the screen is good , the tablet mode is nice , and the keyboard has a good feel .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Okay service .\n->Okay service .\n[{'aspect': 'service', 'opinion': 'Okay', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the screen is excellent .\n->the screen is excellent .\n[{'aspect': 'screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: but after three charge cycles the screen started vibrating vigorously from side - to - side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut after three charge cycles the screen started vibrating vigorously from side - to - side .\n->", + "output": "{\"text\": \"but after three charge cycles the screen started vibrating vigorously from side - to - side .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , it 's the service that leaves a bad taste in my mouth .\n->however , it 's the service that leaves a bad taste in my mouth .\n[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the prices are wonderfully low .\n->the prices are wonderfully low .\n[{'aspect': 'NULL', 'opinion': 'wonderfully low', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: the tablet is good also , however can ' t they design something that covers the keyboard during tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe tablet is good also , however can ' t they design something that covers the keyboard during tablet mode .\n->", + "output": "{\"text\": \"the tablet is good also , however can ' t they design something that covers the keyboard during tablet mode .\", \"labels\": \"[{'aspect': 'tablet', 'opinion': 'good', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: terrible , terrible management - deserves to be shut - down .\n->terrible , terrible management - deserves to be shut - down .\n[{'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The food was just OK , at least for what food was available .\n->The food was just OK , at least for what food was available .\n[{'aspect': 'food', 'opinion': 'OK', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it is the best chromebook that i have ever used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is the best chromebook that i have ever used .\n->", + "output": "{\"text\": \"it is the best chromebook that i have ever used .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but after only a couple of days the thing just turned off and won ' t turn back on again .\n->but after only a couple of days the thing just turned off and won ' t turn back on again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: someone else recommended the dessert - we also left that .\n->someone else recommended the dessert - we also left that .\n[{'aspect': 'dessert', 'opinion': 'recommended', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: both forward and rear facing cameras would be nice too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nboth forward and rear facing cameras would be nice too .\n->", + "output": "{\"text\": \"both forward and rear facing cameras would be nice too .\", \"labels\": \"[{'aspect': 'cameras', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n->the rub is that chromeos itself is so limited in functionality that it is probably the most secure os platform out there .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: mouse and wi - fi never functioned correctly .\n->mouse and wi - fi never functioned correctly .\n[{'aspect': 'mouse', 'opinion': 'never functioned correctly', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'wi - fi', 'opinion': 'never functioned correctly', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: great product ( perfect for student use ) but did n ' t last past 2 months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat product ( perfect for student use ) but did n ' t last past 2 months .\n->", + "output": "{\"text\": \"great product ( perfect for student use ) but did n ' t last past 2 months .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus is a great computer company .\n->asus is a great computer company .\n[{'aspect': 'asus', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'computer company', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n->We ended our great experience by having Gulab Jamun ( dessert ) recommended by the waiter .\n[{'aspect': 'Gulab Jamun ( dessert )', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: then the power cord went bad and i had to pay $ 50 for a new one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen the power cord went bad and i had to pay $ 50 for a new one .\n->", + "output": "{\"text\": \"then the power cord went bad and i had to pay $ 50 for a new one .\", \"labels\": \"[{'aspect': 'power cord', 'opinion': 'bad', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is average .\n->service is average .\n[{'aspect': 'service', 'opinion': 'average', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it didn ' t come with the box but it came with the charger and so far , i ' ve only been using for a few days , but i have no issues with the item at all .\n->it didn ' t come with the box but it came with the charger and so far , i ' ve only been using for a few days , but i have no issues with the item at all .\n[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it ' s fine as a computer , but the lack of a real guest account made it not workable as a family room media machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s fine as a computer , but the lack of a real guest account made it not workable as a family room media machine .\n->", + "output": "{\"text\": \"it ' s fine as a computer , but the lack of a real guest account made it not workable as a family room media machine .\", \"labels\": \"[{'aspect': 'guest account', 'opinion': 'not workable', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love it .\n->i love it .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: awsome machine .\n->awsome machine .\n[{'aspect': 'machine', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: hardware is still pretty sound .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhardware is still pretty sound .\n->", + "output": "{\"text\": \"hardware is still pretty sound .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'sound', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this one is pretty , but obviously not sturdy .\n->this one is pretty , but obviously not sturdy .\n[{'aspect': 'NULL', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not sturdy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: * love * the feel and look of it , no complaints at all , samsung chromebooks are amazing !\n->* love * the feel and look of it , no complaints at all , samsung chromebooks are amazing !\n[{'aspect': 'samsung chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebooks', 'opinion': 'no complaints', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebooks', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: but i ' m growing ever more disenchanted with the core m3 processing speed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut i ' m growing ever more disenchanted with the core m3 processing speed .\n->", + "output": "{\"text\": \"but i ' m growing ever more disenchanted with the core m3 processing speed .\", \"labels\": \"[{'aspect': 'core m3', 'opinion': 'disenchanted', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in fact , the only way you know it ' s probably not new is because it didn ' t arrive in an apple original box ; it arrived wrapped ( neat and tight ) in bubble wrap in an amazon box .\n->in fact , the only way you know it ' s probably not new is because it didn ' t arrive in an apple original box ; it arrived wrapped ( neat and tight ) in bubble wrap in an amazon box .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: camera is sd but not a problem .\n->camera is sd but not a problem .\n[{'aspect': 'camera', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#GENERAL'}]\ntext: also , the batterly life that is reported by industry affiliates is way off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , the batterly life that is reported by industry affiliates is way off .\n->", + "output": "{\"text\": \"also , the batterly life that is reported by industry affiliates is way off .\", \"labels\": \"[{'aspect': 'batterly life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n->i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n[{'aspect': 'computer', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'computer', 'opinion': 'defective', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: love my macbook , beautiful and use daily !\n->love my macbook , beautiful and use daily !\n[{'aspect': 'macbook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'macbook', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: google locked me out because after an update , my keyboard output was not as it should have been ( some keys were inverted ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngoogle locked me out because after an update , my keyboard output was not as it should have been ( some keys were inverted ) .\n->", + "output": "{\"text\": \"google locked me out because after an update , my keyboard output was not as it should have been ( some keys were inverted ) .\", \"labels\": \"[{'aspect': 'keyboard output', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when i play a game , there is noise on the screen .\n->when i play a game , there is noise on the screen .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n->this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: when i go to type something , it sticks and will not release .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i go to type something , it sticks and will not release .\n->", + "output": "{\"text\": \"when i go to type something , it sticks and will not release .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The wine list is extensive and impressive .\n->The wine list is extensive and impressive .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: food was ok .\n->food was ok .\n[{'aspect': 'food', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: i will probably be sending it back as it seems too complicated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will probably be sending it back as it seems too complicated .\n->", + "output": "{\"text\": \"i will probably be sending it back as it seems too complicated .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'complicated', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: turned it off and on and the screen still stays black but you can hear it running .\n->turned it off and on and the screen still stays black but you can hear it running .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Service was very good - prompt , attentive and non-intrusive .\n->Service was very good - prompt , attentive and non-intrusive .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'non-intrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: by far this is the most disappointing experiences that i ever had doing online shopping .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nby far this is the most disappointing experiences that i ever had doing online shopping .\n->", + "output": "{\"text\": \"by far this is the most disappointing experiences that i ever had doing online shopping .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great purchase , quick shipping .\n->great purchase , quick shipping .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'shipping', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: the keyboard is easy to use , and there is no external noise to contend with .\n->the keyboard is easy to use , and there is no external noise to contend with .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: at this stage i am disappointed than raging up and it wasn ' t the end yet the keyboard didn ' t work at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat this stage i am disappointed than raging up and it wasn ' t the end yet the keyboard didn ' t work at all .\n->", + "output": "{\"text\": \"at this stage i am disappointed than raging up and it wasn ' t the end yet the keyboard didn ' t work at all .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at first i was totally stoked on this chromebook .\n->at first i was totally stoked on this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'stoked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the pizza was delivered cold and the cheese was n ' t even fully melted !\n->the pizza was delivered cold and the cheese was n ' t even fully melted !\n[{'aspect': 'pizza', 'opinion': 'cold', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': \"was n ' t even fully melted\", 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: nice chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice chromebook .\n->", + "output": "{\"text\": \"nice chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not a typical pizza joint , but good for a low key and fairly cheap nice sit down dinner .\n->Not a typical pizza joint , but good for a low key and fairly cheap nice sit down dinner .\n[{'aspect': 'dinner', 'opinion': 'cheap nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: with this asus i ' m experiencing 2 very disappointing issues .\n->with this asus i ' m experiencing 2 very disappointing issues .\n[{'aspect': 'asus', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: but seemed very poorly made for the money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut seemed very poorly made for the money .\n->", + "output": "{\"text\": \"but seemed very poorly made for the money .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: would never go back\n->would never go back\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the keyboard is easy to use , and there is no external noise to contend with .\n->the keyboard is easy to use , and there is no external noise to contend with .\n[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: it is may 14 and it ' s not connecting to wifi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is may 14 and it ' s not connecting to wifi .\n->", + "output": "{\"text\": \"it is may 14 and it ' s not connecting to wifi .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: every app i have downloaded from the google app store has worked perfectly .\n->every app i have downloaded from the google app store has worked perfectly .\n[{'aspect': 'app', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the computer itself is as stated and i received at a price my husband and i were both happy with .\n->the computer itself is as stated and i received at a price my husband and i were both happy with .\n[{'aspect': 'computer', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n->", + "output": "{\"text\": \"it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\", \"labels\": \"[{'aspect': 'equipment', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'equipment', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If we were to move from the upper east side , we would genuinely miss this restaurant .\n->If we were to move from the upper east side , we would genuinely miss this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: average to good thai food , but terrible delivery .\n->average to good thai food , but terrible delivery .\n[{'aspect': 'thai food', 'opinion': 'average to good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: nice piece but battery holds only 3h - not 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice piece but battery holds only 3h - not 10 .\n->", + "output": "{\"text\": \"nice piece but battery holds only 3h - not 10 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is worth an one-hour drive .\n->This place is worth an one-hour drive .\n[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is my first chromebook , and i ' m absolutely loving it .\n->this is my first chromebook , and i ' m absolutely loving it .\n[{'aspect': 'this', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: manufacturing seems unreliable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmanufacturing seems unreliable .\n->", + "output": "{\"text\": \"manufacturing seems unreliable .\", \"labels\": \"[{'aspect': 'manufacturing', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ' ll be there for every anniversary , birthday , valentines day . . .\n->you ' ll be there for every anniversary , birthday , valentines day . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: The menu choices are similar but the taste lacked more flavor than it looked .\n->The menu choices are similar but the taste lacked more flavor than it looked .\n[{'aspect': 'taste', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu choices', 'opinion': 'similar', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'lacked', 'polarity': 'negative', 'category': 'NULL'}]\ntext: also , often , the device simply would not power on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , often , the device simply would not power on .\n->", + "output": "{\"text\": \"also , often , the device simply would not power on .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not even going to bother to describe it ; it speaks for itself .\n->i ' m not even going to bother to describe it ; it speaks for itself .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: skip dessert .\n->skip dessert .\n[{'aspect': 'dessert', 'opinion': 'skip', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: loved this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nloved this chromebook .\n->", + "output": "{\"text\": \"loved this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is a lot of fun with live entertainment and all kinds of Disney type special effects .\n->It is a lot of fun with live entertainment and all kinds of Disney type special effects .\n[{'aspect': 'live entertainment', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'special effects', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it looks nice .\n->it looks nice .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: first one i received , the space bar got stuck and returned it for a replacement .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst one i received , the space bar got stuck and returned it for a replacement .\n->", + "output": "{\"text\": \"first one i received , the space bar got stuck and returned it for a replacement .\", \"labels\": \"[{'aspect': 'space bar', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n->Since it literally is a complete hole in the wall , it 's a bit intimidating at first , but you get over that very quickly as soon as the friendly staff welcomes you - do n't hesitate to ask for help with what to get .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: completely aggravated , sending this crap back as soon as i hear from them .\n->completely aggravated , sending this crap back as soon as i hear from them .\n[{'aspect': 'crap', 'opinion': 'crap', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: now , the headphone jack produces low volume at 10 percent capacity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow , the headphone jack produces low volume at 10 percent capacity .\n->", + "output": "{\"text\": \"now , the headphone jack produces low volume at 10 percent capacity .\", \"labels\": \"[{'aspect': 'headphone jack', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n->i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n[{'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: so i call asus customer support , and received some of the worst customer service ever .\n->so i call asus customer support , and received some of the worst customer service ever .\n[{'aspect': 'asus customer support', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: the laptop is pretty lightweight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop is pretty lightweight .\n->", + "output": "{\"text\": \"the laptop is pretty lightweight .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - memory is not easily upgradable anymore .\n->- memory is not easily upgradable anymore .\n[{'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: also , chromeos does not allow color / temperature calibration of the display device .\n->also , chromeos does not allow color / temperature calibration of the display device .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\ntext: cons : the speakers make a loud muffled white noise while playing music on occasion .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncons : the speakers make a loud muffled white noise while playing music on occasion .\n->", + "output": "{\"text\": \"cons : the speakers make a loud muffled white noise while playing music on occasion .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'cons', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The white bean brushetta to start was incredible and the pasta was phenomenal .\n->The white bean brushetta to start was incredible and the pasta was phenomenal .\n[{'aspect': 'white bean brushetta', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i had the best ravioli ever .\n->i had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\n->", + "output": "{\"text\": \"on the one i received , the trackpad is hard to press down and seems to rub against the palm rest casing .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'hard', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n->a different server enhanced the fun , dumping our entrees in front of us halfway through our appetizer ( which was delicious ) .\n[{'aspect': 'server', 'opinion': 'enhanced', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n->The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n[{'aspect': 'bhelpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sevpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'samosa chaats', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bombay style chaat', 'opinion': 'famous scrumptious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: screen is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen is good .\n->", + "output": "{\"text\": \"screen is good .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we split a tasty vegetable samosa and the malai tikka wrap .\n->we split a tasty vegetable samosa and the malai tikka wrap .\n[{'aspect': 'vegetable samosa', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'malai tikka wrap', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: unfortunately , the downfall for me are the speakers .\n->unfortunately , the downfall for me are the speakers .\n[{'aspect': 'speakers', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: it is not up to my expectations , it produces some kind of sound when you play something on youtube ( from its keyboard ) perhaps !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is not up to my expectations , it produces some kind of sound when you play something on youtube ( from its keyboard ) perhaps !\n->", + "output": "{\"text\": \"it is not up to my expectations , it produces some kind of sound when you play something on youtube ( from its keyboard ) perhaps !\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n->update - 7 / 30 / 18 - exactly the same issue has occurred with a crack in the keyboard cover on the opposite side .\n[{'aspect': 'keyboard cover', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n->The Bagels have an outstanding taste with a terrific texture , both chewy yet not gummy .\n[{'aspect': 'Bagels', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'chewy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Bagels', 'opinion': 'gummy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: my main complain involves terrible battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy main complain involves terrible battery life .\n->", + "output": "{\"text\": \"my main complain involves terrible battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was slow , but the people were friendly .\n->service was slow , but the people were friendly .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the touch screen is nice , and i like to use it for free handing things when i need to .\n->the touch screen is nice , and i like to use it for free handing things when i need to .\n[{'aspect': 'touch screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: speakers were messed up when turned on and the return did it still have n ' t processed even though it ' s been several weeks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeakers were messed up when turned on and the return did it still have n ' t processed even though it ' s been several weeks .\n->", + "output": "{\"text\": \"speakers were messed up when turned on and the return did it still have n ' t processed even though it ' s been several weeks .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'messed up', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n->The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n[{'aspect': 'wait staff', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'knowledgable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'likeable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was great .\n->The food was great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very poor battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery poor battery life .\n->", + "output": "{\"text\": \"very poor battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'poor', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we did tip , i guess the model / waitress just wanted more and complained to the manager .\n->we did tip , i guess the model / waitress just wanted more and complained to the manager .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: amazing fresh dogs but best of all endless toppings ! ! !\n->amazing fresh dogs but best of all endless toppings ! ! !\n[{'aspect': 'dogs', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dogs', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'toppings', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'toppings', 'opinion': 'endless', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n->", + "output": "{\"text\": \"this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fries', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: oh and this is a beautiful machine and the lid is amazing .\n->oh and this is a beautiful machine and the lid is amazing .\n[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'lid', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: very touchy touch screen , too sensitive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery touchy touch screen , too sensitive .\n->", + "output": "{\"text\": \"very touchy touch screen , too sensitive .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'touchy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'sensitive', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: gates and this machine allows me to do this .\n->gates and this machine allows me to do this .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: great place to go for a drink too because they have 100 kinds of beer .\n->great place to go for a drink too because they have 100 kinds of beer .\n[{'aspect': 'drink', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: this is the worst computer i have ever owned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the worst computer i have ever owned .\n->", + "output": "{\"text\": \"this is the worst computer i have ever owned .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: otherwise i really do love it\n->otherwise i really do love it\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Judging from previous posts this used to be a good place , but not any longer .\n->Judging from previous posts this used to be a good place , but not any longer .\n[{'aspect': 'place', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: however , after having the computer for about 4 months it suddenly died one day and would not turn on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , after having the computer for about 4 months it suddenly died one day and would not turn on .\n->", + "output": "{\"text\": \"however , after having the computer for about 4 months it suddenly died one day and would not turn on .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the asus chromebook flip 302 fit the bill .\n->the asus chromebook flip 302 fit the bill .\n[{'aspect': 'asus chromebook flip 302', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Unique apppetizers .\n->Unique apppetizers .\n[{'aspect': 'apppetizers', 'opinion': 'Unique', 'polarity': 'positive', 'category': 'NULL'}]\ntext: udpate : after talking to tech support , i found out that they made the wrong repair .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nudpate : after talking to tech support , i found out that they made the wrong repair .\n->", + "output": "{\"text\": \"udpate : after talking to tech support , i found out that they made the wrong repair .\", \"labels\": \"[{'aspect': 'tech support', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: let me tell you , this thing is snappy .\n->let me tell you , this thing is snappy .\n[{'aspect': 'NULL', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: but $ 1 for each small piece ? ? ?\n->but $ 1 for each small piece ? ? ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: worst customer service experience in years .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworst customer service experience in years .\n->", + "output": "{\"text\": \"worst customer service experience in years .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n->this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n[{'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'samsung chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great selection of wine , and seafood .\n->Great selection of wine , and seafood .\n[{'aspect': 'selection of wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen is great and the unit is fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is great and the unit is fast .\n->", + "output": "{\"text\": \"the screen is great and the unit is fast .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'unit', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m satisfied with the product .\n->i ' m satisfied with the product .\n[{'aspect': 'product', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Good food .\n->Good food .\n[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very disappointed in very bad tech support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery disappointed in very bad tech support .\n->", + "output": "{\"text\": \"very disappointed in very bad tech support .\", \"labels\": \"[{'aspect': 'tech support', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'tech support', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wifi sketchy to nonexistent .\n->wifi sketchy to nonexistent .\n[{'aspect': 'wifi', 'opinion': 'sketchy', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\nExample:\ntext: still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n->still , any quibbles about the bill were off - set by the pour - your - own measures of liquers which were courtesey of the house . . .\n[{'aspect': 'measures of liquers', 'opinion': 'pour - your - own', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'measures of liquers', 'opinion': 'courtesey', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\ntext: this is a piece of garbage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a piece of garbage .\n->", + "output": "{\"text\": \"this is a piece of garbage .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the quantity is also very good , you will come out satisfied .\n->the quantity is also very good , you will come out satisfied .\n[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n->The seafood is amazing , there 's a good wine list , and the ever-changing menu always offers some great surprises .\n[{'aspect': 'seafood', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'ever-changing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'great surprises', 'polarity': 'positive', 'category': 'NULL'}]\ntext: then tonight ( april 29th ) my daughter says it ' s not charging .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen tonight ( april 29th ) my daughter says it ' s not charging .\n->", + "output": "{\"text\": \"then tonight ( april 29th ) my daughter says it ' s not charging .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard feels firm and no flex , screen is nice for the price range .\n->keyboard feels firm and no flex , screen is nice for the price range .\n[{'aspect': 'keyboard', 'opinion': 'firm', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\nExample:\ntext: its cheap plastic and honestly , the keyboard its really bad .\n->its cheap plastic and honestly , the keyboard its really bad .\n[{'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: bad touchpad , jerky movement , imprecise , no controls to improve .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbad touchpad , jerky movement , imprecise , no controls to improve .\n->", + "output": "{\"text\": \"bad touchpad , jerky movement , imprecise , no controls to improve .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'jerky', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'imprecise', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the usb - c ports are nice and i found that a completely dead battery to fully charged time was about an hour .\n->the usb - c ports are nice and i found that a completely dead battery to fully charged time was about an hour .\n[{'aspect': 'usb - c ports', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'charged time', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: was very easy to add memory .\n->was very easy to add memory .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: wifi radio loses signal too frequently .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwifi radio loses signal too frequently .\n->", + "output": "{\"text\": \"wifi radio loses signal too frequently .\", \"labels\": \"[{'aspect': 'wifi radio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it can not .\n->it can not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I recieved prompt service with a smile .\n->I recieved prompt service with a smile .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it began to shut down and restart all on it ' s own - continuously .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit began to shut down and restart all on it ' s own - continuously .\n->", + "output": "{\"text\": \"it began to shut down and restart all on it ' s own - continuously .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n->i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n[{'aspect': 'asus customer service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: peppers , onions , relish , chilli , cheeses , you name it .\n->peppers , onions , relish , chilli , cheeses , you name it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: it sounded clean and simple , exactly what we need .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit sounded clean and simple , exactly what we need .\n->", + "output": "{\"text\": \"it sounded clean and simple , exactly what we need .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'clean', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n->i ' ve also been amazed at all the new additions in the past few years : a new jazz bar , the most fantastic dining garden , the best thin crust pizzas , and now a lasagna menu which is to die for ( these are not your average lasagnas ) !\n[{'aspect': 'dining garden', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'jazz bar', 'opinion': 'new', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'thin crust pizzas', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lasagna menu', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s so much faster and the mac os is so much more secure .\n->it ' s so much faster and the mac os is so much more secure .\n[{'aspect': 'NULL', 'opinion': 'faster', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mac os', 'opinion': 'secure', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\ntext: very very very very weak battery and the play store is a joke , this thing still needs a lot of work to become a serious product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery very very very weak battery and the play store is a joke , this thing still needs a lot of work to become a serious product .\n->", + "output": "{\"text\": \"very very very very weak battery and the play store is a joke , this thing still needs a lot of work to become a serious product .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'weak', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'play store', 'opinion': 'joke', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n->The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n[{'aspect': 'anti-pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Fresh ingredients and everything is made to order .\n->Fresh ingredients and everything is made to order .\n[{'aspect': 'ingredients', 'opinion': 'Fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: beyond that , less than a week into the ownership trial , the power _ supply failed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeyond that , less than a week into the ownership trial , the power _ supply failed .\n->", + "output": "{\"text\": \"beyond that , less than a week into the ownership trial , the power _ supply failed .\", \"labels\": \"[{'aspect': 'power _ supply failed', 'opinion': '.', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I was very disappointed with this restaurant .\n->I was very disappointed with this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: in total , it took 4 updates to access the google play store - - completely unacceptable !\n->in total , it took 4 updates to access the google play store - - completely unacceptable !\n[{'aspect': 'google play store', 'opinion': 'unacceptable', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: but basic stuff needs to be properly engineered and designed , and this machine had two huge problems right out of the gate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut basic stuff needs to be properly engineered and designed , and this machine had two huge problems right out of the gate .\n->", + "output": "{\"text\": \"but basic stuff needs to be properly engineered and designed , and this machine had two huge problems right out of the gate .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n->For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n[{'aspect': 'Paneer Roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: perfect laptop for everyday use .\n->perfect laptop for everyday use .\n[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: good laptop , but not great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood laptop , but not great .\n->", + "output": "{\"text\": \"good laptop , but not great .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good selection of wines ranging from affordable to high end .\n->good selection of wines ranging from affordable to high end .\n[{'aspect': 'selection of wines', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n->the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n[{'aspect': 'build quality', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'back lit keys', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: the screen is only pretty good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is only pretty good .\n->", + "output": "{\"text\": \"the screen is only pretty good .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: how can a brand new computer not charge properly ?\n->how can a brand new computer not charge properly ?\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the touchpad is above average , but not great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchpad is above average , but not great .\n->", + "output": "{\"text\": \"the touchpad is above average , but not great .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'above average', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The dinner menu is diverse and top-notch as well .\n->The dinner menu is diverse and top-notch as well .\n[{'aspect': 'dinner menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner menu', 'opinion': 'top-notch', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We waited at the bar and had martinis that were just right .\n->We waited at the bar and had martinis that were just right .\n[{'aspect': 'martinis', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}]\ntext: why does this company keep releasing faulty units from the production line ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhy does this company keep releasing faulty units from the production line ?\n->", + "output": "{\"text\": \"why does this company keep releasing faulty units from the production line ?\", \"labels\": \"[{'aspect': 'company', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#OPERATION_PERFORMANCE'}, {'aspect': 'units', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the issue is that i got a faulty laptop and that ' s why the negative review .\n->the issue is that i got a faulty laptop and that ' s why the negative review .\n[{'aspect': 'laptop', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'negative', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: overall i like the portability and battery life on this device .\n->overall i like the portability and battery life on this device .\n[{'aspect': 'device', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'battery life', 'opinion': 'like', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: 2 - right arrow key not stabilized within the body and comes off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n2 - right arrow key not stabilized within the body and comes off .\n->", + "output": "{\"text\": \"2 - right arrow key not stabilized within the body and comes off .\", \"labels\": \"[{'aspect': 'right arrow key', 'opinion': 'not stabilized', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great product and price\n->great product and price\n[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n->this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: - display glass not glued well on one side .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- display glass not glued well on one side .\n->", + "output": "{\"text\": \"- display glass not glued well on one side .\", \"labels\": \"[{'aspect': 'display glass', 'opinion': 'not glued well', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - no cd / dvd reader ( but who uses them nowadays anyway )\n->- no cd / dvd reader ( but who uses them nowadays anyway )\n[{'aspect': 'cd / dvd reader', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OPTICAL_DRIVES#DESIGN_FEATURES'}]\nExample:\ntext: The only thing more wonderful than the food ( which is exceptional ) is the service .\n->The only thing more wonderful than the food ( which is exceptional ) is the service .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: screen not aligned perfectly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen not aligned perfectly .\n->", + "output": "{\"text\": \"screen not aligned perfectly .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'not aligned perfectly', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice value .\n->nice value .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n->it has been super easy to become familiar with it ; the more i use it , the more i appreciate it !\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i purchased this asus chromebook in may of 2018 and initially loved it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni purchased this asus chromebook in may of 2018 and initially loved it .\n->", + "output": "{\"text\": \"i purchased this asus chromebook in may of 2018 and initially loved it .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ask for Usha , the nicest bartender in manhattan .\n->Ask for Usha , the nicest bartender in manhattan .\n[{'aspect': 'bartender', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: not a good quality laptop .\n->not a good quality laptop .\n[{'aspect': 'laptop', 'opinion': 'not a good', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: this is my third and last chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my third and last chromebook .\n->", + "output": "{\"text\": \"this is my third and last chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n->original order arrived with damaged screen ; however , i contacted amazon and 2 days later i had the replacement .\n[{'aspect': 'screen', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: the hostess was rude and i got a distinct feeling that they did not want to serve us .\n->the hostess was rude and i got a distinct feeling that they did not want to serve us .\n[{'aspect': 'hostess', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it wouldn ' t charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit wouldn ' t charge .\n->", + "output": "{\"text\": \"it wouldn ' t charge .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is very good , but not outstanding .\n->the food is very good , but not outstanding .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'not outstanding', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: even the wine by the glass was good .\n->even the wine by the glass was good .\n[{'aspect': 'wine by the glass', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\ntext: i really wanted to like this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really wanted to like this chromebook .\n->", + "output": "{\"text\": \"i really wanted to like this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'wanted to like', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the os doesn ' t leave menu bar at the top for copying in programs for studies .\n->the os doesn ' t leave menu bar at the top for copying in programs for studies .\n[{'aspect': 'os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: i give it 4 stars because i believe that these devices are perfect for adults who just want to surf the internet or watch netflix .\n->i give it 4 stars because i believe that these devices are perfect for adults who just want to surf the internet or watch netflix .\n[{'aspect': 'devices', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: unfortunately , the touchscreen is extremely insensitive , making it unusable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunfortunately , the touchscreen is extremely insensitive , making it unusable .\n->", + "output": "{\"text\": \"unfortunately , the touchscreen is extremely insensitive , making it unusable .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'insensitive', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'touchscreen', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n->The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: udpate : after talking to tech support , i found out that they made the wrong repair .\n->udpate : after talking to tech support , i found out that they made the wrong repair .\n[{'aspect': 'tech support', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: everything was good for a few days after receiving the product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything was good for a few days after receiving the product .\n->", + "output": "{\"text\": \"everything was good for a few days after receiving the product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have to say I have never had a disapointing meal here .\n->I have to say I have never had a disapointing meal here .\n[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i really like my chromebook .\n->i really like my chromebook .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: very disappointing in an otherwise great product\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery disappointing in an otherwise great product\n->", + "output": "{\"text\": \"very disappointing in an otherwise great product\", \"labels\": \"[{'aspect': 'product', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this item 7 months ago and i love it .\n->i purchased this item 7 months ago and i love it .\n[{'aspect': 'item', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n->Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'creme brulee', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sugar', 'opinion': 'charred', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the unit is sleek , nice and the keyboard feels tactily right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe unit is sleek , nice and the keyboard feels tactily right .\n->", + "output": "{\"text\": \"the unit is sleek , nice and the keyboard feels tactily right .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'unit', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'right', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Most of the servers are very attentive , friendly and quite attractive .\n->Most of the servers are very attentive , friendly and quite attractive .\n[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Patroon features a nice cigar bar and has great staff .\n->Patroon features a nice cigar bar and has great staff .\n[{'aspect': 'cigar bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but , when software compatibility began stacking up , it became a nogo for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut , when software compatibility began stacking up , it became a nogo for me .\n->", + "output": "{\"text\": \"but , when software compatibility began stacking up , it became a nogo for me .\", \"labels\": \"[{'aspect': 'software', 'opinion': 'nogo', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have to say i have never had a disapointing meal here .\n->i have to say i have never had a disapointing meal here .\n[{'aspect': 'meal', 'opinion': 'never had a disapointing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the screen display is absolutely amazing and totally blows me away .\n->the screen display is absolutely amazing and totally blows me away .\n[{'aspect': 'screen display', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: very fast product , but with this kind of technology it ' s not possible to use a program , that i work ( minitab ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery fast product , but with this kind of technology it ' s not possible to use a program , that i work ( minitab ) .\n->", + "output": "{\"text\": \"very fast product , but with this kind of technology it ' s not possible to use a program , that i work ( minitab ) .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: barely have it for 6 months and everything ' s going haywire .\n->barely have it for 6 months and everything ' s going haywire .\n[{'aspect': 'NULL', 'opinion': 'haywire', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Obv caviar is top of the line but the rest of the menu is so diverse it gives you a chance to taste so manydifferent varietys .\n->Obv caviar is top of the line but the rest of the menu is so diverse it gives you a chance to taste so manydifferent varietys .\n[{'aspect': 'Obv caviar', 'opinion': 'top of the line', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'diverse', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' m very disappointed with my purchase\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m very disappointed with my purchase\n->", + "output": "{\"text\": \"i ' m very disappointed with my purchase\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food , great decor , great service .\n->Great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is great , the computer is fast , and looks great with the aluminum case .\n->the screen is great , the computer is fast , and looks great with the aluminum case .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'aluminum case', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: cpu and ram were running low .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncpu and ram were running low .\n->", + "output": "{\"text\": \"cpu and ram were running low .\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'low', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'ram', 'opinion': 'low', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: touchpad is nice and responsive .\n->touchpad is nice and responsive .\n[{'aspect': 'touchpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the waitress was very patient with us and the food is phenomenal !\n->the waitress was very patient with us and the food is phenomenal !\n[{'aspect': 'waitress', 'opinion': 'patient', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: gpu wasn ' t drawing a lot of power because i was playing world of warcraft on the recommended settings .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngpu wasn ' t drawing a lot of power because i was playing world of warcraft on the recommended settings .\n->", + "output": "{\"text\": \"gpu wasn ' t drawing a lot of power because i was playing world of warcraft on the recommended settings .\", \"labels\": \"[{'aspect': 'gpu', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked properly for less than a week , and the touch screen stopped functioning again .\n->it worked properly for less than a week , and the touch screen stopped functioning again .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: this makes this chromebook closer to a real computer .\n->this makes this chromebook closer to a real computer .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: got my replacement recently and i haven ' t had any major issues so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngot my replacement recently and i haven ' t had any major issues so far .\n->", + "output": "{\"text\": \"got my replacement recently and i haven ' t had any major issues so far .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , i feel this is the best laptop i ' ve ever purchased or used .\n->overall , i feel this is the best laptop i ' ve ever purchased or used .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the actual laptop is very much darker and blue .\n->the actual laptop is very much darker and blue .\n[{'aspect': 'actual laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i ' m still worried about the quality of capacitor and conductor inside this thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m still worried about the quality of capacitor and conductor inside this thing .\n->", + "output": "{\"text\": \"i ' m still worried about the quality of capacitor and conductor inside this thing .\", \"labels\": \"[{'aspect': 'capacitor', 'opinion': 'worried', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}, {'aspect': 'conductor', 'opinion': 'worried', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is a cute place and could be good but they need to get their act together .\n->This is a cute place and could be good but they need to get their act together .\n[{'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was so excited to buy the asus chromebook , and bought some for my grandchildren .\n->i was so excited to buy the asus chromebook , and bought some for my grandchildren .\n[{'aspect': 'asus chromebook', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: overall , it is a great laptop for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , it is a great laptop for the price .\n->", + "output": "{\"text\": \"overall , it is a great laptop for the price .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n->the chicken pot pie is exceptional , the cheeseburger huge and delictable , and the service professional wan warm .\n[{'aspect': 'chicken pot pie', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheeseburger', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cheeseburger', 'opinion': 'delictable', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'warm', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n->it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n[{'aspect': 'NULL', 'opinion': 'not really bad', 'polarity': 'neutral', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the cpu runs super fast and doesn ' t start utilizing its full potential until you start doing things like installation of applications , but the memory usage sits around 40 - 50 % the most of the time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe cpu runs super fast and doesn ' t start utilizing its full potential until you start doing things like installation of applications , but the memory usage sits around 40 - 50 % the most of the time .\n->", + "output": "{\"text\": \"the cpu runs super fast and doesn ' t start utilizing its full potential until you start doing things like installation of applications , but the memory usage sits around 40 - 50 % the most of the time .\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: win for the ports , win for the price , and win for a brand new unopened macbook .\n->win for the ports , win for the price , and win for a brand new unopened macbook .\n[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n->she was very helpful in suggesting us drinks and helped us in ordering a lot of good dishes since we knew nothing about indian food .\n[{'aspect': 'dishes', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: my fan can get it as low as 82 - 83 degrees , consistently , while idle or gaming .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy fan can get it as low as 82 - 83 degrees , consistently , while idle or gaming .\n->", + "output": "{\"text\": \"my fan can get it as low as 82 - 83 degrees , consistently , while idle or gaming .\", \"labels\": \"[{'aspect': 'fan', 'opinion': 'low', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The selection changes frequently but the basic dishes are always available .\n->The selection changes frequently but the basic dishes are always available .\n[{'aspect': 'selection', 'opinion': 'changes frequently', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'basic dishes', 'opinion': 'available', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: which lets face it . . . . at times it ' s a good thing .\n->which lets face it . . . . at times it ' s a good thing .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the steelsteries keyboard feels great and looks awesome , but the keyboard surface of the laptop can get warm while sitting idle or gaming even when i have a fan pushing cold air underneath the laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe steelsteries keyboard feels great and looks awesome , but the keyboard surface of the laptop can get warm while sitting idle or gaming even when i have a fan pushing cold air underneath the laptop .\n->", + "output": "{\"text\": \"the steelsteries keyboard feels great and looks awesome , but the keyboard surface of the laptop can get warm while sitting idle or gaming even when i have a fan pushing cold air underneath the laptop .\", \"labels\": \"[{'aspect': 'steelsteries keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'steelsteries keyboard', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n->* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the food was good .\n->the food was good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the monitor is bright and colorful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe monitor is bright and colorful .\n->", + "output": "{\"text\": \"the monitor is bright and colorful .\", \"labels\": \"[{'aspect': 'monitor', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'monitor', 'opinion': 'colorful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Warning : You may find it difficult to dine at other Japanese restaurants after a visit to Mizu !\n->Warning : You may find it difficult to dine at other Japanese restaurants after a visit to Mizu !\n[{'aspect': 'Mizu', 'opinion': 'difficult', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: food was very good , but not what i would consider out of this world .\n->food was very good , but not what i would consider out of this world .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: i don ' t see any problems with it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t see any problems with it .\n->", + "output": "{\"text\": \"i don ' t see any problems with it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i highly recommend this laptop for anyone looking for a great performing machine with an outstanding price ( just to be clear , it won ' t be running the newest high - end games on ultra - high graphics settings , but it still performs phenomenally for its price range and usage category ) .\n->i highly recommend this laptop for anyone looking for a great performing machine with an outstanding price ( just to be clear , it won ' t be running the newest high - end games on ultra - high graphics settings , but it still performs phenomenally for its price range and usage category ) .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'phenomenally', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: great friendly service , fast seating , fast delivery , excellent sushi .\n->great friendly service , fast seating , fast delivery , excellent sushi .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'seating', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'sushi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: keeping the msi software is optional , a few of these are useful like burnrecovery to make a backup of your version of windows just in case anything happens .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeeping the msi software is optional , a few of these are useful like burnrecovery to make a backup of your version of windows just in case anything happens .\n->", + "output": "{\"text\": \"keeping the msi software is optional , a few of these are useful like burnrecovery to make a backup of your version of windows just in case anything happens .\", \"labels\": \"[{'aspect': 'msi software', 'opinion': 'optional', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'burnrecovery', 'opinion': 'useful', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen is bright , doesn ' t feel heavy .\n->screen is bright , doesn ' t feel heavy .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': \"' t feel heavy\", 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: but after three charge cycles the screen started vibrating vigorously from side - to - side .\n->but after three charge cycles the screen started vibrating vigorously from side - to - side .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n->", + "output": "{\"text\": \"i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\", \"labels\": \"[{'aspect': 'realtek audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n->this is undoubtedly my favorite modern japanese brasserie ( that don \u2019 t serve sushi ) , and in my opinion , one of the most romantic restaurants in the city !\n[{'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'modern japanese brasserie', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: My wife had the fried shrimp which are huge and loved it .\n->My wife had the fried shrimp which are huge and loved it .\n[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: do not purchase this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not purchase this laptop .\n->", + "output": "{\"text\": \"do not purchase this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: aside from the larger capacity , it hasn ' t lived up to some of the other hardware i ' ve used .\n->aside from the larger capacity , it hasn ' t lived up to some of the other hardware i ' ve used .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i had previouslu bought an msi mobo which refused to boot unless windows 10 was the os , but i had worked around all of those problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had previouslu bought an msi mobo which refused to boot unless windows 10 was the os , but i had worked around all of those problems .\n->", + "output": "{\"text\": \"i had previouslu bought an msi mobo which refused to boot unless windows 10 was the os , but i had worked around all of those problems .\", \"labels\": \"[{'aspect': 'msi mobo', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n->touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n[{'aspect': 'touchscreen', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'neat', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'awkward', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n->only drawback - they wo n ' t toast your bagel , and they do n ' t make eggs for the bagel .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i should have known better : msi has boot issues , no way around it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni should have known better : msi has boot issues , no way around it .\n->", + "output": "{\"text\": \"i should have known better : msi has boot issues , no way around it .\", \"labels\": \"[{'aspect': 'msi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MOTHERBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n->even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: delicious crab cakes too .\n->delicious crab cakes too .\n[{'aspect': 'crab cakes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it simply refuses to boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit simply refuses to boot .\n->", + "output": "{\"text\": \"it simply refuses to boot .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - horrible customer service .\n->- horrible customer service .\n[{'aspect': 'customer service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: The waitress remembers me and is very friendly , she knows what my regular is and that 's the fried mini buns with the condensed milk and the assorted fruits on beancurd .\n->The waitress remembers me and is very friendly , she knows what my regular is and that 's the fried mini buns with the condensed milk and the assorted fruits on beancurd .\n[{'aspect': 'waitress', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried mini buns with the condensed milk and the assorted fruits on beancurd', 'opinion': 'regular', 'polarity': 'positive', 'category': 'NULL'}]\ntext: yet it fails to boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyet it fails to boot .\n->", + "output": "{\"text\": \"yet it fails to boot .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fails', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria 's .\n->The pizza is delicious - they use fresh mozzarella instead of the cheap , frozen , shredded cheese common to most pizzaria 's .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozzarella', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'frozen', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'shredded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Moules were excellent , lobster ravioli was VERY salty !\n->Moules were excellent , lobster ravioli was VERY salty !\n[{'aspect': 'Moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it is apparent the hard drive has failed yet again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is apparent the hard drive has failed yet again .\n->", + "output": "{\"text\": \"it is apparent the hard drive has failed yet again .\", \"labels\": \"[{'aspect': 'hard drive', 'opinion': 'failed', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .\n->my chicken was inedible as there were so many fatty lumps which i had to keep spitting out into my napkin .\n[{'aspect': 'chicken', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: We visited Bread Bar during January restaurant week and were so pleased with the menu selections and service .\n->We visited Bread Bar during January restaurant week and were so pleased with the menu selections and service .\n[{'aspect': 'menu selections', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'NULL'}]\ntext: upon accepting a graphics driver update , the whole laptop froze .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupon accepting a graphics driver update , the whole laptop froze .\n->", + "output": "{\"text\": \"upon accepting a graphics driver update , the whole laptop froze .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'froze', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great for groups , great for a date , great for early brunch or a nightcap .\n->Great for groups , great for a date , great for early brunch or a nightcap .\n[{'aspect': 'brunch', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'nightcap', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: lucky strike is a great casual place to just grab a bite to eat .\n->lucky strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'lucky strike', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'lucky strike', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i ' ve never owned an msi laptop and if i can help it , this one is going back and i will never own one again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve never owned an msi laptop and if i can help it , this one is going back and i will never own one again .\n->", + "output": "{\"text\": \"i ' ve never owned an msi laptop and if i can help it , this one is going back and i will never own one again .\", \"labels\": \"[{'aspect': 'msi laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - though the case is plastic , the keyboard area itself has a cold metallic feel .\n->- though the case is plastic , the keyboard area itself has a cold metallic feel .\n[{'aspect': 'case', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard area', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: works good but right click on mouse pad wont wok have to use external mouse\n->works good but right click on mouse pad wont wok have to use external mouse\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: * * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\n->", + "output": "{\"text\": \"* * update - so i closed and opened this laptop again , and the simple act of putting it to sleep and waking it up , as usual , caused a problem .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n->My wife and I ate here earlier this week and have not stopped ranting and raving about the food .\n[{'aspect': 'food', 'opinion': 'ranting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'raving', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: It 's great to go for a quick lunch either alone or with a friend .\n->It 's great to go for a quick lunch either alone or with a friend .\n[{'aspect': 'lunch', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}]\ntext: out of the box , gorgeous laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nout of the box , gorgeous laptop .\n->", + "output": "{\"text\": \"out of the box , gorgeous laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also , the batterly life that is reported by industry affiliates is way off .\n->also , the batterly life that is reported by industry affiliates is way off .\n[{'aspect': 'batterly life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: i had fish and my husband had the filet - both of which exceeded our expectations .\n->i had fish and my husband had the filet - both of which exceeded our expectations .\n[{'aspect': 'fish', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'filet', 'opinion': 'exceeded our expectations', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: keyboard problems too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard problems too .\n->", + "output": "{\"text\": \"keyboard problems too .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is not what one would expect from a joint in this price category .\n->service is not what one would expect from a joint in this price category .\n[{'aspect': 'service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: besides that it runs ok\n->besides that it runs ok\n[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: so far great machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far great machine .\n->", + "output": "{\"text\": \"so far great machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Saul is the best restaurant on Smith Street and in Brooklyn .\n->Saul is the best restaurant on Smith Street and in Brooklyn .\n[{'aspect': 'Saul', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n->Food was average and creme brulee was awful - the sugar was charred , not caramelized and smelled of kerosene .\n[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'creme brulee', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sugar', 'opinion': 'charred', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the laptop ran quite swiftly !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop ran quite swiftly !\n->", + "output": "{\"text\": \"the laptop ran quite swiftly !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'swiftly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n->i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n[{'aspect': 'pizza', 'opinion': 'crave', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my 17 year old granddaughter loves it .\n->my 17 year old granddaughter loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the temperatures were good , and the overall responsiveness of the system was fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe temperatures were good , and the overall responsiveness of the system was fine .\n->", + "output": "{\"text\": \"the temperatures were good , and the overall responsiveness of the system was fine .\", \"labels\": \"[{'aspect': 'temperatures', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'responsiveness of the system', 'opinion': 'fine', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The drinks are always well made and wine selection is fairly priced .\n->The drinks are always well made and wine selection is fairly priced .\n[{'aspect': 'drinks', 'opinion': 'well made', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine selection', 'opinion': 'fairly priced', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: less wait time for me !\n->less wait time for me !\n[{'aspect': 'wait time', 'opinion': 'less', 'polarity': 'positive', 'category': 'NULL'}]\ntext: game performance was fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngame performance was fantastic .\n->", + "output": "{\"text\": \"game performance was fantastic .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and when it did work it was very slow .\n->and when it did work it was very slow .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Bagels are ok , but be sure not to make any special requests !\n->Bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\n->", + "output": "{\"text\": \"the intel rst driver ( for the hybrid drive ) version is old , and causes rare system lockups .\", \"labels\": \"[{'aspect': 'intel rst driver', 'opinion': 'old', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was also very good .\n->Service was also very good .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is very nice for me .\n->the keyboard is very nice for me .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: further , the msi dragon center ( default software on the installation ) caused a blue screen which lead to me having to factory reset .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfurther , the msi dragon center ( default software on the installation ) caused a blue screen which lead to me having to factory reset .\n->", + "output": "{\"text\": \"further , the msi dragon center ( default software on the installation ) caused a blue screen which lead to me having to factory reset .\", \"labels\": \"[{'aspect': 'msi dragon center (', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: baluchi ' s has solid food and a nice decor at reasonable prices .\n->baluchi ' s has solid food and a nice decor at reasonable prices .\n[{'aspect': \"baluchi ' s\", 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'food', 'opinion': 'solid', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i do n ' t game , so not idea how that would go , but it ' s probably not bad .\n->i do n ' t game , so not idea how that would go , but it ' s probably not bad .\n[{'aspect': 'NULL', 'opinion': 'not bad', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: msi used to have a driver installer by disk that would install all the proper drivers in the right order , so you ' d have the perfect configuration .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmsi used to have a driver installer by disk that would install all the proper drivers in the right order , so you ' d have the perfect configuration .\n->", + "output": "{\"text\": \"msi used to have a driver installer by disk that would install all the proper drivers in the right order , so you ' d have the perfect configuration .\", \"labels\": \"[{'aspect': 'driver installer', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop charger has sparked repeatedly .\n->the laptop charger has sparked repeatedly .\n[{'aspect': 'laptop charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: your mileage may vary but it ' s been a headache for me since i bought it .\n->your mileage may vary but it ' s been a headache for me since i bought it .\n[{'aspect': 'NULL', 'opinion': 'headache', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: however , the laptop has no dvd drive , and no such driver dvd was provided .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , the laptop has no dvd drive , and no such driver dvd was provided .\n->", + "output": "{\"text\": \"however , the laptop has no dvd drive , and no such driver dvd was provided .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is only 3 .\n->battery life is only 3 .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: get the pepperoni - yum - and a family style salad .\n->get the pepperoni - yum - and a family style salad .\n[{'aspect': 'pepperoni', 'opinion': 'yum', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'family style salad', 'opinion': 'yum', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: it ' s possible i got bad hardware by chance , but all of the issues being directly traceable to drivers suggests the issues were all driver - related .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s possible i got bad hardware by chance , but all of the issues being directly traceable to drivers suggests the issues were all driver - related .\n->", + "output": "{\"text\": \"it ' s possible i got bad hardware by chance , but all of the issues being directly traceable to drivers suggests the issues were all driver - related .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}, {'aspect': 'drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Mizu is home to creative and unique rolls not to found anywhere else .\n->Mizu is home to creative and unique rolls not to found anywhere else .\n[{'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n->the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screen', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: second where the heck is my other 8 gigs of ram ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsecond where the heck is my other 8 gigs of ram ?\n->", + "output": "{\"text\": \"second where the heck is my other 8 gigs of ram ?\", \"labels\": \"[{'aspect': '8 gigs of ram', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If I could rate the people this place would be off the charts - unfortunately - the pizza , sorry - not the best in NYC .\n->If I could rate the people this place would be off the charts - unfortunately - the pizza , sorry - not the best in NYC .\n[{'aspect': 'people', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'best', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the speakers on at the front on bottom so sound quality isn ' t the best .\n->the speakers on at the front on bottom so sound quality isn ' t the best .\n[{'aspect': 'speakers', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'sound quality', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: its fast , light weight , quiet , and looks easy to add additional ram and hdd ' s .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits fast , light weight , quiet , and looks easy to add additional ram and hdd ' s .\n->", + "output": "{\"text\": \"its fast , light weight , quiet , and looks easy to add additional ram and hdd ' s .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n->i was visiting new york city with a friend and we discovered this really warm and inviting restaurant .\n[{'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'inviting', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n->overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n[{'aspect': 'asus c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i think this msi product will be a part of my business for a long time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think this msi product will be a part of my business for a long time .\n->", + "output": "{\"text\": \"i think this msi product will be a part of my business for a long time .\", \"labels\": \"[{'aspect': 'msi product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n->i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Food was good not great not worth the wait or another visit\n->Food was good not great not worth the wait or another visit\n[{'aspect': 'Food', 'opinion': 'good not great not worth the wait or another visit', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i bought this laptop on black friday for $ 700 and i ' m debating returning it because i ' ve had quite a few issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this laptop on black friday for $ 700 and i ' m debating returning it because i ' ve had quite a few issues .\n->", + "output": "{\"text\": \"i bought this laptop on black friday for $ 700 and i ' m debating returning it because i ' ve had quite a few issues .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ask for Usha , the nicest bartender in manhattan .\n->Ask for Usha , the nicest bartender in manhattan .\n[{'aspect': 'Usha', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Kind , attentive wait staff .\n->Kind , attentive wait staff .\n[{'aspect': 'wait staff', 'opinion': 'Kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: im actually wondering if there is an issue with the speakers , it ' s so bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nim actually wondering if there is an issue with the speakers , it ' s so bad .\n->", + "output": "{\"text\": \"im actually wondering if there is an issue with the speakers , it ' s so bad .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: loses wifi connection every hour .\n->loses wifi connection every hour .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is may 14 and it ' s not connecting to wifi .\n->it is may 14 and it ' s not connecting to wifi .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: the battery life is terrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is terrible .\n->", + "output": "{\"text\": \"the battery life is terrible .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is bright and color spread is good .\n->the screen is bright and color spread is good .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'color spread', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great service , great food .\n->Great service , great food .\n[{'aspect': 'service', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: again , i wonder if my unit is defective because most reviews talk about this laptop having a 6 + hour battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nagain , i wonder if my unit is defective because most reviews talk about this laptop having a 6 + hour battery life .\n->", + "output": "{\"text\": \"again , i wonder if my unit is defective because most reviews talk about this laptop having a 6 + hour battery life .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'defective', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n->However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n[{'aspect': 'management', 'opinion': 'changed', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'door', 'opinion': 'great big', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Service is top notch .\n->Service is top notch .\n[{'aspect': 'Service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}]\ntext: not sure it is related , but when i open facebook , aaaallllll the messages open simultaneously .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot sure it is related , but when i open facebook , aaaallllll the messages open simultaneously .\n->", + "output": "{\"text\": \"not sure it is related , but when i open facebook , aaaallllll the messages open simultaneously .\", \"labels\": \"[{'aspect': 'facebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'back room', 'opinion': 'secret', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Service was also very good .\n->Service was also very good .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the unit is whisper quiet and hasn ' t gotten hot no matter how hard i push it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe unit is whisper quiet and hasn ' t gotten hot no matter how hard i push it .\n->", + "output": "{\"text\": \"the unit is whisper quiet and hasn ' t gotten hot no matter how hard i push it .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: besides that it runs ok\n->besides that it runs ok\n[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n->while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n[{'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i ' ve never had a computer as fast as this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve never had a computer as fast as this .\n->", + "output": "{\"text\": \"i ' ve never had a computer as fast as this .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i purchased this asus chromebook in may of 2018 and initially loved it .\n->i purchased this asus chromebook in may of 2018 and initially loved it .\n[{'aspect': 'asus chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: whether i ' m playing games or video editing , or web design , it doesn ' t hesitate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhether i ' m playing games or video editing , or web design , it doesn ' t hesitate .\n->", + "output": "{\"text\": \"whether i ' m playing games or video editing , or web design , it doesn ' t hesitate .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n->Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n[{'aspect': 'dim sum', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: quiet keyboard .\n->quiet keyboard .\n[{'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i ' m not a huge gamer , but it can run crysis with full mods on ultra settings and doesn ' t make so much as a light hum .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m not a huge gamer , but it can run crysis with full mods on ultra settings and doesn ' t make so much as a light hum .\n->", + "output": "{\"text\": \"i ' m not a huge gamer , but it can run crysis with full mods on ultra settings and doesn ' t make so much as a light hum .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n->This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n[{'aspect': 'night scene', 'opinion': 'alive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spot', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the overall design is sleek and pleasing to look at and hold .\n->the overall design is sleek and pleasing to look at and hold .\n[{'aspect': 'design', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'pleasing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: honestly , i ' m debating returning this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhonestly , i ' m debating returning this laptop .\n->", + "output": "{\"text\": \"honestly , i ' m debating returning this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'debating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s all about the food ! !\n->it ' s all about the food ! !\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: we were drawn into the belly dancing show that captivated the crowd .\n->we were drawn into the belly dancing show that captivated the crowd .\n[{'aspect': 'belly dancing show', 'opinion': 'captivated', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: your mileage may vary but it ' s been a headache for me since i bought it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyour mileage may vary but it ' s been a headache for me since i bought it .\n->", + "output": "{\"text\": \"your mileage may vary but it ' s been a headache for me since i bought it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'headache', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n->an unpretentious spot in park slope , the sushi is consistently good , the service is pleasant , effective and unassuming .\n[{'aspect': 'spot', 'opinion': 'unpretentious', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'effective', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the battery on this computer is not very good i feel like i always have to have it plugged in .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery on this computer is not very good i feel like i always have to have it plugged in .\n->", + "output": "{\"text\": \"the battery on this computer is not very good i feel like i always have to have it plugged in .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fabulous food - if the front of house staff do n ' t put you off \u2013\n->fabulous food - if the front of house staff do n ' t put you off \u2013\n[{'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'front of house staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n->great laptop , would not recommend for hardcore gaming , but light gaming it can handle .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: bad battery , speaker and touchpad\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbad battery , speaker and touchpad\n->", + "output": "{\"text\": \"bad battery , speaker and touchpad\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'bad', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'speaker', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer was a 50 - 50 chance to boot from day one , but we figured it needed to run updates and get settled .\n->the computer was a 50 - 50 chance to boot from day one , but we figured it needed to run updates and get settled .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s fast , but the whole thing is wearing out quickly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s fast , but the whole thing is wearing out quickly .\n->", + "output": "{\"text\": \"it ' s fast , but the whole thing is wearing out quickly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can actually take a picture of this screen and not have near the amount of wavy lines that you used to get .\n->you can actually take a picture of this screen and not have near the amount of wavy lines that you used to get .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Dessert is a joke ... dont bother\n->Dessert is a joke ... dont bother\n[{'aspect': 'Dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'NULL'}]\ntext: however , when i received this laptop , it stopped booting to windows after 3 days .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , when i received this laptop , it stopped booting to windows after 3 days .\n->", + "output": "{\"text\": \"however , when i received this laptop , it stopped booting to windows after 3 days .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i returned it because the speaker is dead low .\n->i returned it because the speaker is dead low .\n[{'aspect': 'speaker', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: kinda small but good quality !\n->kinda small but good quality !\n[{'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: the ssd is not work after 4 month\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe ssd is not work after 4 month\n->", + "output": "{\"text\": \"the ssd is not work after 4 month\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'not work', 'polarity': 'negative', 'category': 'HARD_DISC#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sauce was watery and the food did n ' t have much flavor .\n->sauce was watery and the food did n ' t have much flavor .\n[{'aspect': 'sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: easy to use .\n->easy to use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: laptop runs really well but fans get a bit loud when gaming\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop runs really well but fans get a bit loud when gaming\n->", + "output": "{\"text\": \"laptop runs really well but fans get a bit loud when gaming\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's a shame that a nice , convenient place like the Pink Pony can be so ruined by lousy service .\n->It 's a shame that a nice , convenient place like the Pink Pony can be so ruined by lousy service .\n[{'aspect': 'place', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i absolutely love this chromebook !\n->i absolutely love this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this computer is fast , as the specs would have you believe , but i ' ve only had it about a week , and it has already crashed so bad once that i had to do a factory reset on it ( though , to be fair , the computer made the reboot super easy , so that was nice ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer is fast , as the specs would have you believe , but i ' ve only had it about a week , and it has already crashed so bad once that i had to do a factory reset on it ( though , to be fair , the computer made the reboot super easy , so that was nice ) .\n->", + "output": "{\"text\": \"this computer is fast , as the specs would have you believe , but i ' ve only had it about a week , and it has already crashed so bad once that i had to do a factory reset on it ( though , to be fair , the computer made the reboot super easy , so that was nice ) .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'computer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is a cute place and could be good but they need to get their act together .\n->This is a cute place and could be good but they need to get their act together .\n[{'aspect': 'place', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the internal flash memory is like greased lightning .\n->the internal flash memory is like greased lightning .\n[{'aspect': 'flash memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: the keyboard / mousepad isn ' t super comfortable for casual use ( like on your lap , sitting on a couch ) , so i think it is more meant to be used more or less exclusively for gaming at a desk or table .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard / mousepad isn ' t super comfortable for casual use ( like on your lap , sitting on a couch ) , so i think it is more meant to be used more or less exclusively for gaming at a desk or table .\n->", + "output": "{\"text\": \"the keyboard / mousepad isn ' t super comfortable for casual use ( like on your lap , sitting on a couch ) , so i think it is more meant to be used more or less exclusively for gaming at a desk or table .\", \"labels\": \"[{'aspect': 'keyboard / mousepad', 'opinion': \"' t super comfortable\", 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n->replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: for a restaurant with such a good reputation and that is usually so packed , there was no reason for such a lack of intelligent customer service .\n->for a restaurant with such a good reputation and that is usually so packed , there was no reason for such a lack of intelligent customer service .\n[{'aspect': 'restaurant', 'opinion': 'good reputation', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'customer service', 'opinion': 'intelligent', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it is heavy , but that is to be expected with a laptop like this one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is heavy , but that is to be expected with a laptop like this one .\n->", + "output": "{\"text\": \"it is heavy , but that is to be expected with a laptop like this one .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'heavy', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have never been so disgusted by both food an service .\n->i have never been so disgusted by both food an service .\n[{'aspect': 'food', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: very happy with it .\n->very happy with it .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\n->", + "output": "{\"text\": \"however , i tried to power it up today and kept receiving an error message right at startup that was a ` ` system boot violation ` ` .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but when we looked at the menu , there were n't a lot of choices , most of them were dumplings in the appetizer section .\n->but when we looked at the menu , there were n't a lot of choices , most of them were dumplings in the appetizer section .\n[{'aspect': 'menu', 'opinion': \"were n't a lot of choices\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i have never had cheescake like this .\n->i have never had cheescake like this .\n[{'aspect': 'cheescake', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: , the advertised ram is 16gb and in the answer questions section has answers from the manufacturer saying ` ` this will have 16gb of memory ! ` `\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n, the advertised ram is 16gb and in the answer questions section has answers from the manufacturer saying ` ` this will have 16gb of memory ! ` `\n->", + "output": "{\"text\": \", the advertised ram is 16gb and in the answer questions section has answers from the manufacturer saying ` ` this will have 16gb of memory ! ` `\", \"labels\": \"[{'aspect': 'ram', 'opinion': '16gb', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , i wonder if my unit is defective because most reviews talk about this laptop having a 6 + hour battery life .\n->again , i wonder if my unit is defective because most reviews talk about this laptop having a 6 + hour battery life .\n[{'aspect': 'unit', 'opinion': 'defective', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i went to areo on a sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n->i went to areo on a sunday afternoon with four of my girlfriends , and spent three enjoyable hours there .\n[{'aspect': 'areo', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i did not get what i originally paid for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did not get what i originally paid for .\n->", + "output": "{\"text\": \"i did not get what i originally paid for .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is alright - some stuff is good - some is not ( like the steak dish which tends to be dry ) .\n->The food is alright - some stuff is good - some is not ( like the steak dish which tends to be dry ) .\n[{'aspect': 'steak dish', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: for 7 years they have put out the most tasty , most delicious food and kept it that way . . .\n->for 7 years they have put out the most tasty , most delicious food and kept it that way . . .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\n->", + "output": "{\"text\": \"i don ' t know if i got a defective model or what , but something has gone wrong every time i boot up my computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'computer', 'opinion': 'defective', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: probably would not go back here .\n->probably would not go back here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: not only does make the best pizza in NY , maybe anywhere .\n->not only does make the best pizza in NY , maybe anywhere .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: not to mention it is quite clunky and large .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot to mention it is quite clunky and large .\n->", + "output": "{\"text\": \"not to mention it is quite clunky and large .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'clunky', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'large', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we had a great time at the jekyll and hyde pub last night .\n->we had a great time at the jekyll and hyde pub last night .\n[{'aspect': 'jekyll and hyde pub', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The spicy Tuna roll is huge and probably the best that I 've had at this price range .\n->The spicy Tuna roll is huge and probably the best that I 've had at this price range .\n[{'aspect': 'Tuna roll', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price range', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: just that it seems that the hard drive doesn ' t work properly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust that it seems that the hard drive doesn ' t work properly .\n->", + "output": "{\"text\": \"just that it seems that the hard drive doesn ' t work properly .\", \"labels\": \"[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: only complaint that i took a star off for is that the edge you rest your arms on is not rounded off and it is uncomfortable to rest on them for while .\n->only complaint that i took a star off for is that the edge you rest your arms on is not rounded off and it is uncomfortable to rest on them for while .\n[{'aspect': 'edge', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'edge', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: but just at first glance , this thing is top quality .\n->but just at first glance , this thing is top quality .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: i really do like this computer , however the description is wrong .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really do like this computer , however the description is wrong .\n->", + "output": "{\"text\": \"i really do like this computer , however the description is wrong .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'description', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n->Good , because hey , it 's more food , but bad because dim sum is supposed to be smaller portions so you can try out more dishes and smaller so that each dish is cheap .\n[{'aspect': 'dim sum', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'more', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: not only is the cuisine the best around , the service has always been attentive and charming .\n->not only is the cuisine the best around , the service has always been attentive and charming .\n[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: it only comes with 8 gb or ram not 16 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit only comes with 8 gb or ram not 16 .\n->", + "output": "{\"text\": \"it only comes with 8 gb or ram not 16 .\", \"labels\": \"[{'aspect': 'ram not', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After really enjoying ourselves at the bar we sat down at a table and had dinner .\n->After really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it starts up fast .\n->it starts up fast .\n[{'aspect': 'starts up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the keys feel nice and responsive however the mouse pad is a little over responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keys feel nice and responsive however the mouse pad is a little over responsive .\n->", + "output": "{\"text\": \"the keys feel nice and responsive however the mouse pad is a little over responsive .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keys', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'mouse pad', 'opinion': 'over responsive', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 1st problem that occurred is it kept shutting on and off .\n->the 1st problem that occurred is it kept shutting on and off .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i just find the battery draining to quickly in my opinion .\n->i just find the battery draining to quickly in my opinion .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the monitor looks fantastic , but i still cant get over the fact that you are basically lied to aboit memory availability .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe monitor looks fantastic , but i still cant get over the fact that you are basically lied to aboit memory availability .\n->", + "output": "{\"text\": \"the monitor looks fantastic , but i still cant get over the fact that you are basically lied to aboit memory availability .\", \"labels\": \"[{'aspect': 'monitor', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'memory availability', 'opinion': 'lied', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the crunchy tuna , it is to die for .\n->try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The signs , the specials menus , food , and even all the waitstaff are ALL TOTALLY Japanese .\n->The signs , the specials menus , food , and even all the waitstaff are ALL TOTALLY Japanese .\n[{'aspect': 'signs', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials menus', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'Japanese', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n->", + "output": "{\"text\": \"the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\", \"labels\": \"[{'aspect': 'responses', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the new pro model is very light and compact , and can easily be carried around with you every day .\n->the new pro model is very light and compact , and can easily be carried around with you every day .\n[{'aspect': 'pro model', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro model', 'opinion': 'easily be carried', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: i generally like this place .\n->i generally like this place .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: mousepad is a little wonky if you ' re not deliberate with your fingers , recommend using a mouse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmousepad is a little wonky if you ' re not deliberate with your fingers , recommend using a mouse .\n->", + "output": "{\"text\": \"mousepad is a little wonky if you ' re not deliberate with your fingers , recommend using a mouse .\", \"labels\": \"[{'aspect': 'mousepad', 'opinion': 'wonky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n->my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n[{'aspect': 'Scallion Pancake', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Scallion Pancake', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shredded Squid Family Style', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shuizhu Fish', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The sweet lassi was excellent as was the lamb chettinad and the garlic naan but the rasamalai was forgettable .\n->The sweet lassi was excellent as was the lamb chettinad and the garlic naan but the rasamalai was forgettable .\n[{'aspect': 'sweet lassi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rasamalai', 'opinion': 'forgettable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: keyboard layout is not the best , do not like that i have to press the function key to raise or lower the volume / brightness on the arrow keys .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard layout is not the best , do not like that i have to press the function key to raise or lower the volume / brightness on the arrow keys .\n->", + "output": "{\"text\": \"keyboard layout is not the best , do not like that i have to press the function key to raise or lower the volume / brightness on the arrow keys .\", \"labels\": \"[{'aspect': 'keyboard layout is', 'opinion': 'not the best', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The all-Italian staff is warm and engaging from the start .\n->The all-Italian staff is warm and engaging from the start .\n[{'aspect': 'staff', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'engaging', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Made my dining experience uncomfortable .\n->Made my dining experience uncomfortable .\n[{'aspect': 'dining experience', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the cooler boost button is cool , also like the fan placement and how the laptop is elevated in the back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe cooler boost button is cool , also like the fan placement and how the laptop is elevated in the back .\n->", + "output": "{\"text\": \"the cooler boost button is cool , also like the fan placement and how the laptop is elevated in the back .\", \"labels\": \"[{'aspect': 'cooler boost button', 'opinion': 'cooler', 'polarity': 'positive', 'category': 'FANS&COOLING#DESIGN_FEATURES'}, {'aspect': 'fan placement', 'opinion': 'like', 'polarity': 'positive', 'category': 'FANS&COOLING#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however with that being said i bought this laptop about 3 days ago and it ' s already not working .\n->however with that being said i bought this laptop about 3 days ago and it ' s already not working .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: warm and friendly in the winter and terrific outdoor seating in the warmer months .\n->warm and friendly in the winter and terrific outdoor seating in the warmer months .\n[{'aspect': 'outdoor seating', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: in essence , if you want a gaming pc , this one will do the job .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin essence , if you want a gaming pc , this one will do the job .\n->", + "output": "{\"text\": \"in essence , if you want a gaming pc , this one will do the job .\", \"labels\": \"[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n->i ' ve inadvertently had more than a dozen youtube videos playing at the same time and it was sort of noisy but it wasn ' t lagging .\n[{'aspect': 'NULL', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: how do you rate home ?\n->how do you rate home ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: this laptop is actually horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is actually horrible .\n->", + "output": "{\"text\": \"this laptop is actually horrible .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: has a long battery life .\n->has a long battery life .\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: in the beginning i felt a little weird about this trackpad .\n->in the beginning i felt a little weird about this trackpad .\n[{'aspect': 'trackpad', 'opinion': 'weird', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: everything about the experience has been terrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything about the experience has been terrible .\n->", + "output": "{\"text\": \"everything about the experience has been terrible .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Nothing better than buying a snapple for $ 3.25 too .\n->Nothing better than buying a snapple for $ 3.25 too .\n[{'aspect': 'snapple', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n->The have over 100 different beers to offer thier guest so that made my husband very happy and the food was delicious , if I must recommend a dish it must be the pumkin tortelini .\n[{'aspect': 'beers', 'opinion': 'happy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pumkin tortelini', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the most recent incident is the sound wo n ' t work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe most recent incident is the sound wo n ' t work .\n->", + "output": "{\"text\": \"the most recent incident is the sound wo n ' t work .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the back lit keyboard is one of the nicest keyboards i have ever typed on .\n->the back lit keyboard is one of the nicest keyboards i have ever typed on .\n[{'aspect': 'back lit keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboards', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: issue summary : frequent crashing\n->issue summary : frequent crashing\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i had this for all of a day before it began to have severe issues i set it back to factory settings and that worked for a time but it eventually had an issue with system 32 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had this for all of a day before it began to have severe issues i set it back to factory settings and that worked for a time but it eventually had an issue with system 32 .\n->", + "output": "{\"text\": \"i had this for all of a day before it began to have severe issues i set it back to factory settings and that worked for a time but it eventually had an issue with system 32 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'system 32', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: every time in new york i make it a point to visit restaurant saul on smith street .\n->every time in new york i make it a point to visit restaurant saul on smith street .\n[{'aspect': 'restaurant saul', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i just do n ' t understand all the hype . . .\n->i just do n ' t understand all the hype . . .\n[{'aspect': 'NULL', 'opinion': 'hype', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: within two months , there were too many issues / bugs with the os .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwithin two months , there were too many issues / bugs with the os .\n->", + "output": "{\"text\": \"within two months , there were too many issues / bugs with the os .\", \"labels\": \"[{'aspect': 'os', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n->If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n[{'aspect': 'nori', 'opinion': 'not-so-fresh', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: There was a small wait , but shorter than I expected .\n->There was a small wait , but shorter than I expected .\n[{'aspect': 'wait', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'shorter', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen would freeze , requiring a hard shutdown .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen would freeze , requiring a hard shutdown .\n->", + "output": "{\"text\": \"the screen would freeze , requiring a hard shutdown .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'freeze', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: $ 170 down the toilet . . .\n->$ 170 down the toilet . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: super yummy pizza !\n->super yummy pizza !\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the computer ' s hardware is decent , but the materials are poor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer ' s hardware is decent , but the materials are poor .\n->", + "output": "{\"text\": \"the computer ' s hardware is decent , but the materials are poor .\", \"labels\": \"[{'aspect': \"computer ' s hardware\", 'opinion': 'decent', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'materials', 'opinion': 'poor', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m using this device to run a small etsy business and its perfect for my needs .\n->i ' m using this device to run a small etsy business and its perfect for my needs .\n[{'aspect': 'device', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: they loved them and said they worked perfectly .\n->they loved them and said they worked perfectly .\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the screen is surprisingly poor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is surprisingly poor .\n->", + "output": "{\"text\": \"the screen is surprisingly poor .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n->All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n[{'aspect': 'pastas', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade lasagna', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I was very disappointed with this restaurant .\n->I was very disappointed with this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it took hours to restore to factory default settings , and it crashed once again days later .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit took hours to restore to factory default settings , and it crashed once again days later .\n->", + "output": "{\"text\": \"it took hours to restore to factory default settings , and it crashed once again days later .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is not what you are coming here for . . .\n->service is not what you are coming here for . . .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Waitstaff are very friendly .\n->Waitstaff are very friendly .\n[{'aspect': 'Waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is completely unacceptable for any computer to have this recurring issue , let alone a mid - tier performance gaming laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is completely unacceptable for any computer to have this recurring issue , let alone a mid - tier performance gaming laptop .\n->", + "output": "{\"text\": \"this is completely unacceptable for any computer to have this recurring issue , let alone a mid - tier performance gaming laptop .\", \"labels\": \"[{'aspect': 'mid - tier performance gaming laptop', 'opinion': 'unacceptable', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is by far my favorite place in the neighborhood .\n->This is by far my favorite place in the neighborhood .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n->In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i am still disastified even if this was a replacement\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am still disastified even if this was a replacement\n->", + "output": "{\"text\": \"i am still disastified even if this was a replacement\", \"labels\": \"[{'aspect': 'replacement', 'opinion': 'disastified', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was mediocre at best but it was the horrible service that made me vow never to go back .\n->The food was mediocre at best but it was the horrible service that made me vow never to go back .\n[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The pizza is delicious and the proprietor is one of the nicest in NYC .\n->The pizza is delicious and the proprietor is one of the nicest in NYC .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\n->", + "output": "{\"text\": \"the last straw happened recently when i put it to sleep and it just wouldn ' t wake up - or rather it would try , and then spend the next several hours in a repair / analyzing mode that didn ' t actually do anything .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the support website is incompetent .\n->the support website is incompetent .\n[{'aspect': 'support website', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n->The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i have had nothing but problems with it since i bought it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had nothing but problems with it since i bought it .\n->", + "output": "{\"text\": \"i have had nothing but problems with it since i bought it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n->the screen is great , for both media consumption and general computing / web browsing ; it offers a nice amount of local storage and solid performance via the intel processor and 4gb of ram , and it looks sharp , thanks to modern design queues and aluminum build materials .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'storage', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'intel processor', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: I have never before eaten 40 pieces of relatively good nigiri .\n->I have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: and finally today , 4 months after purchasing it , it has completely crashed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand finally today , 4 months after purchasing it , it has completely crashed .\n->", + "output": "{\"text\": \"and finally today , 4 months after purchasing it , it has completely crashed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: surprisingly to me , the tablet form has been better than expected for reading .\n->surprisingly to me , the tablet form has been better than expected for reading .\n[{'aspect': 'tablet form', 'opinion': 'surprisingly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'tablet form', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\n->Once you step into Cosette , you 're miraculously in a small , off-the-beaten path Parisian bistro .\n[{'aspect': 'Cosette', 'opinion': 'off-the-beaten', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the computer can not repair whatever disc issues it has .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer can not repair whatever disc issues it has .\n->", + "output": "{\"text\": \"the computer can not repair whatever disc issues it has .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery is all day amazing\n->battery is all day amazing\n[{'aspect': 'battery', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: Raga stands out with an interesting fusion of French and Indian cooking .\n->Raga stands out with an interesting fusion of French and Indian cooking .\n[{'aspect': 'fusion of French and Indian cooking', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this was the worst computer ever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was the worst computer ever .\n->", + "output": "{\"text\": \"this was the worst computer ever .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fried dumplings are GREAT !\n->The fried dumplings are GREAT !\n[{'aspect': 'fried dumplings', 'opinion': 'GREAT', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: first , it is hard to run more than 10 tabs open at any given time .\n->first , it is hard to run more than 10 tabs open at any given time .\n[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: do not buy this computer !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not buy this computer !\n->", + "output": "{\"text\": \"do not buy this computer !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n->Food is usually very good , though ocasionally I wondered about freshmess of raw vegatables in side orders .\n[{'aspect': 'raw vegatables', 'opinion': 'wondered', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i have not fiddled around much with the touchscreen yet , but it seems very responsive .\n->i have not fiddled around much with the touchscreen yet , but it seems very responsive .\n[{'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: so far , it all works well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far , it all works well .\n->", + "output": "{\"text\": \"so far , it all works well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all in all , this is a great laptop for the casual user , especially at this price point .\n->all in all , this is a great laptop for the casual user , especially at this price point .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: battery life is great for reviewing adobe forms and web surfing , pretty good for youtube videos .\n->battery life is great for reviewing adobe forms and web surfing , pretty good for youtube videos .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntoday was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\n->", + "output": "{\"text\": \"today was my heaviest use of the new computer , and no less than 20 times have i had to undo / unscroll / unselect / unzoom .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was an excellent machine for the money and it also got me spoiled with its touch screen .\n->it was an excellent machine for the money and it also got me spoiled with its touch screen .\n[{'aspect': 'machine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'touch screen', 'opinion': 'spoiled', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: this laptop ' s tocuhpad is by far the worst i have ever used .\n->this laptop ' s tocuhpad is by far the worst i have ever used .\n[{'aspect': \"laptop ' s tocuhpad\", 'opinion': 'worst', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: multiple system crashes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmultiple system crashes .\n->", + "output": "{\"text\": \"multiple system crashes .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'two types of sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the track pad is excellent .\n->the track pad is excellent .\n[{'aspect': 'track pad', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: but let me not recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut let me not recommend it .\n->", + "output": "{\"text\": \"but let me not recommend it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really recommend the very simple unda ( egg ) rolls .\n->i really recommend the very simple unda ( egg ) rolls .\n[{'aspect': 'unda ( egg ) rolls', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'unda ( egg ) rolls', 'opinion': 'simple', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this tiny restaurant is as cozy as it gets , with that certain parisian flair .\n->this tiny restaurant is as cozy as it gets , with that certain parisian flair .\n[{'aspect': 'restaurant', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: was installing an update and the computer went black .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwas installing an update and the computer went black .\n->", + "output": "{\"text\": \"was installing an update and the computer went black .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do not buy this laptop .\n->do not buy this laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the sound and screen quality is low .\n->the sound and screen quality is low .\n[{'aspect': 'sound', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'screen quality', 'opinion': 'low', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: i bought this originally a few months back , died within a week .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this originally a few months back , died within a week .\n->", + "output": "{\"text\": \"i bought this originally a few months back , died within a week .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great place , great value .\n->great place , great value .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: there is a buzzing sound that comes from inside of the keyboard .\n->there is a buzzing sound that comes from inside of the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: so called msi tech support , went through the troubleshooting and of course they could not fix it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso called msi tech support , went through the troubleshooting and of course they could not fix it .\n->", + "output": "{\"text\": \"so called msi tech support , went through the troubleshooting and of course they could not fix it .\", \"labels\": \"[{'aspect': 'msi tech support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: apple should ashamed to be associated with this cheap , lousy , clearly inferior , plastic nightmare - but then again , maybe that is exactly what they do want , so profits soar .\n->apple should ashamed to be associated with this cheap , lousy , clearly inferior , plastic nightmare - but then again , maybe that is exactly what they do want , so profits soar .\n[{'aspect': 'apple', 'opinion': 'ashamed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'inferior', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i don ' t do much more than google searches , so the lower end model works great for me .\n->i don ' t do much more than google searches , so the lower end model works great for me .\n[{'aspect': 'model', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: eventually it bricked and died 3 days after any possible refund could be made .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neventually it bricked and died 3 days after any possible refund could be made .\n->", + "output": "{\"text\": \"eventually it bricked and died 3 days after any possible refund could be made .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza was great .\n->the pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: they should have called it mascarpone with chocolate chips - good but a far cry from what the name implies .\n->they should have called it mascarpone with chocolate chips - good but a far cry from what the name implies .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n->", + "output": "{\"text\": \"if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->The food is yummy , especially their cooked-to-perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked-to-perfection', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fries', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen on this looks great , the bezels aren ' t noticeable .\n->the screen on this looks great , the bezels aren ' t noticeable .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': \"' t noticeable\", 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: it ' s a very fast laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a very fast laptop .\n->", + "output": "{\"text\": \"it ' s a very fast laptop .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's great to go for a quick lunch either alone or with a friend .\n->It 's great to go for a quick lunch either alone or with a friend .\n[{'aspect': 'lunch', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it feels very nice and i also really like the backlit keys in the dark .\n->it feels very nice and i also really like the backlit keys in the dark .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'backlit keys', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: i use it for gaming and it runs rocket league at max graphics and it looks amazing !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use it for gaming and it runs rocket league at max graphics and it looks amazing !\n->", + "output": "{\"text\": \"i use it for gaming and it runs rocket league at max graphics and it looks amazing !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n->Its location is good and the fact that Hutner College is near and their prices are very reasonable , makes students go back to Suan again and again .\n[{'aspect': 'location', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Suan', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Good food .\n->Good food .\n[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i definitely recommend this if you are looking for a good gaming laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni definitely recommend this if you are looking for a good gaming laptop .\n->", + "output": "{\"text\": \"i definitely recommend this if you are looking for a good gaming laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he was ecstatic at the power , the screen , the layout - pretty much everything .\n->he was ecstatic at the power , the screen , the layout - pretty much everything .\n[{'aspect': 'power', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'screen', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'ecstatic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: They pray to their Food Gods to make them into a good pizza like VT 's .\n->They pray to their Food Gods to make them into a good pizza like VT 's .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i have had to wipe this pc about 8 times in the short 2 months i have owned it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had to wipe this pc about 8 times in the short 2 months i have owned it .\n->", + "output": "{\"text\": \"i have had to wipe this pc about 8 times in the short 2 months i have owned it .\", \"labels\": \"[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\n->they never brought us complimentary noodles , ignored repeated requests for sugar , and threw our dishes on the table .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: I 've been to several places for Dim Sum and this has got to be the WORST .\n->I 've been to several places for Dim Sum and this has got to be the WORST .\n[{'aspect': 'Dim Sum', 'opinion': 'WORST .', 'polarity': 'negative', 'category': 'NULL'}]\ntext: its functionality is fine and for a gaming computer it performs well , don ' t connect an hdmi cable to it though , this will fry it , also don ' t turn it off cause you have a good chance of having to reset your laptop to factory settings .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits functionality is fine and for a gaming computer it performs well , don ' t connect an hdmi cable to it though , this will fry it , also don ' t turn it off cause you have a good chance of having to reset your laptop to factory settings .\n->", + "output": "{\"text\": \"its functionality is fine and for a gaming computer it performs well , don ' t connect an hdmi cable to it though , this will fry it , also don ' t turn it off cause you have a good chance of having to reset your laptop to factory settings .\", \"labels\": \"[{'aspect': 'gaming computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n->he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n[{'aspect': 'uni hand roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n->i 've been back to nha trang literally a hundred times for the beef cubes - they 're that good .\n[{'aspect': 'beef cubes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i can no longer return it and have wasted $ 850 , the support refuses to get back in touch or provide any form of civility .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can no longer return it and have wasted $ 850 , the support refuses to get back in touch or provide any form of civility .\n->", + "output": "{\"text\": \"i can no longer return it and have wasted $ 850 , the support refuses to get back in touch or provide any form of civility .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n->despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'done to perfection', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the only thing i don ' t like is that the power button sits beside the delete key .\n->the only thing i don ' t like is that the power button sits beside the delete key .\n[{'aspect': 'power button', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i used this laptop mainly to play counterstrike and pubg which it did just fine until something got corrupted with the optane drive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni used this laptop mainly to play counterstrike and pubg which it did just fine until something got corrupted with the optane drive .\n->", + "output": "{\"text\": \"i used this laptop mainly to play counterstrike and pubg which it did just fine until something got corrupted with the optane drive .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'optane drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'GRAPHICS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n->the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n[{'aspect': 'startup time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: it also has pretty decent i / o with two usb 3 .\n->it also has pretty decent i / o with two usb 3 .\n[{'aspect': 'i / o', 'opinion': 'decent', 'polarity': 'positive', 'category': 'PORTS#CONNECTIVITY'}]\ntext: however , not to impressed with msi ' s customer support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , not to impressed with msi ' s customer support .\n->", + "output": "{\"text\": \"however , not to impressed with msi ' s customer support .\", \"labels\": \"[{'aspect': \"msi ' s customer support\", 'opinion': 'not to impressed', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is always fresh . . .\n->the food is always fresh . . .\n[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: every app i have downloaded from the google app store has worked perfectly .\n->every app i have downloaded from the google app store has worked perfectly .\n[{'aspect': 'app', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: playing a movie couldn ' t even last the duration of the movie on the battery , again , disappointing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplaying a movie couldn ' t even last the duration of the movie on the battery , again , disappointing .\n->", + "output": "{\"text\": \"playing a movie couldn ' t even last the duration of the movie on the battery , again , disappointing .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Moules were excellent , lobster ravioli was VERY salty !\n->Moules were excellent , lobster ravioli was VERY salty !\n[{'aspect': 'Moules', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lobster ravioli', 'opinion': 'salty', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i gave it 3 out of 5 stars , because there is no sd card slot !\n->i gave it 3 out of 5 stars , because there is no sd card slot !\n[{'aspect': 'sd card slot', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\n->", + "output": "{\"text\": \"its a sleek racecar of a machine and ran the new insurgency : sandstorm with ease .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is no nonsense .\n->The staff is no nonsense .\n[{'aspect': 'staff', 'opinion': 'no nonsense', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The place was quiet and delightful .\n->The place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: if you buy this machine - be prepared for it to break .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you buy this machine - be prepared for it to break .\n->", + "output": "{\"text\": \"if you buy this machine - be prepared for it to break .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Bagels are ok , but be sure not to make any special requests !\n->Bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'Bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: they have a huge selection of different cream cheeses and all of their salads are great .\n->they have a huge selection of different cream cheeses and all of their salads are great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: very good looking and fast laptop if only if it would stop crashing and having to reset back to default settings .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good looking and fast laptop if only if it would stop crashing and having to reset back to default settings .\n->", + "output": "{\"text\": \"very good looking and fast laptop if only if it would stop crashing and having to reset back to default settings .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good looking', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Another plus is most of the entrees are approx .\n->Another plus is most of the entrees are approx .\n[{'aspect': 'entrees', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: While there are plenty of places to go for a good corned beef sandwich , Katz 's has a charm about it .\n->While there are plenty of places to go for a good corned beef sandwich , Katz 's has a charm about it .\n[{'aspect': 'corned beef sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it randomly shuts down all programs running and goes back to the desktop like nothing was going on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit randomly shuts down all programs running and goes back to the desktop like nothing was going on .\n->", + "output": "{\"text\": \"it randomly shuts down all programs running and goes back to the desktop like nothing was going on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only downside . . . they only take cash which is ok if you know about it ahead of time .\n->the only downside . . . they only take cash which is ok if you know about it ahead of time .\n[{'aspect': 'NULL', 'opinion': 'downside', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Delivery service is great too .\n->Delivery service is great too .\n[{'aspect': 'Delivery service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: stay far away from this laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstay far away from this laptop !\n->", + "output": "{\"text\": \"stay far away from this laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place had all the trimmings and i mean all .\n->this place had all the trimmings and i mean all .\n[{'aspect': 'trimmings', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: - i wish the sound quality was better .\n->- i wish the sound quality was better .\n[{'aspect': 'sound quality', 'opinion': 'better', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: only been using it for about a week , continuously getting the error that it can not connect to speakers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly been using it for about a week , continuously getting the error that it can not connect to speakers .\n->", + "output": "{\"text\": \"only been using it for about a week , continuously getting the error that it can not connect to speakers .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it feels amazing and the travel is perfect .\n->it feels amazing and the travel is perfect .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'travel', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#PORTABILITY'}]\nExample:\ntext: Not only is the cuisine the best around , the service has always been attentive and charming .\n->Not only is the cuisine the best around , the service has always been attentive and charming .\n[{'aspect': 'cuisine', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'charming', 'polarity': 'positive', 'category': 'NULL'}]\ntext: note that this laptop is only 45 daysish old .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnote that this laptop is only 45 daysish old .\n->", + "output": "{\"text\": \"note that this laptop is only 45 daysish old .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the people who want great food plus great service , Roxy is a place to AVOID !\n->For the people who want great food plus great service , Roxy is a place to AVOID !\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n->the hot dogs are top notch , and they ' re slamwich is amazing !\n[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: cpu and gpu are good , ram is good and i like the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncpu and gpu are good , ram is good and i like the keyboard .\n->", + "output": "{\"text\": \"cpu and gpu are good , ram is good and i like the keyboard .\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'gpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'GRAPHICS#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop itself seemed fine at first .\n->the laptop itself seemed fine at first .\n[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it 's a perfect place to have a amazing indian food .\n->it 's a perfect place to have a amazing indian food .\n[{'aspect': 'indian food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: port options are nice as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nport options are nice as well .\n->", + "output": "{\"text\": \"port options are nice as well .\", \"labels\": \"[{'aspect': 'port options', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ca n ' t go wrong here .\n->you ca n ' t go wrong here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: dead pixel on display on arrival\n->dead pixel on display on arrival\n[{'aspect': 'display', 'opinion': 'dead', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: the picture quality seems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe picture quality seems .\n->", + "output": "{\"text\": \"the picture quality seems .\", \"labels\": \"[{'aspect': 'picture quality', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n->i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: The food was so-so .\n->The food was so-so .\n[{'aspect': 'food', 'opinion': 'so-so', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: fans can get loud .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfans can get loud .\n->", + "output": "{\"text\": \"fans can get loud .\", \"labels\": \"[{'aspect': 'fans', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the support of the play store in beta is also a nice addition like icing on a cake .\n->the support of the play store in beta is also a nice addition like icing on a cake .\n[{'aspect': 'support of the play store', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: I would highly recommand requesting a table by the window .\n->I would highly recommand requesting a table by the window .\n[{'aspect': 'table by the window', 'opinion': 'recommand', 'polarity': 'positive', 'category': 'NULL'}]\ntext: speakers sound tinny .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeakers sound tinny .\n->", + "output": "{\"text\": \"speakers sound tinny .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'tinny', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n->i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\nExample:\ntext: Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n->Joya used to be a cool spot with decent food and a colorful - if not relaxed - atmosphere .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'colorful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this laptop is my first time using the intel optane thing and after this i can not recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop is my first time using the intel optane thing and after this i can not recommend it .\n->", + "output": "{\"text\": \"this laptop is my first time using the intel optane thing and after this i can not recommend it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n->i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' ve used this daily for nearly eight months and have been very happy with .\n->i ' ve used this daily for nearly eight months and have been very happy with .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it is freaking painful how long it can take games to load with that hard drive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is freaking painful how long it can take games to load with that hard drive .\n->", + "output": "{\"text\": \"it is freaking painful how long it can take games to load with that hard drive .\", \"labels\": \"[{'aspect': 'hard drive', 'opinion': 'painful', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will never buy an apple product again .\n->i will never buy an apple product again .\n[{'aspect': 'apple product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n->From the spectacular caviar to the hospitable waitstaff , I felt like royalty and enjoyed every second of it .\n[{'aspect': 'caviar', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'caviar', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'hospitable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n->", + "output": "{\"text\": \"i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cons : no caps lock key ( still haven ' t found it , help ! )\n->cons : no caps lock key ( still haven ' t found it , help ! )\n[{'aspect': 'caps lock key', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: never has it run out of power while on battery .\n->never has it run out of power while on battery .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: its battery life is really good , and the led lights are nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits battery life is really good , and the led lights are nice .\n->", + "output": "{\"text\": \"its battery life is really good , and the led lights are nice .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'led lights', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They have a huge selection of different cream cheeses and all of their salads are great .\n->They have a huge selection of different cream cheeses and all of their salads are great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my friend got the mushroom pizza which tasted better .\n->my friend got the mushroom pizza which tasted better .\n[{'aspect': 'mushroom pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: however , after weeks of having this laptop , its outgoes a bunch of problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , after weeks of having this laptop , its outgoes a bunch of problems .\n->", + "output": "{\"text\": \"however , after weeks of having this laptop , its outgoes a bunch of problems .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: that is awesome .\n->that is awesome .\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: service was quick .\n->service was quick .\n[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the 1st problem that occurred is it kept shutting on and off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 1st problem that occurred is it kept shutting on and off .\n->", + "output": "{\"text\": \"the 1st problem that occurred is it kept shutting on and off .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was slow had to wait to order and get food although not crowded .\n->Service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'lot', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 2nd problem was the game card was damaged and 3rd problem was youtube wouldn ' t work again , and the search bar on the laptop wasnt working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n2nd problem was the game card was damaged and 3rd problem was youtube wouldn ' t work again , and the search bar on the laptop wasnt working .\n->", + "output": "{\"text\": \"2nd problem was the game card was damaged and 3rd problem was youtube wouldn ' t work again , and the search bar on the laptop wasnt working .\", \"labels\": \"[{'aspect': 'game card', 'opinion': 'damaged', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'youtube', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'search bar', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ingredients are organic which is a real plus for me .\n->Ingredients are organic which is a real plus for me .\n[{'aspect': 'Ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n->However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n[{'aspect': 'management', 'opinion': 'changed', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'door', 'opinion': 'great big', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this bios is horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis bios is horrible .\n->", + "output": "{\"text\": \"this bios is horrible .\", \"labels\": \"[{'aspect': 'bios', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n->I choose to go with one of the special , the braised lamb shank in red wine , which was excellent .\n[{'aspect': 'braised lamb shank in red wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the best !\n->the best !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: what was even worse was the customer service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat was even worse was the customer service .\n->", + "output": "{\"text\": \"what was even worse was the customer service .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'worse', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is something to reconsidered if one to buy the chromebook for doing homework and a lot of typing .\n->this is something to reconsidered if one to buy the chromebook for doing homework and a lot of typing .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: less than three minutes passed before i found myself doubled over the toilet .\n->less than three minutes passed before i found myself doubled over the toilet .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: purchased it the first time , thought it was faulty hardware , returned for a replacement of the exact same , and it had the exact problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npurchased it the first time , thought it was faulty hardware , returned for a replacement of the exact same , and it had the exact problems .\n->", + "output": "{\"text\": \"purchased it the first time , thought it was faulty hardware , returned for a replacement of the exact same , and it had the exact problems .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is great and reasonably priced .\n->The food is great and reasonably priced .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: everything on the menu is great .\n->everything on the menu is great .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i bought this product for black friday and i have been using it steadily since then .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this product for black friday and i have been using it steadily since then .\n->", + "output": "{\"text\": \"i bought this product for black friday and i have been using it steadily since then .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'steadily', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n->The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The fish is fresh but the variety of fish is nothing out of ordinary .\n->The fish is fresh but the variety of fish is nothing out of ordinary .\n[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'variety of fish', 'opinion': 'ordinary', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\n->", + "output": "{\"text\": \"i have been using it for about a month and the screen already is having little white streaks on it without anything to cause it .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: gross food \u2013 wow -\n->gross food \u2013 wow -\n[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the dancing , white river and millenium rolls are musts .\n->the dancing , white river and millenium rolls are musts .\n[{'aspect': 'dancing , white river and millenium rolls', 'opinion': 'musts', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the inside is great but i feel like it won ' t last long enough before the outside crumbles .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe inside is great but i feel like it won ' t last long enough before the outside crumbles .\n->", + "output": "{\"text\": \"the inside is great but i feel like it won ' t last long enough before the outside crumbles .\", \"labels\": \"[{'aspect': 'inside', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s one of our favorite places to eat in ny .\n->it ' s one of our favorite places to eat in ny .\n[{'aspect': 'NULL', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the computer was a 50 - 50 chance to boot from day one , but we figured it needed to run updates and get settled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer was a 50 - 50 chance to boot from day one , but we figured it needed to run updates and get settled .\n->", + "output": "{\"text\": \"the computer was a 50 - 50 chance to boot from day one , but we figured it needed to run updates and get settled .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: plus the screen is bland .\n->plus the screen is bland .\n[{'aspect': 'screen', 'opinion': 'bland', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: very ` ` normal indian food ' ' , but done really well .\n->very ` ` normal indian food ' ' , but done really well .\n[{'aspect': 'indian food', 'opinion': 'normal', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'indian food', 'opinion': 'well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this was a christmas present and now we ' re scrambling because it sucks .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis was a christmas present and now we ' re scrambling because it sucks .\n->", + "output": "{\"text\": \"this was a christmas present and now we ' re scrambling because it sucks .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus support is responsive but ineffective .\n->asus support is responsive but ineffective .\n[{'aspect': 'asus support', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus support', 'opinion': 'ineffective', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: well designed , nice fit and finish , and the build quality seems exceptional .\n->well designed , nice fit and finish , and the build quality seems exceptional .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'build quality', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: suddenly the laptop goes to sleep and doesn ' t wake up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuddenly the laptop goes to sleep and doesn ' t wake up .\n->", + "output": "{\"text\": \"suddenly the laptop goes to sleep and doesn ' t wake up .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there are certain very basic tasks that this computer can do .\n->there are certain very basic tasks that this computer can do .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food here does a great service to the name ( cantonese that is . . . ) .\n->the food here does a great service to the name ( cantonese that is . . . ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\n->", + "output": "{\"text\": \"i bought this in august and i have had to completely wipe 2x and rollback after every critical update since november .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one special roll and one regular roll is enough to fill you up , but save room for dessert !\n->one special roll and one regular roll is enough to fill you up , but save room for dessert !\n[{'aspect': 'dessert', 'opinion': 'save room', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'special roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'regular roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: Drinks way over priced .\n->Drinks way over priced .\n[{'aspect': 'Drinks', 'opinion': 'over priced', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'over', 'polarity': 'negative', 'category': 'NULL'}]\ntext: definitely has issues with windows 10 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely has issues with windows 10 .\n->", + "output": "{\"text\": \"definitely has issues with windows 10 .\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bread and lamb chops I had before the meal were quite good , however .\n->The bread and lamb chops I had before the meal were quite good , however .\n[{'aspect': 'bread', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb chops', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: all in all , the food was great ( except for the dessserts ) .\n->all in all , the food was great ( except for the dessserts ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessserts', 'opinion': 'except', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: system shutdown problems every month .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsystem shutdown problems every month .\n->", + "output": "{\"text\": \"system shutdown problems every month .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the crunchy tuna , it is to die for .\n->Try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Please try the Filet Mignon , its just the most tender piece ever .\n->Please try the Filet Mignon , its just the most tender piece ever .\n[{'aspect': 'Filet Mignon', 'opinion': 'tender', 'polarity': 'positive', 'category': 'NULL'}]\ntext: tech support is useless .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntech support is useless .\n->", + "output": "{\"text\": \"tech support is useless .\", \"labels\": \"[{'aspect': 'tech support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Light , refreshing summer rolls ( not fried ) remind me of Vietnamese places in Paris .\n->Light , refreshing summer rolls ( not fried ) remind me of Vietnamese places in Paris .\n[{'aspect': 'summer rolls', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i highly recommend the fish tacos , everything else was ok .\n->i highly recommend the fish tacos , everything else was ok .\n[{'aspect': 'fish tacos', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: update : i have had this computer for about 3 months now , and it is full of problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupdate : i have had this computer for about 3 months now , and it is full of problems .\n->", + "output": "{\"text\": \"update : i have had this computer for about 3 months now , and it is full of problems .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doubles as an android tablet and so far the experience with running android apps has been good .\n->it doubles as an android tablet and so far the experience with running android apps has been good .\n[{'aspect': 'android apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: everyone was more then happy with his choices .\n->everyone was more then happy with his choices .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i got this laptop 2 days ago and it says plugged in , not charged .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got this laptop 2 days ago and it says plugged in , not charged .\n->", + "output": "{\"text\": \"i got this laptop 2 days ago and it says plugged in , not charged .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: during the course of the past 3 months , the chef and staff changed and it was not for the better .\n->during the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'not for the better', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: we ordered some beef and noodle soup dishes from the thai section of the menu but nothing we got was thai .\n->we ordered some beef and noodle soup dishes from the thai section of the menu but nothing we got was thai .\n[{'aspect': 'beef and noodle soup dishes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i replug and restarted the laptop 3 times and it still does n ' t work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni replug and restarted the laptop 3 times and it still does n ' t work .\n->", + "output": "{\"text\": \"i replug and restarted the laptop 3 times and it still does n ' t work .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so $ 500 bucks down the drain as i ' m sure that isn ' t covered by any warranties .\n->so $ 500 bucks down the drain as i ' m sure that isn ' t covered by any warranties .\n[{'aspect': 'warranties', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'WARRANTY#QUALITY'}]\nExample:\ntext: yes , it ' s absolute garbage .\n->yes , it ' s absolute garbage .\n[{'aspect': 'NULL', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: the fan is almost always on even if you have it set to automatic , although it ' s not loud .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fan is almost always on even if you have it set to automatic , although it ' s not loud .\n->", + "output": "{\"text\": \"the fan is almost always on even if you have it set to automatic , although it ' s not loud .\", \"labels\": \"[{'aspect': 'fan', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n->with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n[{'aspect': 'cpu', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'solid', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'good', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n->bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n[{'aspect': 'specs', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: microphone is really low .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmicrophone is really low .\n->", + "output": "{\"text\": \"microphone is really low .\", \"labels\": \"[{'aspect': 'microphone', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good touchpad\n->good touchpad\n[{'aspect': 'touchpad', 'opinion': 'good', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: i just fear for the long term ruggedness of the exterior .\n->i just fear for the long term ruggedness of the exterior .\n[{'aspect': 'exterior', 'opinion': 'fear', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: absolutely terrible quality control to not even get past the initial boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nabsolutely terrible quality control to not even get past the initial boot .\n->", + "output": "{\"text\": \"absolutely terrible quality control to not even get past the initial boot .\", \"labels\": \"[{'aspect': 'quality control', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fabulous food - if the front of house staff do n ' t put you off \u2013\n->fabulous food - if the front of house staff do n ' t put you off \u2013\n[{'aspect': 'food', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'front of house staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: A touch more jalapeno heat for contrast and it would have been very good indeed .\n->A touch more jalapeno heat for contrast and it would have been very good indeed .\n[{'aspect': 'jalapeno', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: had purchase this for my child to surf the net ; i ' d noticed ( and i can easily reproduce the problem ) , whenever her game needs to download an update , it seems like no data is coming down , while the harddrive activity ( read and write ) is very high .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhad purchase this for my child to surf the net ; i ' d noticed ( and i can easily reproduce the problem ) , whenever her game needs to download an update , it seems like no data is coming down , while the harddrive activity ( read and write ) is very high .\n->", + "output": "{\"text\": \"had purchase this for my child to surf the net ; i ' d noticed ( and i can easily reproduce the problem ) , whenever her game needs to download an update , it seems like no data is coming down , while the harddrive activity ( read and write ) is very high .\", \"labels\": \"[{'aspect': 'harddrive activity', 'opinion': 'high', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes , under heavy loads , the bottom of the c302 gets warm to the touch , but not to the point of pain .\n->sometimes , under heavy loads , the bottom of the c302 gets warm to the touch , but not to the point of pain .\n[{'aspect': 'c302', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FANS&COOLING#QUALITY'}]\nExample:\ntext: Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n->Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n[{'aspect': 'space', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: not a good quality laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot a good quality laptop .\n->", + "output": "{\"text\": \"not a good quality laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'not a good', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in about 12 minutes , the thing is gone .\n->in about 12 minutes , the thing is gone .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my friend got the mushroom pizza which tasted better .\n->my friend got the mushroom pizza which tasted better .\n[{'aspect': 'mushroom pizza', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this laptop has good hardware specs , but the screen has very poor color coverage : 59 % .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop has good hardware specs , but the screen has very poor color coverage : 59 % .\n->", + "output": "{\"text\": \"this laptop has good hardware specs , but the screen has very poor color coverage : 59 % .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is by far my favorite place in the neighborhood .\n->this is by far my favorite place in the neighborhood .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Great food , great prices , great service .\n->Great food , great prices , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n->", + "output": "{\"text\": \"it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i pray it stays open forever .\n->i pray it stays open forever .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: excellent for those uses .\n->excellent for those uses .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: i returned this one as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni returned this one as well .\n->", + "output": "{\"text\": \"i returned this one as well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: quacamole at pacifico is yummy , as are the wings with chimmichuri .\n->quacamole at pacifico is yummy , as are the wings with chimmichuri .\n[{'aspect': 'quacamole', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wings with chimmichuri', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: they ' re like a little - known gem , practically unknown in my area .\n->they ' re like a little - known gem , practically unknown in my area .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it is not a great build .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is not a great build .\n->", + "output": "{\"text\": \"it is not a great build .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not a great', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The price was extremely reasonable for the appetizers and food we ate .\n->The price was extremely reasonable for the appetizers and food we ate .\n[{'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: pizza - the only pizza in nyc that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->pizza - the only pizza in nyc that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'pizza', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'freshly baked', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i had high hopes but sadly i had to return the second as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had high hopes but sadly i had to return the second as well .\n->", + "output": "{\"text\": \"i had high hopes but sadly i had to return the second as well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'sadly', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the Pad Thai , it 's fabulous and their prices are so cheap !\n->Try the Pad Thai , it 's fabulous and their prices are so cheap !\n[{'aspect': 'Pad Thai', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pad Thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n->the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n[{'aspect': 'NULL', 'opinion': 'not usable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: computer was good but i would not recommend for battery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomputer was good but i would not recommend for battery .\n->", + "output": "{\"text\": \"computer was good but i would not recommend for battery .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a weakness is the chicken in the salads .\n->a weakness is the chicken in the salads .\n[{'aspect': 'chicken in the salads', 'opinion': 'weakness', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: also upon turning it on , i got a blue screen .\n->also upon turning it on , i got a blue screen .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: to bad the battery did n ' t work out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto bad the battery did n ' t work out .\n->", + "output": "{\"text\": \"to bad the battery did n ' t work out .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'bad', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were drawn into the belly dancing show that captivated the crowd .\n->we were drawn into the belly dancing show that captivated the crowd .\n[{'aspect': 'belly dancing show', 'opinion': 'captivated', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n->the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: when it works , it works well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen it works , it works well .\n->", + "output": "{\"text\": \"when it works , it works well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - fast boot up ( 3 seconds )\n->- fast boot up ( 3 seconds )\n[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i had the thai style fried sea bass . . . which was very good .\n->i had the thai style fried sea bass . . . which was very good .\n[{'aspect': 'thai style fried sea bass', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: but i ' ve had consistent issues with this laptop since i ' ve bought it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut i ' ve had consistent issues with this laptop since i ' ve bought it .\n->", + "output": "{\"text\": \"but i ' ve had consistent issues with this laptop since i ' ve bought it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fish was overdone .\n->fish was overdone .\n[{'aspect': 'fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i have had no issues with it since i bought it i would highly recommend it .\n->i have had no issues with it since i bought it i would highly recommend it .\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: * * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n->", + "output": "{\"text\": \"* * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: had it for a week now and still finding things it can not do .\n->had it for a week now and still finding things it can not do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - excellent keyboard in all aspects - feel , rigidity , and backlight\n->- excellent keyboard in all aspects - feel , rigidity , and backlight\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\n->", + "output": "{\"text\": \"what is the point of downloading games , files , anything , when a couple of weeks later it will crash and fail to recover !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fail', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: somehow working the italian charm with constant mille grazie does not constitute proper service .\n->somehow working the italian charm with constant mille grazie does not constitute proper service .\n[{'aspect': 'service', 'opinion': 'not constitute proper', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the c302 m5 ( the best processor i could find so far , is a very good chromebook .\n->the c302 m5 ( the best processor i could find so far , is a very good chromebook .\n[{'aspect': 'm5', 'opinion': 'best', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'processor', 'opinion': 'best', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i bought this because it had a good price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this because it had a good price .\n->", + "output": "{\"text\": \"i bought this because it had a good price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really do like this computer , however the description is wrong .\n->i really do like this computer , however the description is wrong .\n[{'aspect': 'computer', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'description', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: then suddenly it needs a software update which made my laptop crash .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen suddenly it needs a software update which made my laptop crash .\n->", + "output": "{\"text\": \"then suddenly it needs a software update which made my laptop crash .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they refuse to seat parties of 3 or more on weekends .\n->they refuse to seat parties of 3 or more on weekends .\n[{'aspect': 'NULL', 'opinion': 'refuse', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the laptop does n ' t work .\n->the laptop does n ' t work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: the computer literally blue screened on the second day because system 32 was corrupt .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer literally blue screened on the second day because system 32 was corrupt .\n->", + "output": "{\"text\": \"the computer literally blue screened on the second day because system 32 was corrupt .\", \"labels\": \"[{'aspect': 'system 32', 'opinion': 'corrupt', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff offers impeccable service .\n->the staff offers impeccable service .\n[{'aspect': 'staff', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n->my main concern was the sanity of the food that was being sent out to myself and others , but i would be lying is i said that as someone who has worked in restaurants since the age of fifteen i was expecting at least a minimal effort on the part of the restaurant to amend the situation .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: when it does run it runs great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen it does run it runs great .\n->", + "output": "{\"text\": \"when it does run it runs great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: has nice features for the price and nice video for streaming movies .\n->has nice features for the price and nice video for streaming movies .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'video', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n->the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n[{'aspect': 'service', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: do not buy this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not buy this .\n->", + "output": "{\"text\": \"do not buy this .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There is actually space to breathe and the decor sets the tone for an intimate dinner .\n->There is actually space to breathe and the decor sets the tone for an intimate dinner .\n[{'aspect': 'dinner', 'opinion': 'intimate', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i had the best ravioli ever .\n->i had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: will never buy an msi product again , and will tell every person i know to stay far away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill never buy an msi product again , and will tell every person i know to stay far away .\n->", + "output": "{\"text\": \"will never buy an msi product again , and will tell every person i know to stay far away .\", \"labels\": \"[{'aspect': 'msi product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will never be back .\n->will never be back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we thought the dessert would be better , wrong !\n->we thought the dessert would be better , wrong !\n[{'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: it stopped working a week after i recieved it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit stopped working a week after i recieved it .\n->", + "output": "{\"text\": \"it stopped working a week after i recieved it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Sauce was watery and the food did n't have much flavor .\n->Sauce was watery and the food did n't have much flavor .\n[{'aspect': 'Sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: but just at first glance , this thing is top quality .\n->but just at first glance , this thing is top quality .\n[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: don ' t recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndon ' t recommend it .\n->", + "output": "{\"text\": \"don ' t recommend it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': \"' t recommend\", 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n->the reason for 3 stars is due to that i have a rickety feeling when touching middle of the right side frame of the screen , it doesn ' t affect to screen , i am not sure it is faulty or normal , kind of uncomfortable to hold in tablet mode .\n[{'aspect': 'tablet mode', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: it ' s to die for !\n->it ' s to die for !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\n->", + "output": "{\"text\": \"i bought this computer a bit over two months ago for my son ' s college and after installing the windows update , it crashed !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They offer the same menu but have creative drinks that are loaded with alcohol and cheeky names -- but they do cost you .\n->They offer the same menu but have creative drinks that are loaded with alcohol and cheeky names -- but they do cost you .\n[{'aspect': 'menu', 'opinion': 'same', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'creative', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n->the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'travel / feedback', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\ntext: do n ' t buy this laptop or brand .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo n ' t buy this laptop or brand .\n->", + "output": "{\"text\": \"do n ' t buy this laptop or brand .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'brand', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when we inquired about ports - the waitress listed off several but did not know taste variations or cost .\n->when we inquired about ports - the waitress listed off several but did not know taste variations or cost .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i love the feel of a lighter os and can do many tasks using google / web based apps .\n->i love the feel of a lighter os and can do many tasks using google / web based apps .\n[{'aspect': 'os', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: i barely do anything on it and it is a complete garbage can of a laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni barely do anything on it and it is a complete garbage can of a laptop .\n->", + "output": "{\"text\": \"i barely do anything on it and it is a complete garbage can of a laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so kudos to acer for the keyboard !\n->so kudos to acer for the keyboard !\n[{'aspect': 'keyboard', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'acer', 'opinion': 'kudos', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n->I LOVE their Thai noodles with shrimp and chicken and coconut juice is the MUST !\n[{'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai noodles with shrimp and chicken and coconut juice', 'opinion': 'MUST', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen will not work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen will not work .\n->", + "output": "{\"text\": \"the screen will not work .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sake menu should not be overlooked !\n->The sake menu should not be overlooked !\n[{'aspect': 'sake menu', 'opinion': 'overlooked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it integrates perfectly with my google account !\n->it integrates perfectly with my google account !\n[{'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: now the start up is failing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow the start up is failing .\n->", + "output": "{\"text\": \"now the start up is failing .\", \"labels\": \"[{'aspect': 'start up', 'opinion': 'failing', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->the service is excellent , the decor is great , and the food is delicious and comes in large portions .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: its dark , and cozy . . there is always jazz music playing when we go .\n->its dark , and cozy . . there is always jazz music playing when we go .\n[{'aspect': 'NULL', 'opinion': 'cozy . .', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'dark', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i wouldn ' t recommend this product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wouldn ' t recommend this product .\n->", + "output": "{\"text\": \"i wouldn ' t recommend this product .\", \"labels\": \"[{'aspect': 'product', 'opinion': \"' t recommend\", 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n->Barbecued codfish was gorgeously moist - as if poached - yet the fabulous texture was let down by curiously bland seasoning - a spice rub might have overwhelmed , however herb mix or other sauce would have done much to enhance .\n[{'aspect': 'Barbecued codfish', 'opinion': 'moist', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seasoning', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spice rub', 'opinion': 'overwhelmed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'herb mix', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'to enhance', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i would defiantly come back here again as one of my top choices .\n->i would defiantly come back here again as one of my top choices .\n[{'aspect': 'NULL', 'opinion': 'top choices', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it performs good for the price tag attached but it will have a booting problem atlest once every week and you have to format the machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit performs good for the price tag attached but it will have a booting problem atlest once every week and you have to format the machine .\n->", + "output": "{\"text\": \"it performs good for the price tag attached but it will have a booting problem atlest once every week and you have to format the machine .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the all - u - can - eat sushi is definitely in very poor quality .\n->the all - u - can - eat sushi is definitely in very poor quality .\n[{'aspect': 'all - u - can - eat sushi', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The place was quiet and delightful .\n->The place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i do not recommend this to anyone in the gaming labtop market .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do not recommend this to anyone in the gaming labtop market .\n->", + "output": "{\"text\": \"i do not recommend this to anyone in the gaming labtop market .\", \"labels\": \"[{'aspect': 'gaming labtop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good gaming computer .\n->good gaming computer .\n[{'aspect': 'gaming computer', 'opinion': 'good', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: you can even run time machine backups while the computer is sleeping now .\n->you can even run time machine backups while the computer is sleeping now .\n[{'aspect': 'machine backups', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupdate review : having had this laptop for a few months it has had a bad crash from a recent windows update .\n->", + "output": "{\"text\": \"update review : having had this laptop for a few months it has had a bad crash from a recent windows update .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'crash', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n->The ingredients taste fresher , the crust is thinner and crispier , the slice is less oily , and it 's never burnt like it occasionally is at Joe 's .\n[{'aspect': 'ingredients', 'opinion': 'fresher', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crust', 'opinion': 'crispier', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'slice', 'opinion': 'less oily', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i had nothing to lose since it was a paper weight otherwise .\n->i had nothing to lose since it was a paper weight otherwise .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: very let down by the reliability of this machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery let down by the reliability of this machine .\n->", + "output": "{\"text\": \"very let down by the reliability of this machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'let down', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n->if you like your music blasted and the system isnt that great and if you want to pay at least 100 dollar bottle minimun then you ' ll love it here .\n[{'aspect': 'bottle', 'opinion': 'love', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}, {'aspect': 'music', 'opinion': 'like', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n->the cold appetizer dishes taste like the way i remember them to taste when i was growing up in taiwan .\n[{'aspect': 'cold appetizer dishes', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i will never buy msi again and urge others to re - think about this brand .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will never buy msi again and urge others to re - think about this brand .\n->", + "output": "{\"text\": \"i will never buy msi again and urge others to re - think about this brand .\", \"labels\": \"[{'aspect': 'msi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hope this review can save others from the initial hassle i endured because the chromebook 3 looks terrific in any other way .\n->hope this review can save others from the initial hassle i endured because the chromebook 3 looks terrific in any other way .\n[{'aspect': 'chromebook 3', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i would highly recommend this product if you want to get into music production like myself .\n->i would highly recommend this product if you want to get into music production like myself .\n[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it gets used everyday for work all day long and some light gaming in the evening .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit gets used everyday for work all day long and some light gaming in the evening .\n->", + "output": "{\"text\": \"it gets used everyday for work all day long and some light gaming in the evening .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doesn ' t get super loud , but for normal usage situations it ' s fine .\n->it doesn ' t get super loud , but for normal usage situations it ' s fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Great wine , great food .\n->Great wine , great food .\n[{'aspect': 'wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so far the performance has been spectacular !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far the performance has been spectacular !\n->", + "output": "{\"text\": \"so far the performance has been spectacular !\", \"labels\": \"[{'aspect': 'performance', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n->Ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n->The atmosphere is nothing special , but it feels like a Sushi establishment in Tokyo .\n[{'aspect': 'atmosphere', 'opinion': 'nothing special', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 8 + lbs , this one is right under 5 so it makes it nice and portable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n8 + lbs , this one is right under 5 so it makes it nice and portable .\n->", + "output": "{\"text\": \"8 + lbs , this one is right under 5 so it makes it nice and portable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was average or above including some surprising tasty dishes .\n->The food was average or above including some surprising tasty dishes .\n[{'aspect': 'food', 'opinion': 'average or above', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we have always liked lenovo laptops .\n->we have always liked lenovo laptops .\n[{'aspect': 'lenovo laptops', 'opinion': 'liked', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: this is my first msi and if it stays great i will be a returning customer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my first msi and if it stays great i will be a returning customer .\n->", + "output": "{\"text\": \"this is my first msi and if it stays great i will be a returning customer .\", \"labels\": \"[{'aspect': 'msi', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n->Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n[{'aspect': 'group dinner', 'opinion': 'easy', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Good , dark atmosphere and the music is a nice touch .\n->Good , dark atmosphere and the music is a nice touch .\n[{'aspect': 'atmosphere', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'nice touch', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the battery life on the laptop is disappointing and the webcam doesn ' t work it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life on the laptop is disappointing and the webcam doesn ' t work it .\n->", + "output": "{\"text\": \"the battery life on the laptop is disappointing and the webcam doesn ' t work it .\", \"labels\": \"[{'aspect': 'battery life on the laptop', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'webcam', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am not fond of touchpads anyway , so probably not the best one to judge them .\n->i am not fond of touchpads anyway , so probably not the best one to judge them .\n[{'aspect': 'touchpads', 'opinion': 'not fond of', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i think the mbp ticks off more of the nice - to - have boxes for me than the xps13 overall .\n->i think the mbp ticks off more of the nice - to - have boxes for me than the xps13 overall .\n[{'aspect': 'mbp', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreceived this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n->", + "output": "{\"text\": \"received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the new retina is amazing and the speed is awesome .\n->the new retina is amazing and the speed is awesome .\n[{'aspect': 'retina', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'speed', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n->this quaint and romantic trattoria is at the top of my manhattan restaurant list .\n[{'aspect': 'trattoria', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'trattoria', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: msi should be ashamed at putting out products they know have issues with no intent of correcting the problem during the building and testing phase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmsi should be ashamed at putting out products they know have issues with no intent of correcting the problem during the building and testing phase .\n->", + "output": "{\"text\": \"msi should be ashamed at putting out products they know have issues with no intent of correcting the problem during the building and testing phase .\", \"labels\": \"[{'aspect': 'msi', 'opinion': 'ashamed', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'products', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great notebook .\n->this is a great notebook .\n[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the prices were cheap compared to the quality of service and food .\n->the prices were cheap compared to the quality of service and food .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: i can not recommend any products from this company and encourage everyone to avoid them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can not recommend any products from this company and encourage everyone to avoid them .\n->", + "output": "{\"text\": \"i can not recommend any products from this company and encourage everyone to avoid them .\", \"labels\": \"[{'aspect': 'products from this company', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s fast , connects quickly to wifi , and the screen is quite nice .\n->it ' s fast , connects quickly to wifi , and the screen is quite nice .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: and it was a very good price .\n->and it was a very good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: i really like the laptop and how fast it is and all the specs but , the internet started dropping only in this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really like the laptop and how fast it is and all the specs but , the internet started dropping only in this laptop .\n->", + "output": "{\"text\": \"i really like the laptop and how fast it is and all the specs but , the internet started dropping only in this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'internet', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not recommend any products from this company and encourage everyone to avoid them .\n->i can not recommend any products from this company and encourage everyone to avoid them .\n[{'aspect': 'products from this company', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the problem with it is that it freezes from time to time .\n->the problem with it is that it freezes from time to time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: good gaming computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood gaming computer .\n->", + "output": "{\"text\": \"good gaming computer .\", \"labels\": \"[{'aspect': 'gaming computer', 'opinion': 'good', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n->So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n[{'aspect': 'thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n->i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: for the price i paid , this laptop can ' t be beat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the price i paid , this laptop can ' t be beat .\n->", + "output": "{\"text\": \"for the price i paid , this laptop can ' t be beat .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this unit is a great compromise between powerful cpu and gpu with good battery life .\n->this unit is a great compromise between powerful cpu and gpu with good battery life .\n[{'aspect': 'cpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'gpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}, {'aspect': 'unit', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: My boyfriend had Prime Rib it was good .\n->My boyfriend had Prime Rib it was good .\n[{'aspect': 'Prime Rib', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: for $ 499 the laptop is a bargain , but you should only buy if you you plan to add an ssd in the near future .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor $ 499 the laptop is a bargain , but you should only buy if you you plan to add an ssd in the near future .\n->", + "output": "{\"text\": \"for $ 499 the laptop is a bargain , but you should only buy if you you plan to add an ssd in the near future .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'bargain', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n->we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'delight', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the track pad , which i don ' t use , is also highly responsive .\n->the track pad , which i don ' t use , is also highly responsive .\n[{'aspect': 'track pad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: + the colour is a beautiful grey and not purple as reviewed by other users\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n+ the colour is a beautiful grey and not purple as reviewed by other users\n->", + "output": "{\"text\": \"+ the colour is a beautiful grey and not purple as reviewed by other users\", \"labels\": \"[{'aspect': 'colour', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n->the keyboard was a bonus for me : i really like having the number pad for when working with spreadsheets and such , and this size laptop doesn ' t always have one .\n[{'aspect': 'number pad', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: setting it up is awkward because it ' s chrome .\n->setting it up is awkward because it ' s chrome .\n[{'aspect': 'chrome', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: + fingerprint sensor is accurate\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n+ fingerprint sensor is accurate\n->", + "output": "{\"text\": \"+ fingerprint sensor is accurate\", \"labels\": \"[{'aspect': 'fingerprint sensor', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just want to warn you all - do n ' t waste your time and money .\n->just want to warn you all - do n ' t waste your time and money .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: works good but right click on mouse pad wont wok have to use external mouse\n->works good but right click on mouse pad wont wok have to use external mouse\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: - power button next to delete button ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- power button next to delete button ?\n->", + "output": "{\"text\": \"- power button next to delete button ?\", \"labels\": \"[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great machine for all my needs .\n->great machine for all my needs .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - caps lock without a led notification , not so clever .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- caps lock without a led notification , not so clever .\n->", + "output": "{\"text\": \"- caps lock without a led notification , not so clever .\", \"labels\": \"[{'aspect': 'caps lock', 'opinion': 'not so clever', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: had purchase this for my child to surf the net ; i ' d noticed ( and i can easily reproduce the problem ) , whenever her game needs to download an update , it seems like no data is coming down , while the harddrive activity ( read and write ) is very high .\n->had purchase this for my child to surf the net ; i ' d noticed ( and i can easily reproduce the problem ) , whenever her game needs to download an update , it seems like no data is coming down , while the harddrive activity ( read and write ) is very high .\n[{'aspect': 'harddrive activity', 'opinion': 'high', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: I stumbled upon this great pizzeria as I explored my new neighborhood .\n->I stumbled upon this great pizzeria as I explored my new neighborhood .\n[{'aspect': 'pizzeria', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it does have an ips screen , battery life is going strong , and no touch pad issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit does have an ips screen , battery life is going strong , and no touch pad issues .\n->", + "output": "{\"text\": \"it does have an ips screen , battery life is going strong , and no touch pad issues .\", \"labels\": \"[{'aspect': 'ips screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'strong', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my husband and i have been sold on this from the first visit .\n->my husband and i have been sold on this from the first visit .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The banana tower is an amazing dessert as well .\n->The banana tower is an amazing dessert as well .\n[{'aspect': 'banana tower', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: definitely a steal at the price i bought this for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely a steal at the price i bought this for .\n->", + "output": "{\"text\": \"definitely a steal at the price i bought this for .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the ambiance and atmosphere were great , the food and service could have been a lot better .\n->While the ambiance and atmosphere were great , the food and service could have been a lot better .\n[{'aspect': 'ambiance', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The dining room is quietly elegant with no music to shout over -- how refreshing !\n->The dining room is quietly elegant with no music to shout over -- how refreshing !\n[{'aspect': 'dining room', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dining room', 'opinion': 'refreshing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ninitial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n->", + "output": "{\"text\": \"initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do not recommend this to anyone in the gaming labtop market .\n->i do not recommend this to anyone in the gaming labtop market .\n[{'aspect': 'gaming labtop', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: in short , this unit is a chromebook with a really nice display .\n->in short , this unit is a chromebook with a really nice display .\n[{'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: keyboard feels firm and no flex , screen is nice for the price range .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard feels firm and no flex , screen is nice for the price range .\n->", + "output": "{\"text\": \"keyboard feels firm and no flex , screen is nice for the price range .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'firm', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My boyfriend had Prime Rib it was good .\n->My boyfriend had Prime Rib it was good .\n[{'aspect': 'Prime Rib', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n->I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: good sound quality\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood sound quality\n->", + "output": "{\"text\": \"good sound quality\", \"labels\": \"[{'aspect': 'sound quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sound sort of sucks but i don ' t use it for music .\n->sound sort of sucks but i don ' t use it for music .\n[{'aspect': 'sound', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: The prices and ambience are especially great considering it 's in the West Village .\n->The prices and ambience are especially great considering it 's in the West Village .\n[{'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: horrible keyboard flex\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhorrible keyboard flex\n->", + "output": "{\"text\": \"horrible keyboard flex\", \"labels\": \"[{'aspect': 'keyboard flex', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love it !\n->love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the computer has been running the programs such as matlab , mathematics , diamond , among others without problem .\n->the computer has been running the programs such as matlab , mathematics , diamond , among others without problem .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the max screen brightness isn ' t very bright\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe max screen brightness isn ' t very bright\n->", + "output": "{\"text\": \"the max screen brightness isn ' t very bright\", \"labels\": \"[{'aspect': 'screen', 'opinion': \"' t very bright\", 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after years of using amazon for hundreds of orders , this is my very first negative review :\n->after years of using amazon for hundreds of orders , this is my very first negative review :\n[{'aspect': 'amazon', 'opinion': 'negative', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n->The menu is very limited - i think we counted 4 or 5 entrees .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\ntext: no backlit keyboard\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno backlit keyboard\n->", + "output": "{\"text\": \"no backlit keyboard\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use some other apps but nothing is quite the same for my workflow .\n->i use some other apps but nothing is quite the same for my workflow .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: the hard drive is definitely slow .\n->the hard drive is definitely slow .\n[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: horrible light bleed from the top edge of the screen\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhorrible light bleed from the top edge of the screen\n->", + "output": "{\"text\": \"horrible light bleed from the top edge of the screen\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 've rarely had a problem with slow staff in the 10 years I 've been going .\n->I 've rarely had a problem with slow staff in the 10 years I 've been going .\n[{'aspect': 'staff', 'opinion': 'slow', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is spacious , and has a nice tactile feel .\n->the keyboard is spacious , and has a nice tactile feel .\n[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: no ventilation at bottom of laptop\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno ventilation at bottom of laptop\n->", + "output": "{\"text\": \"no ventilation at bottom of laptop\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - excellent cpu\n->- excellent cpu\n[{'aspect': 'cpu', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\nExample:\ntext: The waitress suggested glasses of wine that went very well with the food .\n->The waitress suggested glasses of wine that went very well with the food .\n[{'aspect': 'glasses of wine', 'opinion': 'went very well with the food', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the laptop looks beautiful and the 8th gen intel core is a performance powerhouse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop looks beautiful and the 8th gen intel core is a performance powerhouse .\n->", + "output": "{\"text\": \"the laptop looks beautiful and the 8th gen intel core is a performance powerhouse .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '8th gen intel core', 'opinion': 'powerhouse', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Chennai Garden is my favorite Indian restaurant in the city .\n->Chennai Garden is my favorite Indian restaurant in the city .\n[{'aspect': 'Chennai Garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Terrific menu full of unique rolls and special dishes .\n->Terrific menu full of unique rolls and special dishes .\n[{'aspect': 'menu', 'opinion': 'Terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the laptop is a little bigger than i had hoped and heavier than i expected it to feel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop is a little bigger than i had hoped and heavier than i expected it to feel .\n->", + "output": "{\"text\": \"the laptop is a little bigger than i had hoped and heavier than i expected it to feel .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'bigger', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no thanks ! ! !\n->no thanks ! ! !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n->the mussels were fantastic and so was the dessert . . . definitely going to be back very soon .\n[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: to me back - lighting on a keyboard is a make or break so i might return the laptop though ( bought it because i could have sworn i read it was backlit when i purchased it ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nto me back - lighting on a keyboard is a make or break so i might return the laptop though ( bought it because i could have sworn i read it was backlit when i purchased it ) .\n->", + "output": "{\"text\": \"to me back - lighting on a keyboard is a make or break so i might return the laptop though ( bought it because i could have sworn i read it was backlit when i purchased it ) .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n->they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n->Where tanks in other Chinatown restaurants display a lurking myriad of sad-looking marine life in their murky waters , the tanks at Ping 's are clear as glass with healthy-looking creatures who do not yet know that they will be part of some dim sum lover 's brunch .\n[{'aspect': 'tanks', 'opinion': 'sad-looking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tanks', 'opinion': 'clear', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dim sum', 'opinion': 'healthy-looking', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: overall , its a pretty good looking laptop that falls short of feeling truly premium ( backlit keyboard , cheap quality screen , keyboard flex ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , its a pretty good looking laptop that falls short of feeling truly premium ( backlit keyboard , cheap quality screen , keyboard flex ) .\n->", + "output": "{\"text\": \"overall , its a pretty good looking laptop that falls short of feeling truly premium ( backlit keyboard , cheap quality screen , keyboard flex ) .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good looking', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tech support is useless .\n->tech support is useless .\n[{'aspect': 'tech support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: If I wanted to deal with a crappy scene and annoying customers I 'd go out in Manhattan .\n->If I wanted to deal with a crappy scene and annoying customers I 'd go out in Manhattan .\n[{'aspect': 'scene', 'opinion': 'crappy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'customers', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the display is brilliant , has the whitest whites and the blackest blacks , contrast is excellent and the colors are outstanding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display is brilliant , has the whitest whites and the blackest blacks , contrast is excellent and the colors are outstanding .\n->", + "output": "{\"text\": \"the display is brilliant , has the whitest whites and the blackest blacks , contrast is excellent and the colors are outstanding .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'contrast', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'colors', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n->although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n->i ' m not sure where the other reviewers ate but it seems as if we visited two different restaurants because my friends and i all enjoy mizu very much . . . and we ' re repeat customers .\n[{'aspect': 'mizu', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: not only is it a hdd , but its a slow one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only is it a hdd , but its a slow one .\n->", + "output": "{\"text\": \"not only is it a hdd , but its a slow one .\", \"labels\": \"[{'aspect': 'hdd', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after seven months , the usb - c ports stopped charging .\n->after seven months , the usb - c ports stopped charging .\n[{'aspect': 'usb - c ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: i recommend this place to everyone .\n->i recommend this place to everyone .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: - keyboard isn ' t back - lit\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- keyboard isn ' t back - lit\n->", + "output": "{\"text\": \"- keyboard isn ' t back - lit\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall this is a very capable machine , better life is great as well .\n->overall this is a very capable machine , better life is great as well .\n[{'aspect': 'machine ,', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'better life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: Service -- friendly and attentive .\n->Service -- friendly and attentive .\n[{'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - no cd / dvd reader ( but who uses them nowadays anyway )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- no cd / dvd reader ( but who uses them nowadays anyway )\n->", + "output": "{\"text\": \"- no cd / dvd reader ( but who uses them nowadays anyway )\", \"labels\": \"[{'aspect': 'cd / dvd reader', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OPTICAL_DRIVES#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'lot', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s a very fast laptop .\n->it ' s a very fast laptop .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: highly recommend this laptop for mobile workers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhighly recommend this laptop for mobile workers .\n->", + "output": "{\"text\": \"highly recommend this laptop for mobile workers .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: One of my favorites though was the Angry Lobster , a cold lobster salad that was magnificent .\n->One of my favorites though was the Angry Lobster , a cold lobster salad that was magnificent .\n[{'aspect': 'Angry Lobster', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cold lobster salad', 'opinion': 'magnificent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the battery broke after just 4 months from baying it am so disappointed with the product\n->the battery broke after just 4 months from baying it am so disappointed with the product\n[{'aspect': 'battery', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}, {'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\n->", + "output": "{\"text\": \"the ram is expandable ; i bought 8 gb additional and installed myself fairly easily .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'expandable', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n->they just don ' t get loud enough and that ' s crucial for me since i watch a lot of youtube and listen to music while doing other things on the machine .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s not worth what i paid for it .\n->it ' s not worth what i paid for it .\n[{'aspect': 'it', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: quiet keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nquiet keyboard .\n->", + "output": "{\"text\": \"quiet keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer runs great .\n->the computer runs great .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: do not buy !\n->do not buy !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: ok battery life , 4 or 5 hours of continuous use , but i ' m never far away from a plug so no big deal really .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nok battery life , 4 or 5 hours of continuous use , but i ' m never far away from a plug so no big deal really .\n->", + "output": "{\"text\": \"ok battery life , 4 or 5 hours of continuous use , but i ' m never far away from a plug so no big deal really .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n->We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n[{'aspect': 'dinner specials', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner specials', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we had half / half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !\n->we had half / half pizza , mine was eggplant and my friend had the buffalo and it was sooo huge for a small size pizza !\n[{'aspect': 'half / half pizza', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: cons : delete key near the power button ( oops ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncons : delete key near the power button ( oops ! )\n->", + "output": "{\"text\": \"cons : delete key near the power button ( oops ! )\", \"labels\": \"[{'aspect': 'delete key', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: volume was not working .\n->volume was not working .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\ntext: internal graphics only ; not recommended for high intensity gaming or 3d modeling .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ninternal graphics only ; not recommended for high intensity gaming or 3d modeling .\n->", + "output": "{\"text\": \"internal graphics only ; not recommended for high intensity gaming or 3d modeling .\", \"labels\": \"[{'aspect': 'internal graphics', 'opinion': 'not recommended', 'polarity': 'negative', 'category': 'GRAPHICS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am able to write , look up facts on the internet , and it has a pretty good battery life .\n->i am able to write , look up facts on the internet , and it has a pretty good battery life .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate decor will make you feel like you \u2019 re no longer in the city .\n->not only is it an adventure getting to this somewhat hidden spot , once you enter the unmarked wooden doors , the zen and intimate decor will make you feel like you \u2019 re no longer in the city .\n[{'aspect': 'spot', 'opinion': 'hidden', 'polarity': 'neutral', 'category': 'LOCATION#GENERAL'}, {'aspect': 'unmarked wooden doors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: this has been a really good computer for the money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis has been a really good computer for the money .\n->", + "output": "{\"text\": \"this has been a really good computer for the money .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * love * the feel and look of it , no complaints at all , samsung chromebooks are amazing !\n->* love * the feel and look of it , no complaints at all , samsung chromebooks are amazing !\n[{'aspect': 'samsung chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebooks', 'opinion': 'no complaints', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebooks', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Service here was great , food was fantastic .\n->Service here was great , food was fantastic .\n[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it is fast and lightweight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is fast and lightweight .\n->", + "output": "{\"text\": \"it is fast and lightweight .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'it', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great computer .\n->great computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: On a hot day it was fabulous to stop in and enjoy lunch .\n->On a hot day it was fabulous to stop in and enjoy lunch .\n[{'aspect': 'lunch', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i do n ' t game , so not idea how that would go , but it ' s probably not bad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni do n ' t game , so not idea how that would go , but it ' s probably not bad .\n->", + "output": "{\"text\": \"i do n ' t game , so not idea how that would go , but it ' s probably not bad .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not bad', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: at taj , vegetarians can rejoice-all the dishes are manna from heaven .\n->at taj , vegetarians can rejoice-all the dishes are manna from heaven .\n[{'aspect': 'dishes', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great food\n->great food\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the only thing i don ' t like is that the power button sits beside the delete key .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing i don ' t like is that the power button sits beside the delete key .\n->", + "output": "{\"text\": \"the only thing i don ' t like is that the power button sits beside the delete key .\", \"labels\": \"[{'aspect': 'power button', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n->While the place is not a hotspot hangout , the drinks are unique and pack a lot of bang for the buck .\n[{'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service was ok .\n->The service was ok .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: it ' s a good system and has loads of space available for storage :\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a good system and has loads of space available for storage :\n->", + "output": "{\"text\": \"it ' s a good system and has loads of space available for storage :\", \"labels\": \"[{'aspect': 'system', 'opinion': 'good', 'polarity': 'positive', 'category': 'OS#GENERAL'}, {'aspect': 'storage', 'opinion': 'available', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: laptop in perfect condition .\n->laptop in perfect condition .\n[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Horrible food and horrible service .\n->Horrible food and horrible service .\n[{'aspect': 'food', 'opinion': 'Horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n->", + "output": "{\"text\": \"the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\", \"labels\": \"[{'aspect': '1tb included drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'processing power', 'opinion': 'great', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n->i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: second where the heck is my other 8 gigs of ram ?\n->second where the heck is my other 8 gigs of ram ?\n[{'aspect': '8 gigs of ram', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\ntext: this computer functions very well as a gaming laptop for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer functions very well as a gaming laptop for the price .\n->", + "output": "{\"text\": \"this computer functions very well as a gaming laptop for the price .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'gaming laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'gaming laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is a nice size and the pads clicks on touch and not stiff or hard .\n->the keyboard is a nice size and the pads clicks on touch and not stiff or hard .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: all in all , i could live with this as my sole device if i had to\n->all in all , i could live with this as my sole device if i had to\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: the laptop runs smoothly and renders larger games quickly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop runs smoothly and renders larger games quickly .\n->", + "output": "{\"text\": \"the laptop runs smoothly and renders larger games quickly .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our phones are usb c so one cable does everything for me .\n->our phones are usb c so one cable does everything for me .\n[{'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'PORTS#GENERAL'}]\nExample:\ntext: definitely the best chromebook out there .\n->definitely the best chromebook out there .\n[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: there are only two cons , the sound quality and the overheating .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere are only two cons , the sound quality and the overheating .\n->", + "output": "{\"text\": \"there are only two cons , the sound quality and the overheating .\", \"labels\": \"[{'aspect': 'sound quality', 'opinion': 'cons', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'overheating', 'opinion': 'cons', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: polite acknowledgement is out ;\n->polite acknowledgement is out ;\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: delivered quickly , easy set up .\n->delivered quickly , easy set up .\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}, {'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: all in all a great cheap gaming laptop that even with the cons i am not dissatisfied with the product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all a great cheap gaming laptop that even with the cons i am not dissatisfied with the product .\n->", + "output": "{\"text\": \"all in all a great cheap gaming laptop that even with the cons i am not dissatisfied with the product .\", \"labels\": \"[{'aspect': 'gaming laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: But the thing that my wife and I hated was it was so loud and it felt like ' bar ' or ' pub ' .\n->But the thing that my wife and I hated was it was so loud and it felt like ' bar ' or ' pub ' .\n[{'aspect': 'bar', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pub', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i like this laptop , for a 15 ` ` monitor laptop with i5 - 8250u cpu , the weight is acceptable for me to carry it to work between different office .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like this laptop , for a 15 ` ` monitor laptop with i5 - 8250u cpu , the weight is acceptable for me to carry it to work between different office .\n->", + "output": "{\"text\": \"i like this laptop , for a 15 ` ` monitor laptop with i5 - 8250u cpu , the weight is acceptable for me to carry it to work between different office .\", \"labels\": \"[{'aspect': 'weight', 'opinion': 'acceptable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the service was a bit slow .\n->but the service was a bit slow .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i bought 2 , one stopped working after 9 months , was sent for ` ` repair ` ` it wasn ' t came right back with the same issue .\n->i bought 2 , one stopped working after 9 months , was sent for ` ` repair ` ` it wasn ' t came right back with the same issue .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: the monitor is good , and graphic chip is enough for my office work , internet browsing and video streaming , don ' t think about what game it can play , i won ' t expect intel graphic chip can do a lot , if you want a gaming laptop , find some model with independent graphic chip , if you want a cheap laptop but can play computer game , you should wake up from the dream .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe monitor is good , and graphic chip is enough for my office work , internet browsing and video streaming , don ' t think about what game it can play , i won ' t expect intel graphic chip can do a lot , if you want a gaming laptop , find some model with independent graphic chip , if you want a cheap laptop but can play computer game , you should wake up from the dream .\n->", + "output": "{\"text\": \"the monitor is good , and graphic chip is enough for my office work , internet browsing and video streaming , don ' t think about what game it can play , i won ' t expect intel graphic chip can do a lot , if you want a gaming laptop , find some model with independent graphic chip , if you want a cheap laptop but can play computer game , you should wake up from the dream .\", \"labels\": \"[{'aspect': 'monitor', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'graphic chip', 'opinion': 'enough', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The steak is good , the fish is good and the sushi was surprisingly great .\n->The steak is good , the fish is good and the sushi was surprisingly great .\n[{'aspect': 'steak', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - excellent keyboard in all aspects - feel , rigidity , and backlight\n->- excellent keyboard in all aspects - feel , rigidity , and backlight\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: just one question , now i am running the os on the ssd and the original 1tb harddisk just for storing file , its boot up and start softwares very fast , but operating with file explore is very slow , i already set the file explore not open the quick access but it still slow , rename will effective after seconds , copy file and even right click sometimes need wait for minutes , who know how can i solve it ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \njust one question , now i am running the os on the ssd and the original 1tb harddisk just for storing file , its boot up and start softwares very fast , but operating with file explore is very slow , i already set the file explore not open the quick access but it still slow , rename will effective after seconds , copy file and even right click sometimes need wait for minutes , who know how can i solve it ?\n->", + "output": "{\"text\": \"just one question , now i am running the os on the ssd and the original 1tb harddisk just for storing file , its boot up and start softwares very fast , but operating with file explore is very slow , i already set the file explore not open the quick access but it still slow , rename will effective after seconds , copy file and even right click sometimes need wait for minutes , who know how can i solve it ?\", \"labels\": \"[{'aspect': 'boot up', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sound isn ' t great on both devices but they ' ll suffice ; at least the asus speakers are side - firing and not coming from the bottom like the pro .\n->the sound isn ' t great on both devices but they ' ll suffice ; at least the asus speakers are side - firing and not coming from the bottom like the pro .\n[{'aspect': 'asus speakers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\nExample:\ntext: overall this chromebook worked well and was reliable .\n->overall this chromebook worked well and was reliable .\n[{'aspect': 'chromebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: awesome computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nawesome computer .\n->", + "output": "{\"text\": \"awesome computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: fabulous decor - makes you feel like you ' re in a trendy manhattan restaurant , very very good food , cheaply - priced , generally friendly staff , and if you ' re a manhattanite , or spend most of your time in manhattan , rice avenue will make you feel at home . . . . . very soho / village / upper west side minus the expensive prices and pretentious clientele . . . . . all on roosevelt avenue !\n->fabulous decor - makes you feel like you ' re in a trendy manhattan restaurant , very very good food , cheaply - priced , generally friendly staff , and if you ' re a manhattanite , or spend most of your time in manhattan , rice avenue will make you feel at home . . . . . very soho / village / upper west side minus the expensive prices and pretentious clientele . . . . . all on roosevelt avenue !\n[{'aspect': 'decor', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheaply - priced', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}, {'aspect': 'rice avenue', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n->i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: as everyone else says ; the keyboard is not backlit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas everyone else says ; the keyboard is not backlit .\n->", + "output": "{\"text\": \"as everyone else says ; the keyboard is not backlit .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was as they advertised .\n->it was as they advertised .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: We had a wonderful meal at Naples 45 a month ago on a visit to NYC .\n->We had a wonderful meal at Naples 45 a month ago on a visit to NYC .\n[{'aspect': 'meal', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the original hdd in this laptop has some speed limitations for load up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe original hdd in this laptop has some speed limitations for load up .\n->", + "output": "{\"text\": \"the original hdd in this laptop has some speed limitations for load up .\", \"labels\": \"[{'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: super off balance with respect to screen .\n->super off balance with respect to screen .\n[{'aspect': 'screen', 'opinion': 'off balance', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: the temperatures were good , and the overall responsiveness of the system was fine .\n->the temperatures were good , and the overall responsiveness of the system was fine .\n[{'aspect': 'temperatures', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'responsiveness of the system', 'opinion': 'fine', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the specs are good , its even faster since i added an additional 8b ram , making it a total of 16gb .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe specs are good , its even faster since i added an additional 8b ram , making it a total of 16gb .\n->", + "output": "{\"text\": \"the specs are good , its even faster since i added an additional 8b ram , making it a total of 16gb .\", \"labels\": \"[{'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'ram', 'opinion': 'faster', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: super yummy pizza !\n->super yummy pizza !\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: no desert menu , no apology , nothing ! ! ! ! ! !\n->no desert menu , no apology , nothing ! ! ! ! ! !\n[{'aspect': 'NULL', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the 15 ` ` screen is big and nice , this will make my examinations go much faster .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 15 ` ` screen is big and nice , this will make my examinations go much faster .\n->", + "output": "{\"text\": \"the 15 ` ` screen is big and nice , this will make my examinations go much faster .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'faster', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s easy to use , convenient .\n->it ' s easy to use , convenient .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: faan is sooo good .\n->faan is sooo good .\n[{'aspect': 'faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it ' s completely quiet , no heat whatsoever , and very fast !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s completely quiet , no heat whatsoever , and very fast !\n->", + "output": "{\"text\": \"it ' s completely quiet , no heat whatsoever , and very fast !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there was no tap beer that evening , which was a disappointment .\n->there was no tap beer that evening , which was a disappointment .\n[{'aspect': 'beer', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'DRINKS#STYLE_OPTIONS'}]\nExample:\ntext: slightly on the pricey side but worth it !\n->slightly on the pricey side but worth it !\n[{'aspect': 'NULL', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: good screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood screen .\n->", + "output": "{\"text\": \"good screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n->the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n[{'aspect': 'battery life', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'longevity', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: love the mobility of this .\n->love the mobility of this .\n[{'aspect': 'mobility', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: perfect laptop for everyday use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nperfect laptop for everyday use .\n->", + "output": "{\"text\": \"perfect laptop for everyday use .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do not buy this .\n->do not buy this .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n->Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food art', 'opinion': 'ultra fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' ve used it to do some basic video editing , developing applications , and some gaming and it ' s handled it all fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve used it to do some basic video editing , developing applications , and some gaming and it ' s handled it all fine .\n->", + "output": "{\"text\": \"i ' ve used it to do some basic video editing , developing applications , and some gaming and it ' s handled it all fine .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: computer wo n ' t turn on have had it less then a year .\n->computer wo n ' t turn on have had it less then a year .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the fans did not turn on loudly if at all .\n->the fans did not turn on loudly if at all .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: ca n ' t go wrong with an asus !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nca n ' t go wrong with an asus !\n->", + "output": "{\"text\": \"ca n ' t go wrong with an asus !\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n->The service is excellent , the decor is great , and the food is delicious and comes in large portions .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: amazing laptop !\n->amazing laptop !\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i plugged it back in , let it fully charge as directed and have had no problems since .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni plugged it back in , let it fully charge as directed and have had no problems since .\n->", + "output": "{\"text\": \"i plugged it back in , let it fully charge as directed and have had no problems since .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n->received on time and the packaging assured that if those ups drivers dropped my computer , it would t damage it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\nExample:\ntext: go here .\n->go here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the screen on this looks great , the bezels aren ' t noticeable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen on this looks great , the bezels aren ' t noticeable .\n->", + "output": "{\"text\": \"the screen on this looks great , the bezels aren ' t noticeable .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': \"' t noticeable\", 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: terrible , terrible management - deserves to be shut - down .\n->terrible , terrible management - deserves to be shut - down .\n[{'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'management', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The quality of food at this restaurant accompanied by fantastic live jazz makes this place a perfect 10 !\n->The quality of food at this restaurant accompanied by fantastic live jazz makes this place a perfect 10 !\n[{'aspect': 'quality of food', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'live jazz', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: well , wifi worked instantly without bugs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwell , wifi worked instantly without bugs .\n->", + "output": "{\"text\": \"well , wifi worked instantly without bugs .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n->There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n[{'aspect': 'delivery guys', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: all in all , the food was great ( except for the dessserts ) .\n->all in all , the food was great ( except for the dessserts ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessserts', 'opinion': 'except', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i ' ve had the laptop for a full day now and i can say it is quite impressive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had the laptop for a full day now and i can say it is quite impressive .\n->", + "output": "{\"text\": \"i ' ve had the laptop for a full day now and i can say it is quite impressive .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: still learning but it ' s a good computer and a great deal\n->still learning but it ' s a good computer and a great deal\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: much of the time it seems like they do not care about you .\n->much of the time it seems like they do not care about you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the screen is also crisp and the speakers are punchy for a laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is also crisp and the speakers are punchy for a laptop .\n->", + "output": "{\"text\": \"the screen is also crisp and the speakers are punchy for a laptop .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'punchy', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the metal case is really well built , and the fit and finish are virtually perfect .\n->the metal case is really well built , and the fit and finish are virtually perfect .\n[{'aspect': 'metal case', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'fit', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'finish', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: package arrived faster than the estimated arrival .\n->package arrived faster than the estimated arrival .\n[{'aspect': 'package', 'opinion': 'faster', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\ntext: this is a great notebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great notebook .\n->", + "output": "{\"text\": \"this is a great notebook .\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n->i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n[{'aspect': 'pizza', 'opinion': 'crave', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: oh , and this charges fast .\n->oh , and this charges fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: on the flip side , this asus is very fast with minimal bloatware that is easy to get rid of .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non the flip side , this asus is very fast with minimal bloatware that is easy to get rid of .\n->", + "output": "{\"text\": \"on the flip side , this asus is very fast with minimal bloatware that is easy to get rid of .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'asus', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in the summer months , the back garden area is really nice .\n->in the summer months , the back garden area is really nice .\n[{'aspect': 'back garden area', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the place was quiet and delightful .\n->the place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: battery life is between 4 to 7 hours depending on what i ' m doing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is between 4 to 7 hours depending on what i ' m doing .\n->", + "output": "{\"text\": \"battery life is between 4 to 7 hours depending on what i ' m doing .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n->This is definitely an excellent date spot because of the ambiance and on the weekends the night scene is more than alive .\n[{'aspect': 'night scene', 'opinion': 'alive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spot', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is only pretty good .\n->the screen is only pretty good .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: i bought this laptop for software development .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this laptop for software development .\n->", + "output": "{\"text\": \"i bought this laptop for software development .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will return it .\n->i will return it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard and touchpad experience are pivotal for a device like this and what asus delivers is very satisfying .\n->the keyboard and touchpad experience are pivotal for a device like this and what asus delivers is very satisfying .\n[{'aspect': 'asus', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\ntext: my only complaint is that the trackpad is just awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy only complaint is that the trackpad is just awful .\n->", + "output": "{\"text\": \"my only complaint is that the trackpad is just awful .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use it mostly when traveling it works well for that purpose .\n->i use it mostly when traveling it works well for that purpose .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: nice screen and keyboard , touch pad is great .\n->nice screen and keyboard , touch pad is great .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n->", + "output": "{\"text\": \"overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was the only thing good about this restaurant .\n->The service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: excellent quality and very fast delivery .\n->excellent quality and very fast delivery .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'delivery', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: it ' s pretty light , too , so it ' s easy to travel with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s pretty light , too , so it ' s easy to travel with .\n->", + "output": "{\"text\": \"it ' s pretty light , too , so it ' s easy to travel with .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little guy fits the bill perfectly .\n->this little guy fits the bill perfectly .\n[{'aspect': 'guy', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: awesome\n->awesome\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the build quality is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is .\n->", + "output": "{\"text\": \"the build quality is .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the kind of place you ' d like to take all your friends to and still keep a secret .\n->this is the kind of place you ' d like to take all your friends to and still keep a secret .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n->the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard response', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i am much more productive with this machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am much more productive with this machine .\n->", + "output": "{\"text\": \"i am much more productive with this machine .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'productive', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': \"ca n't be beat\", 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The shrimp scampi was excellent and the antipasti were plentiful .\n->The shrimp scampi was excellent and the antipasti were plentiful .\n[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: for me , it ' s been fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor me , it ' s been fantastic .\n->", + "output": "{\"text\": \"for me , it ' s been fantastic .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'pepper', 'opinion': 'much', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n->Ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: would have been 5 stars if the keyboard was back light and the finger print reader had linux drivers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwould have been 5 stars if the keyboard was back light and the finger print reader had linux drivers .\n->", + "output": "{\"text\": \"would have been 5 stars if the keyboard was back light and the finger print reader had linux drivers .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'finger print reader', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n->The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spaghetti with Scallops and Shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: they ' re like a little - known gem , practically unknown in my area .\n->they ' re like a little - known gem , practically unknown in my area .\n[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this by far one of the best laptops i ' ve ever purchased .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis by far one of the best laptops i ' ve ever purchased .\n->", + "output": "{\"text\": \"this by far one of the best laptops i ' ve ever purchased .\", \"labels\": \"[{'aspect': 'laptops', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good sound quality\n->good sound quality\n[{'aspect': 'sound quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: cons hd display is n ' t the greatest\n->cons hd display is n ' t the greatest\n[{'aspect': 'hd display', 'opinion': 'cons', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'hd display', 'opinion': \"n ' t the greatest\", 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\ntext: ( coughlenovocough ) this is a beast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n( coughlenovocough ) this is a beast .\n->", + "output": "{\"text\": \"( coughlenovocough ) this is a beast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'beast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .\n->The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .\n[{'aspect': 'Prix Fixe menu', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Very affordable and excellent ambient !\n->Very affordable and excellent ambient !\n[{'aspect': 'ambient', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambient', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: good for students that carry it from class to class .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood for students that carry it from class to class .\n->", + "output": "{\"text\": \"good for students that carry it from class to class .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve used it for 3 days and still have n ' t plugged it in !\n->i ' ve used it for 3 days and still have n ' t plugged it in !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: it ' s all about the food ! !\n->it ' s all about the food ! !\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: also , the power button placement is not very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , the power button placement is not very good .\n->", + "output": "{\"text\": \"also , the power button placement is not very good .\", \"labels\": \"[{'aspect': 'power button', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: both forward and rear facing cameras would be nice too .\n->both forward and rear facing cameras would be nice too .\n[{'aspect': 'cameras', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: i honestly do n ' t even know where to begin .\n->i honestly do n ' t even know where to begin .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: it bothered me that i could accidentally press it while typing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit bothered me that i could accidentally press it while typing .\n->", + "output": "{\"text\": \"it bothered me that i could accidentally press it while typing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'bothered', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the price you can ' t beat a chromebook .\n->for the price you can ' t beat a chromebook .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: it was really good pizza .\n->it was really good pizza .\n[{'aspect': 'pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i ' m a programmer and it can run all programs perfectly without delay and i don ' t have to worry about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m a programmer and it can run all programs perfectly without delay and i don ' t have to worry about it .\n->", + "output": "{\"text\": \"i ' m a programmer and it can run all programs perfectly without delay and i don ' t have to worry about it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: has all the top features and runs fast .\n->has all the top features and runs fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Lucky Strike is a great casual place to just grab a bite to eat .\n->Lucky Strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'place', 'opinion': 'great casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the notebook is very well built and it could easily pass as a high - end machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe notebook is very well built and it could easily pass as a high - end machine .\n->", + "output": "{\"text\": \"the notebook is very well built and it could easily pass as a high - end machine .\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'machine', 'opinion': 'high - end', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great product and price\n->great product and price\n[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the food is very average . . . the thai fusion stuff is a bit too sweet , every thing they serve is too sweet here .\n->the food is very average . . . the thai fusion stuff is a bit too sweet , every thing they serve is too sweet here .\n[{'aspect': 'food', 'opinion': 'average', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai fusion stuff', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: asus support is horrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nasus support is horrible .\n->", + "output": "{\"text\": \"asus support is horrible .\", \"labels\": \"[{'aspect': 'asus support', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: They offer the same menu but have creative drinks that are loaded with alcohol and cheeky names -- but they do cost you .\n->They offer the same menu but have creative drinks that are loaded with alcohol and cheeky names -- but they do cost you .\n[{'aspect': 'menu', 'opinion': 'same', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'creative', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the support website is incompetent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe support website is incompetent .\n->", + "output": "{\"text\": \"the support website is incompetent .\", \"labels\": \"[{'aspect': 'support website', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen display isn ' t bright at all !\n->the screen display isn ' t bright at all !\n[{'aspect': 'screen display', 'opinion': \"' t bright at all\", 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it ' s now been totally reliable for half a year or so .\n->it ' s now been totally reliable for half a year or so .\n[{'aspect': 'NULL', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this is a decent laptop no thanks to asus support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a decent laptop no thanks to asus support .\n->", + "output": "{\"text\": \"this is a decent laptop no thanks to asus support .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the vivobook f510ua is a great laptop with fantastic specs .\n->the vivobook f510ua is a great laptop with fantastic specs .\n[{'aspect': 'vivobook f510ua', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'specs', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i ' m amazed at this product that has the same build as the other acer chromebooks .\n->i ' m amazed at this product that has the same build as the other acer chromebooks .\n[{'aspect': 'product', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\n->", + "output": "{\"text\": \"this thing is now light , fast and powerful enough to allow me to spin up some virtual machines to help with my study courses .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so easy to use and is not that slow like some say works just fine for casual use .\n->so easy to use and is not that slow like some say works just fine for casual use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'not that slow', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: now the start up is failing .\n->now the start up is failing .\n[{'aspect': 'start up', 'opinion': 'failing', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: screen color is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen color is excellent .\n->", + "output": "{\"text\": \"screen color is excellent .\", \"labels\": \"[{'aspect': 'screen color', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen would freeze , requiring a hard shutdown .\n->the screen would freeze , requiring a hard shutdown .\n[{'aspect': 'screen', 'opinion': 'freeze', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The waiter was attentive .\n->The waiter was attentive .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: fast , thin , great battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast , thin , great battery life .\n->", + "output": "{\"text\": \"fast , thin , great battery life .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n->thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: sound went out in less than a week .\n->sound went out in less than a week .\n[{'aspect': 'sound went', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: monitor looks crisp .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmonitor looks crisp .\n->", + "output": "{\"text\": \"monitor looks crisp .\", \"labels\": \"[{'aspect': 'monitor', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quick and friendly service .\n->Quick and friendly service .\n[{'aspect': 'service', 'opinion': 'Quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i should have known better : msi has boot issues , no way around it .\n->i should have known better : msi has boot issues , no way around it .\n[{'aspect': 'msi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MOTHERBOARD#QUALITY'}]\ntext: no dvd drive , but who uses those anymore anyway ?\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno dvd drive , but who uses those anymore anyway ?\n->", + "output": "{\"text\": \"no dvd drive , but who uses those anymore anyway ?\", \"labels\": \"[{'aspect': 'dvd drive', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n->Slightly above average wines start at $ 70+ with only one selection listed at $ 30+ .\n[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The wine the service was very good too .\n->The wine the service was very good too .\n[{'aspect': 'wine', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the power _ supply is awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe power _ supply is awesome .\n->", + "output": "{\"text\": \"the power _ supply is awesome .\", \"labels\": \"[{'aspect': 'power _ supply is', 'opinion': '.', 'polarity': 'positive', 'category': 'POWER_SUPPLY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if something happens within 30 days i will return it but i will get another one .\n->if something happens within 30 days i will return it but i will get another one .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: everything about the experience has been terrible .\n->everything about the experience has been terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: only thing i ' d change would be the hard drive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly thing i ' d change would be the hard drive .\n->", + "output": "{\"text\": \"only thing i ' d change would be the hard drive .\", \"labels\": \"[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The white bean brushetta to start was incredible and the pasta was phenomenal .\n->The white bean brushetta to start was incredible and the pasta was phenomenal .\n[{'aspect': 'white bean brushetta', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: processer is blazing fast ( competes with 7th gen i7 hq line - check cpu benchmark ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nprocesser is blazing fast ( competes with 7th gen i7 hq line - check cpu benchmark ) .\n->", + "output": "{\"text\": \"processer is blazing fast ( competes with 7th gen i7 hq line - check cpu benchmark ) .\", \"labels\": \"[{'aspect': 'processer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: luckily we saved room for the bbq salmon , sea bass and crispy duck .\n->luckily we saved room for the bbq salmon , sea bass and crispy duck .\n[{'aspect': 'bbq salmon', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sea bass', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crispy duck', 'opinion': 'luckily', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Someone else recommended the dessert - we also left that .\n->Someone else recommended the dessert - we also left that .\n[{'aspect': 'dessert', 'opinion': 'recommended', 'polarity': 'negative', 'category': 'NULL'}]\ntext: only complaint that i took a star off for is that the edge you rest your arms on is not rounded off and it is uncomfortable to rest on them for while .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly complaint that i took a star off for is that the edge you rest your arms on is not rounded off and it is uncomfortable to rest on them for while .\n->", + "output": "{\"text\": \"only complaint that i took a star off for is that the edge you rest your arms on is not rounded off and it is uncomfortable to rest on them for while .\", \"labels\": \"[{'aspect': 'edge', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'edge', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not a very fancy place but very good chinese style indian food .\n->not a very fancy place but very good chinese style indian food .\n[{'aspect': 'place', 'opinion': 'fancy', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'chinese style indian food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: go to volare for 1st class service and terrific food .\n->go to volare for 1st class service and terrific food .\n[{'aspect': 'service', 'opinion': '1st class', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: display is ok not great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndisplay is ok not great .\n->", + "output": "{\"text\": \"display is ok not great .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: no fans grinding away .\n->no fans grinding away .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#QUALITY'}]\nExample:\ntext: this place is always packed .\n->this place is always packed .\n[{'aspect': 'place', 'opinion': 'packed', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\ntext: nice computer for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice computer for the price .\n->", + "output": "{\"text\": \"nice computer for the price .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was okay , nothing great .\n->Food was okay , nothing great .\n[{'aspect': 'Food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: From the entrees to the sides to the drinks , everything was creatively prepared yet still simple .\n->From the entrees to the sides to the drinks , everything was creatively prepared yet still simple .\n[{'aspect': 'entrees', 'opinion': 'creatively prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'entrees', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sides', 'opinion': 'creatively prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sides', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'creatively prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}]\ntext: quick startup and has a nice display which is matte .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nquick startup and has a nice display which is matte .\n->", + "output": "{\"text\": \"quick startup and has a nice display which is matte .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ' ve tried before but it always packed and does n ' t take reservations .\n->we ' ve tried before but it always packed and does n ' t take reservations .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: i highly recommend the sophia pizza .\n->i highly recommend the sophia pizza .\n[{'aspect': 'sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this is a great laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great laptop .\n->", + "output": "{\"text\": \"this is a great laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like the laptop for it ' s hardware , and it ' s working properly .\n->i like the laptop for it ' s hardware , and it ' s working properly .\n[{'aspect': 'hardware', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n->i bought mine this year february and the laptop is very awesome the boot speed is perfect and the gaming it ' s fine as well and yeah i can say it depends on what you want to use with your laptop but overall the laptop is fine the only problem is the cooling fan it is too loud but the processor the ram the storage it ' s fine\n[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'boot speed', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'OS#GENERAL'}, {'aspect': 'cooling fan', 'opinion': 'loud', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\ntext: i really love the laptop !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really love the laptop !\n->", + "output": "{\"text\": \"i really love the laptop !\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n->now the romance is over ; it won ' t charge , and my only option is to send it in for service , a 10 - day event not including shipping there and back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: my first time with a solid state drive , very nice quick and quiet .\n->my first time with a solid state drive , very nice quick and quiet .\n[{'aspect': 'solid state drive', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'solid state drive', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: it works really well with my art programs and runs a lot better !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit works really well with my art programs and runs a lot better !\n->", + "output": "{\"text\": \"it works really well with my art programs and runs a lot better !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Lived in Shanghai most of my life and thought the food was comparable to the flagship Green Bo restaurant there .\n->Lived in Shanghai most of my life and thought the food was comparable to the flagship Green Bo restaurant there .\n[{'aspect': 'food', 'opinion': 'comparable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it can not .\n->it can not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: don ' t know what was going on , but , it seems like this laptop is working fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndon ' t know what was going on , but , it seems like this laptop is working fine .\n->", + "output": "{\"text\": \"don ' t know what was going on , but , it seems like this laptop is working fine .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have never before eaten 40 pieces of relatively good nigiri .\n->i have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: am i just unlucky , or was this a bad batch ?\n->am i just unlucky , or was this a bad batch ?\n[{'aspect': 'NULL', 'opinion': 'unlucky', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: i bought the computer on january 2018 and so far i am really enjoying it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought the computer on january 2018 and so far i am really enjoying it .\n->", + "output": "{\"text\": \"i bought the computer on january 2018 and so far i am really enjoying it .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wonderful at holiday time .\n->wonderful at holiday time .\n[{'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Quite frankly , this is some of the worst sushi I have ever tried .\n->Quite frankly , this is some of the worst sushi I have ever tried .\n[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the computer has been running the programs such as matlab , mathematics , diamond , among others without problem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer has been running the programs such as matlab , mathematics , diamond , among others without problem .\n->", + "output": "{\"text\": \"the computer has been running the programs such as matlab , mathematics , diamond , among others without problem .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: bagels are ok , but be sure not to make any special requests !\n->bagels are ok , but be sure not to make any special requests !\n[{'aspect': 'bagels', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: second update - speakers no longer work after 1 month of use .\n->second update - speakers no longer work after 1 month of use .\n[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\ntext: for me has been worth the $ 500 for the computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor me has been worth the $ 500 for the computer .\n->", + "output": "{\"text\": \"for me has been worth the $ 500 for the computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n->most apps run pretty - ok in beta - some problems i ' m noticing in beta are that bluetooth is less than ideal and i believe battery life suffers greatly , now that i ' ve been paying attention to it .\n[{'aspect': 'apps', 'opinion': 'ok', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: if you are looking for a quality chromebook with the most important features for everyday use , the asus flip is a fantastic choice .\n->if you are looking for a quality chromebook with the most important features for everyday use , the asus flip is a fantastic choice .\n[{'aspect': 'chromebook', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus flip', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: and it ' s light which is a big plus since i carry it to school .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand it ' s light which is a big plus since i carry it to school .\n->", + "output": "{\"text\": \"and it ' s light which is a big plus since i carry it to school .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: am i just unlucky , or was this a bad batch ?\n->am i just unlucky , or was this a bad batch ?\n[{'aspect': 'NULL', 'opinion': 'unlucky', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: chromebooks are a waste of time / money .\n->chromebooks are a waste of time / money .\n[{'aspect': 'chromebooks', 'opinion': 'waste', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i guess the computer is quite okay for the price they are asking for it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni guess the computer is quite okay for the price they are asking for it .\n->", + "output": "{\"text\": \"i guess the computer is quite okay for the price they are asking for it .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'okay', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n->We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .\n[{'aspect': 'grilled branzino', 'opinion': 'difficult to eat', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n->The waitstaff were attentive , polite and helpful - an impressive feat in such close quarters .\n[{'aspect': 'waitstaff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'polite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: after using this for 7 months , i can say this is one of the better laptops i have owned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter using this for 7 months , i can say this is one of the better laptops i have owned .\n->", + "output": "{\"text\": \"after using this for 7 months , i can say this is one of the better laptops i have owned .\", \"labels\": \"[{'aspect': 'laptops', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lobster was good , nothing spectacular .\n->lobster was good , nothing spectacular .\n[{'aspect': 'lobster', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'nothing spectacular', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Delicious food at a great price but do not go here on a cold day and sit by the front door .\n->Delicious food at a great price but do not go here on a cold day and sit by the front door .\n[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'front door', 'opinion': 'cold', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i used this laptop for 2 months without upgrading it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni used this laptop for 2 months without upgrading it .\n->", + "output": "{\"text\": \"i used this laptop for 2 months without upgrading it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the setting is casual and romantic .\n->the setting is casual and romantic .\n[{'aspect': 'setting', 'opinion': 'casual', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'setting', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Food and service was okay .\n->Food and service was okay .\n[{'aspect': 'Food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npurchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n->", + "output": "{\"text\": \"purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\", \"labels\": \"[{'aspect': '8th gen i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great cheap tool for web development ( using linux ) and everyday internet usage .\n->great cheap tool for web development ( using linux ) and everyday internet usage .\n[{'aspect': 'tool', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: so i decided to purchase the asus flip , and so far it has met every one of my nitpicky demanding needs .\n->so i decided to purchase the asus flip , and so far it has met every one of my nitpicky demanding needs .\n[{'aspect': 'asus flip', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: hdd , agree didn ' t sound good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhdd , agree didn ' t sound good .\n->", + "output": "{\"text\": \"hdd , agree didn ' t sound good .\", \"labels\": \"[{'aspect': 'hdd', 'opinion': \"' t sound good\", 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n->Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n[{'aspect': 'waiters', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'busy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n->The service was poor , restaurant poorly lit , staff not very attentive and I would have rather eaten at a Mcdonald 's than this joint .\n[{'aspect': 'service', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'not very attentive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: after that , it ' s actually been running well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter that , it ' s actually been running well .\n->", + "output": "{\"text\": \"after that , it ' s actually been running well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n->very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'sound', 'opinion': 'big', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'boot times', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: I went there for lunch and it was not as good as I expected from the reviews I read .\n->I went there for lunch and it was not as good as I expected from the reviews I read .\n[{'aspect': 'lunch', 'opinion': 'not as good as I expected', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: screen , i have it running at the brightest setting and looks fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen , i have it running at the brightest setting and looks fine .\n->", + "output": "{\"text\": \"screen , i have it running at the brightest setting and looks fine .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: First went here to enjoy their garden terrace .\n->First went here to enjoy their garden terrace .\n[{'aspect': 'garden terrace', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this tiny williamsburg spot is always pleasantly surprising .\n->this tiny williamsburg spot is always pleasantly surprising .\n[{'aspect': 'williamsburg spot', 'opinion': 'surprising', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the hdd sounds more like scraping on tin rather than fine steel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hdd sounds more like scraping on tin rather than fine steel .\n->", + "output": "{\"text\": \"the hdd sounds more like scraping on tin rather than fine steel .\", \"labels\": \"[{'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hardware is still pretty sound .\n->hardware is still pretty sound .\n[{'aspect': 'hardware', 'opinion': 'sound', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the mac works like a new one .\n->the mac works like a new one .\n[{'aspect': 'mac', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: keyboard does seem a little off size as i seem to often be one key offset when get in typing position .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard does seem a little off size as i seem to often be one key offset when get in typing position .\n->", + "output": "{\"text\": \"keyboard does seem a little off size as i seem to often be one key offset when get in typing position .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 10 months in my battery will no longer charge .\n->10 months in my battery will no longer charge .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n->it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n[{'aspect': 'restaurant', 'opinion': 'repulsive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: internet runs well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ninternet runs well .\n->", + "output": "{\"text\": \"internet runs well .\", \"labels\": \"[{'aspect': 'internet', 'opinion': 'well', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen on this looks great , the bezels aren ' t noticeable .\n->the screen on this looks great , the bezels aren ' t noticeable .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': \"' t noticeable\", 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: best for : if you are looking for a travel laptop and are planning on doing light work this is an amazing buy for the price .\n->best for : if you are looking for a travel laptop and are planning on doing light work this is an amazing buy for the price .\n[{'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: downloading is very fast over wifi .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndownloading is very fast over wifi .\n->", + "output": "{\"text\": \"downloading is very fast over wifi .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen is nice .\n->screen is nice .\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: we concluded with tiramisu chocolate cake , both were delicious .\n->we concluded with tiramisu chocolate cake , both were delicious .\n[{'aspect': 'tiramisu chocolate cake', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i don ' t have need for the backlit keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t have need for the backlit keyboard .\n->", + "output": "{\"text\": \"i don ' t have need for the backlit keyboard .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: have frequented ' ino for several years and the food remains excellent .\n->have frequented ' ino for several years and the food remains excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: easy to setup and use\n->easy to setup and use\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: i ended up returning it even after getting a credit because the wireless did not work well and was extremely slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ended up returning it even after getting a credit because the wireless did not work well and was extremely slow .\n->", + "output": "{\"text\": \"i ended up returning it even after getting a credit because the wireless did not work well and was extremely slow .\", \"labels\": \"[{'aspect': 'wireless', 'opinion': 'not work well', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}, {'aspect': 'wireless', 'opinion': 'slow', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n->The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: by far , the best pizza in manhattan .\n->by far , the best pizza in manhattan .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: everything is amazing , love the look and everything about it until now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything is amazing , love the look and everything about it until now .\n->", + "output": "{\"text\": \"everything is amazing , love the look and everything about it until now .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it surfs the internet fast .\n->it surfs the internet fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: it ' s increasingly disappointing to see the much - hyped play access is still nonexistent .\n->it ' s increasingly disappointing to see the much - hyped play access is still nonexistent .\n[{'aspect': 'play access', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'SOFTWARE#QUALITY'}]\ntext: what this tells me is that the hdmi port on my chromebook is defective .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhat this tells me is that the hdmi port on my chromebook is defective .\n->", + "output": "{\"text\": \"what this tells me is that the hdmi port on my chromebook is defective .\", \"labels\": \"[{'aspect': 'hdmi port on my chromebook', 'opinion': 'defective', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love how quick this thing is .\n->i love how quick this thing is .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: i will be going back very soon .\n->i will be going back very soon .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n->", + "output": "{\"text\": \"i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: honestly the worst sushi my husband and i had in our entire lives .\n->honestly the worst sushi my husband and i had in our entire lives .\n[{'aspect': 'sushi', 'opinion': 'worst', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n->i chose this one mainly because of the full size keyboard , larger screen , and full size hdmi .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'larger', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'hdmi', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: manufactures need to quality check their products before sending them out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmanufactures need to quality check their products before sending them out .\n->", + "output": "{\"text\": \"manufactures need to quality check their products before sending them out .\", \"labels\": \"[{'aspect': 'manufactures', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n->kind of a small place but i guess if they are not too busy might be able to fit a group or kids .\n[{'aspect': 'place', 'opinion': 'small', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Their pad penang is delicious and everything else is fantastic .\n->Their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: after logging in to the replacement , the screen flashes black every fives seconds and restart the chrome browser .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter logging in to the replacement , the screen flashes black every fives seconds and restart the chrome browser .\n->", + "output": "{\"text\": \"after logging in to the replacement , the screen flashes black every fives seconds and restart the chrome browser .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen on this looks great , the bezels aren ' t noticeable .\n->the screen on this looks great , the bezels aren ' t noticeable .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': \"' t noticeable\", 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: and when it did work it was very slow .\n->and when it did work it was very slow .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the device itself is light and handsome - but virtually useless for long documents .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe device itself is light and handsome - but virtually useless for long documents .\n->", + "output": "{\"text\": \"the device itself is light and handsome - but virtually useless for long documents .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'handsome', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I expected quite a bit more from such an expensive menu .\n->I expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the thing that made me return it was the trackpad .\n->the thing that made me return it was the trackpad .\n[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: chromebook is not a writer ' s friend .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchromebook is not a writer ' s friend .\n->", + "output": "{\"text\": \"chromebook is not a writer ' s friend .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: oh and the customer service is garbage .\n->oh and the customer service is garbage .\n[{'aspect': 'customer service', 'opinion': 'garbage', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: in laptop mode the trackpad works very well for this .\n->in laptop mode the trackpad works very well for this .\n[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: the build - quality is pretty good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build - quality is pretty good .\n->", + "output": "{\"text\": \"the build - quality is pretty good .\", \"labels\": \"[{'aspect': 'build - quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: giving lower stars for not realizing its limited capabilities is your own fault .\n->giving lower stars for not realizing its limited capabilities is your own fault .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: everything lagged and the screen flickered .\n->everything lagged and the screen flickered .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: biggest gripe , no backlights on the keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbiggest gripe , no backlights on the keyboard .\n->", + "output": "{\"text\": \"biggest gripe , no backlights on the keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it fancies itself a convertible notebook / app consuming functional tablet , but it is not .\n->it fancies itself a convertible notebook / app consuming functional tablet , but it is not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: apple used to make a wonderful product but my computer is now a useless paperweight until i have a new charger sent to me .\n->apple used to make a wonderful product but my computer is now a useless paperweight until i have a new charger sent to me .\n[{'aspect': 'apple', 'opinion': 'wonderful', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'computer', 'opinion': 'useless', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: the keyboard is okay .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is okay .\n->", + "output": "{\"text\": \"the keyboard is okay .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was good too .\n->the food was good too .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The prices were CHEAP compared to the quality of service and food .\n->The prices were CHEAP compared to the quality of service and food .\n[{'aspect': 'prices', 'opinion': 'CHEAP', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the trackpad is mediocre in use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe trackpad is mediocre in use .\n->", + "output": "{\"text\": \"the trackpad is mediocre in use .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus is a great computer company .\n->asus is a great computer company .\n[{'aspect': 'asus', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'computer company', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: i haven ' t had issues with the track pad as others have .\n->i haven ' t had issues with the track pad as others have .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#USABILITY'}]\ntext: most of the time it works very well and one is subject to the vagaries of the various apps , browser , etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmost of the time it works very well and one is subject to the vagaries of the various apps , browser , etc .\n->", + "output": "{\"text\": \"most of the time it works very well and one is subject to the vagaries of the various apps , browser , etc .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Total hipster-wannabe attitude in an otherwise sweet spot .\n->Total hipster-wannabe attitude in an otherwise sweet spot .\n[{'aspect': 'spot', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n->For many people , this may not seem like Aunthentic Thai food because most places in NYC arent quite authentic .\n[{'aspect': 'Thai food', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i find the screen resolution to be very good for video .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni find the screen resolution to be very good for video .\n->", + "output": "{\"text\": \"i find the screen resolution to be very good for video .\", \"labels\": \"[{'aspect': 'screen resolution', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: easy to use .\n->easy to use .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n->the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n[{'aspect': 'baked clams octopus', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i can ' t really testify to its battery - life as i have not used it to the point where the battery is totally dissipated .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can ' t really testify to its battery - life as i have not used it to the point where the battery is totally dissipated .\n->", + "output": "{\"text\": \"i can ' t really testify to its battery - life as i have not used it to the point where the battery is totally dissipated .\", \"labels\": \"[{'aspect': 'battery - life', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can do it here .\n->you can do it here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: This is the best sushi in new york city - hands down .\n->This is the best sushi in new york city - hands down .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: all in all , i could live with this as my sole device if i had to\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all , i could live with this as my sole device if i had to\n->", + "output": "{\"text\": \"all in all , i could live with this as my sole device if i had to\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pasta was well cooked , did n ' t have enough sauce though or flavor .\n->the pasta was well cooked , did n ' t have enough sauce though or flavor .\n[{'aspect': 'pasta', 'opinion': 'well cooked', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n->the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'staff', 'opinion': 'not seem knowledgeable', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: after turning it on and logging into my google account i was getting an error when trying to run chrome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter turning it on and logging into my google account i was getting an error when trying to run chrome .\n->", + "output": "{\"text\": \"after turning it on and logging into my google account i was getting an error when trying to run chrome .\", \"labels\": \"[{'aspect': 'chrome', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n->The only positive was the wait staff , which was prompt , knowledgable , and likeable .\n[{'aspect': 'wait staff', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'knowledgable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait staff', 'opinion': 'likeable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Friendly and informative staff , very attentive and prompt raw bar service .\n->Friendly and informative staff , very attentive and prompt raw bar service .\n[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'informative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar service', 'opinion': 'raw', 'polarity': 'positive', 'category': 'NULL'}]\ntext: aside from that , it ' s a functioning web browser .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \naside from that , it ' s a functioning web browser .\n->", + "output": "{\"text\": \"aside from that , it ' s a functioning web browser .\", \"labels\": \"[{'aspect': 'web browser', 'opinion': 'functioning', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest i ' ve ever had ) .\n->the dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest i ' ve ever had ) .\n[{'aspect': 'dishes', 'opinion': 'unique', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dishes', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lamb sausages', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sardines with biscuits', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'large whole shrimp', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pistachio ice cream', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: when you add it all together , it just does n ' t seem worth it to me . . . especially considering the prices .\n->when you add it all together , it just does n ' t seem worth it to me . . . especially considering the prices .\n[{'aspect': 'NULL', 'opinion': \"does n ' t seem worth\", 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': \"does n ' t seem worth\", 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: we ' ll it ' s been a few weeks now with this chromebook now , aside from the initial issue , no problems .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwe ' ll it ' s been a few weeks now with this chromebook now , aside from the initial issue , no problems .\n->", + "output": "{\"text\": \"we ' ll it ' s been a few weeks now with this chromebook now , aside from the initial issue , no problems .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: be prepared to wait , because the place is pretty tiny .\n->be prepared to wait , because the place is pretty tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: Ravioli was good ... but I have to say that I found everything a bit overpriced .\n->Ravioli was good ... but I have to say that I found everything a bit overpriced .\n[{'aspect': 'Ravioli', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ravioli', 'opinion': 'overpriced', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they ' re like a little - known gem , practically unknown in my area .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey ' re like a little - known gem , practically unknown in my area .\n->", + "output": "{\"text\": \"they ' re like a little - known gem , practically unknown in my area .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gem', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would highly recommend this product if you want to get into music production like myself .\n->i would highly recommend this product if you want to get into music production like myself .\n[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I can not imagine better Indian food in all of the city .\n->I can not imagine better Indian food in all of the city .\n[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i have mixed feelings about this acer chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have mixed feelings about this acer chromebook .\n->", + "output": "{\"text\": \"i have mixed feelings about this acer chromebook .\", \"labels\": \"[{'aspect': 'acer chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it starts up fast .\n->it starts up fast .\n[{'aspect': 'starts up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: very , very nice\n->very , very nice\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: like the other one i borrowed , this one feels streamlined and easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlike the other one i borrowed , this one feels streamlined and easy to use .\n->", + "output": "{\"text\": \"like the other one i borrowed , this one feels streamlined and easy to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'streamlined', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu may be small , but everything on it is delicious .\n->The menu may be small , but everything on it is delicious .\n[{'aspect': 'menu', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n->i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n[{'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'processor', 'opinion': 'faster', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'google play store', 'opinion': 'compatibility', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: it ' s easy find and delete pics and files you ' ve downloaded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s easy find and delete pics and files you ' ve downloaded .\n->", + "output": "{\"text\": \"it ' s easy find and delete pics and files you ' ve downloaded .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n->it was n ' t as if this restaurant had any major bragging points before hand , but now it ' s simply repulsive .\n[{'aspect': 'restaurant', 'opinion': 'repulsive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the waiter was attentive , the food was delicious and the views of the city were great .\n->the waiter was attentive , the food was delicious and the views of the city were great .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'views of the city', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: they have a low - quality , hard plastic feel and a weird textured grain to them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey have a low - quality , hard plastic feel and a weird textured grain to them .\n->", + "output": "{\"text\": \"they have a low - quality , hard plastic feel and a weird textured grain to them .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'low', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'weird', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The flavors are very fresh and pretty unobtrusive , nothing flashy .\n->The flavors are very fresh and pretty unobtrusive , nothing flashy .\n[{'aspect': 'flavors', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavors', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: cozy romantic atomosphere with only around 15 tables at most .\n->cozy romantic atomosphere with only around 15 tables at most .\n[{'aspect': 'atomosphere', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atomosphere', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i wouldn ' t want to do any extensive typing on it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wouldn ' t want to do any extensive typing on it .\n->", + "output": "{\"text\": \"i wouldn ' t want to do any extensive typing on it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , i haven ' t had any disappoint with the battery .\n->however , i haven ' t had any disappoint with the battery .\n[{'aspect': 'battery', 'opinion': \"' t had any disappoint\", 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n->We went around 9:30 on a Friday and it had died down a bit by then so the service was great !\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the touchpad is hard and clunky .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchpad is hard and clunky .\n->", + "output": "{\"text\": \"the touchpad is hard and clunky .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'hard', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'clunky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The atmosphere is unheralded , the service impeccable , and the food magnificant .\n->The atmosphere is unheralded , the service impeccable , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i use some other apps but nothing is quite the same for my workflow .\n->i use some other apps but nothing is quite the same for my workflow .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: even using it a bit makes my hands / wrist uncomfortable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven using it a bit makes my hands / wrist uncomfortable .\n->", + "output": "{\"text\": \"even using it a bit makes my hands / wrist uncomfortable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->the atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the brightness and clarity are awesome .\n->the brightness and clarity are awesome .\n[{'aspect': 'brightness', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'clarity', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: doing general internet surfing is a breeze on this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndoing general internet surfing is a breeze on this laptop .\n->", + "output": "{\"text\": \"doing general internet surfing is a breeze on this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'breeze', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my husbands was perfect , my was well done and dry .\n->my husbands was perfect , my was well done and dry .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'well done', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: with my first day of use everything was terribly slow and updates took hours .\n->with my first day of use everything was terribly slow and updates took hours .\n[{'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: overall , it works well and is easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , it works well and is easy to use .\n->", + "output": "{\"text\": \"overall , it works well and is easy to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n->Granted the space is smaller than most , it is the best service you will find in even the largest of restaurants .\n[{'aspect': 'space', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I recieved prompt service with a smile .\n->I recieved prompt service with a smile .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s still good for basic internet usage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s still good for basic internet usage .\n->", + "output": "{\"text\": \"it ' s still good for basic internet usage .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n->the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n[{'aspect': 'pear torte', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'unable to provide', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'outdoor atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: pros : streamlined and simple ; easy to use overall ; easy to find / delete pics & files ; good price ; looks nice from the outside , with the lid down ; good 14 ` ` screen size that ' s surprisingly hard to find\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npros : streamlined and simple ; easy to use overall ; easy to find / delete pics & files ; good price ; looks nice from the outside , with the lid down ; good 14 ` ` screen size that ' s surprisingly hard to find\n->", + "output": "{\"text\": \"pros : streamlined and simple ; easy to use overall ; easy to find / delete pics & files ; good price ; looks nice from the outside , with the lid down ; good 14 ` ` screen size that ' s surprisingly hard to find\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'streamlined', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->I like Cafe Noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: cons : webcam doesn ' t have video - only takes pics ; awful , uncomfortable keyboard & trackpad ; chromebook extensions are lacking and don ' t address / make up for the limitations of this chromebook ; a bit heavy and clunky ; hard to figure out google cloud print\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncons : webcam doesn ' t have video - only takes pics ; awful , uncomfortable keyboard & trackpad ; chromebook extensions are lacking and don ' t address / make up for the limitations of this chromebook ; a bit heavy and clunky ; hard to figure out google cloud print\n->", + "output": "{\"text\": \"cons : webcam doesn ' t have video - only takes pics ; awful , uncomfortable keyboard & trackpad ; chromebook extensions are lacking and don ' t address / make up for the limitations of this chromebook ; a bit heavy and clunky ; hard to figure out google cloud print\", \"labels\": \"[{'aspect': 'webcam', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'clunky', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer had no answer for that question .\n->acer had no answer for that question .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: touch pad is a + + .\n->touch pad is a + + .\n[{'aspect': 'touch pad', 'opinion': 'a +', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: also on this model the ssd is not replaceable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso on this model the ssd is not replaceable .\n->", + "output": "{\"text\": \"also on this model the ssd is not replaceable .\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'not replaceable', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n->The brioche and lollies as party favors is a cute and sweet touch to a most memorable meal .\n[{'aspect': 'brioche and lollies', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'brioche and lollies', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'memorable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: their calzones are horrific , bad , vomit - inducing , yuck .\n->their calzones are horrific , bad , vomit - inducing , yuck .\n[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'calzones', 'opinion': 'vomit - inducing', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'calzones', 'opinion': 'yuck', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i went online and found that the 1 year acer warranty had already expired .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni went online and found that the 1 year acer warranty had already expired .\n->", + "output": "{\"text\": \"i went online and found that the 1 year acer warranty had already expired .\", \"labels\": \"[{'aspect': 'acer warranty', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'WARRANTY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n->the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: The waiter was attentive .\n->The waiter was attentive .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: also upon turning it on , i got a blue screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso upon turning it on , i got a blue screen .\n->", + "output": "{\"text\": \"also upon turning it on , i got a blue screen .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the perfect spot .\n->the perfect spot .\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n->purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n[{'aspect': '8th gen i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the trackpad is awful as are most acer trackpads .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe trackpad is awful as are most acer trackpads .\n->", + "output": "{\"text\": \"the trackpad is awful as are most acer trackpads .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'acer trackpads', 'opinion': 'awful', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s pretty fast even with heavy use and multiple applications running at once .\n->it ' s pretty fast even with heavy use and multiple applications running at once .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n->i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this happened 7 times within 20 minutes when i was working on something .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis happened 7 times within 20 minutes when i was working on something .\n->", + "output": "{\"text\": \"this happened 7 times within 20 minutes when i was working on something .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i can ' t justify paying that kind of money for some ridiculous upgrade .\n->i can ' t justify paying that kind of money for some ridiculous upgrade .\n[{'aspect': 'NULL', 'opinion': 'ridiculous', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: i am returning immediately , no patience for this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am returning immediately , no patience for this .\n->", + "output": "{\"text\": \"i am returning immediately , no patience for this .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + fingerprint sensor is accurate\n->+ fingerprint sensor is accurate\n[{'aspect': 'fingerprint sensor', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n->i will be out with friends and all of a sudden i am hungry and i only crave one thing . . . their pizza .\n[{'aspect': 'pizza', 'opinion': 'crave', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: everything lagged and the screen flickered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything lagged and the screen flickered .\n->", + "output": "{\"text\": \"everything lagged and the screen flickered .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very clean computer everything looks brand new !\n->very clean computer everything looks brand new !\n[{'aspect': 'computer', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: moderate prices .\n->moderate prices .\n[{'aspect': 'NULL', 'opinion': 'moderate', 'polarity': 'neutral', 'category': 'RESTAURANT#PRICES'}]\ntext: i just received the computer back from repairs and it worked for about 2 days and the same problem started happening again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just received the computer back from repairs and it worked for about 2 days and the same problem started happening again .\n->", + "output": "{\"text\": \"i just received the computer back from repairs and it worked for about 2 days and the same problem started happening again .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is not worth the prices .\n->This place is not worth the prices .\n[{'aspect': 'place', 'opinion': 'not worth the prices', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n->The service is a bit slow , but harkens back to my years growing up in Napoli , Italy where things are not rushed and when you sit down for dinner the table is yours all night .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\ntext: unfortunately this chromebook is very sluggish .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nunfortunately this chromebook is very sluggish .\n->", + "output": "{\"text\": \"unfortunately this chromebook is very sluggish .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'sluggish', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to bad the battery did n ' t work out .\n->to bad the battery did n ' t work out .\n[{'aspect': 'battery', 'opinion': 'bad', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: for me has been worth the $ 500 for the computer .\n->for me has been worth the $ 500 for the computer .\n[{'aspect': 'computer', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: i wouldn ' t recommend this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wouldn ' t recommend this chromebook .\n->", + "output": "{\"text\": \"i wouldn ' t recommend this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': \"' t recommend\", 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wont come back again for sure !\n->wont come back again for sure !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n->Food was just average ... if they lowered the prices just a bit , it would be a bigger draw .\n[{'aspect': 'Food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: while the product worked decently for about a month , it went downhill soon after .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile the product worked decently for about a month , it went downhill soon after .\n->", + "output": "{\"text\": \"while the product worked decently for about a month , it went downhill soon after .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'decently', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n->I recommend the jelly fish , drunken chicken and the soupy dumplings , certainly the stir fry blue crab .\n[{'aspect': 'jelly fish', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drunken chicken', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soupy dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'stir fry blue crab', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the freshest , best variety , and the fastest delivery .\n->the freshest , best variety , and the fastest delivery .\n[{'aspect': 'delivery', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n->", + "output": "{\"text\": \"not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's the perfect restaurant for NY life style , it got cool design , awesome drinks and food and lot 's of good looking people eating and hanging at the pink bar ...\n->It 's the perfect restaurant for NY life style , it got cool design , awesome drinks and food and lot 's of good looking people eating and hanging at the pink bar ...\n[{'aspect': 'design', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'pink', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n->Each table has a pot of boiling water sunken into its surface , and you get platters of thin sliced meats , various vegetables , and rice and glass noodles .\n[{'aspect': 'meats', 'opinion': 'thin', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'various', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: computer arrived doa .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomputer arrived doa .\n->", + "output": "{\"text\": \"computer arrived doa .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n->all the various greek and cypriot dishes are excellent , but the gyro is the reason to come - - if you do n ' t eat one your trip was wasted .\n[{'aspect': 'greek and cypriot dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'gyro', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I highly recommend the Sophia pizza .\n->I highly recommend the Sophia pizza .\n[{'aspect': 'Sophia pizza', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the keys and mouse pad are responsive and comfortable to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keys and mouse pad are responsive and comfortable to use .\n->", + "output": "{\"text\": \"the keys and mouse pad are responsive and comfortable to use .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keys', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the battery is far below what i expected .\n->the battery is far below what i expected .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: i had the best ravioli ever .\n->i had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: now my main complaint is that is it very slow , especially for a new computer !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow my main complaint is that is it very slow , especially for a new computer !\n->", + "output": "{\"text\": \"now my main complaint is that is it very slow , especially for a new computer !\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sound quality is ok , and even at full volume isn ' t that loud , but that ' s not a big deal for me .\n->the sound quality is ok , and even at full volume isn ' t that loud , but that ' s not a big deal for me .\n[{'aspect': 'sound quality', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'sound quality', 'opinion': \"' t that loud\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: - 360 degrees flipping is actually pretty practical\n->- 360 degrees flipping is actually pretty practical\n[{'aspect': '360 degrees flipping', 'opinion': 'practical', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: big learning curve , would hate to see someone older try and figure it out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbig learning curve , would hate to see someone older try and figure it out .\n->", + "output": "{\"text\": \"big learning curve , would hate to see someone older try and figure it out .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'hate', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speakers are not great , but bluetooth connection to an external speaker is standard these days and it ' s how we watch movies .\n->the speakers are not great , but bluetooth connection to an external speaker is standard these days and it ' s how we watch movies .\n[{'aspect': 'bluetooth connection', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: My wife had the fried shrimp which are huge and loved it .\n->My wife had the fried shrimp which are huge and loved it .\n[{'aspect': 'fried shrimp', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fried shrimp', 'opinion': 'loved', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the drag and drop works poorly which is very annoying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe drag and drop works poorly which is very annoying .\n->", + "output": "{\"text\": \"the drag and drop works poorly which is very annoying .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Be sure to try the seasonal , and always delicious , specials .\n->Be sure to try the seasonal , and always delicious , specials .\n[{'aspect': 'specials', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'seasonal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'specials', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my main complain involves terrible battery life .\n->my main complain involves terrible battery life .\n[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: on the upside , the internet is lightning fast and it interfaces with tv through hdmi which is great , is bluetooth compatible and has two usb ports .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non the upside , the internet is lightning fast and it interfaces with tv through hdmi which is great , is bluetooth compatible and has two usb ports .\n->", + "output": "{\"text\": \"on the upside , the internet is lightning fast and it interfaces with tv through hdmi which is great , is bluetooth compatible and has two usb ports .\", \"labels\": \"[{'aspect': 'internet', 'opinion': 'fast', 'polarity': 'positive', 'category': 'PORTS#USABILITY'}, {'aspect': 'hdmi', 'opinion': 'great', 'polarity': 'positive', 'category': 'PORTS#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n->We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n[{'aspect': 'lox', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overall a good buy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall a good buy .\n->", + "output": "{\"text\": \"overall a good buy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'secret back room', 'opinion': 'Check out', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Best Reuben sandwich ever !\n->Best Reuben sandwich ever !\n[{'aspect': 'Reuben sandwich', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: liked it when it was working , but it ' s a paperweight now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nliked it when it was working , but it ' s a paperweight now .\n->", + "output": "{\"text\": \"liked it when it was working , but it ' s a paperweight now .\", \"labels\": \"[{'aspect': 'it', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I absolutely love this place ! ! !\n->I absolutely love this place ! ! !\n[{'aspect': 'place', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: acer had no answer for that question .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer had no answer for that question .\n->", + "output": "{\"text\": \"acer had no answer for that question .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great battery life , a matte screen ( non - glossy ) full hd .\n->great battery life , a matte screen ( non - glossy ) full hd .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'matte screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: The staff there is very attentive and down to earth .\n->The staff there is very attentive and down to earth .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'down to earth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: next day during a video froze and just kept looping one section as it froze , and then froze and made a horrible loud scratching noise .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnext day during a video froze and just kept looping one section as it froze , and then froze and made a horrible loud scratching noise .\n->", + "output": "{\"text\": \"next day during a video froze and just kept looping one section as it froze , and then froze and made a horrible loud scratching noise .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am really concerned that this was not strongly made and just pretty .\n->i am really concerned that this was not strongly made and just pretty .\n[{'aspect': 'NULL', 'opinion': 'just pretty', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i sit with it in my lap all day long and it never gets hot .\n->i sit with it in my lap all day long and it never gets hot .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: completely aggravated , sending this crap back as soon as i hear from them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncompletely aggravated , sending this crap back as soon as i hear from them .\n->", + "output": "{\"text\": \"completely aggravated , sending this crap back as soon as i hear from them .\", \"labels\": \"[{'aspect': 'crap', 'opinion': 'crap', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n->for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: volume was not working .\n->volume was not working .\n[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: computer was used very little and stopped working after 6 months\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomputer was used very little and stopped working after 6 months\n->", + "output": "{\"text\": \"computer was used very little and stopped working after 6 months\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the track pad has now stopped working .\n->the track pad has now stopped working .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: in fact , it appears he is going to go postal at any moment .\n->in fact , it appears he is going to go postal at any moment .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: technical support was easy to reach , but not able to stop the problem i was having .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntechnical support was easy to reach , but not able to stop the problem i was having .\n->", + "output": "{\"text\": \"technical support was easy to reach , but not able to stop the problem i was having .\", \"labels\": \"[{'aspect': 'technical support', 'opinion': 'easy', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i just got this yesterday and i am very satisfied with the speed .\n->i just got this yesterday and i am very satisfied with the speed .\n[{'aspect': 'speed', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Spreads and toppings are great - though a bit pricey .\n->Spreads and toppings are great - though a bit pricey .\n[{'aspect': 'Spreads', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'Spreads', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\ntext: support got quite unpleasant when i ask about replacement .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsupport got quite unpleasant when i ask about replacement .\n->", + "output": "{\"text\": \"support got quite unpleasant when i ask about replacement .\", \"labels\": \"[{'aspect': 'support', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n->During the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the pad se ew chicken was delicious , however the pad thai was far too oily .\n->the pad se ew chicken was delicious , however the pad thai was far too oily .\n[{'aspect': 'pad se ew chicken', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\ntext: other than that , i like it , and this is my first chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother than that , i like it , and this is my first chromebook .\n->", + "output": "{\"text\": \"other than that , i like it , and this is my first chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ca n ' t go wrong here .\n->you ca n ' t go wrong here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it ' s performance is quite zippy and the screen is very sharp and bright .\n->it ' s performance is quite zippy and the screen is very sharp and bright .\n[{'aspect': 'performance', 'opinion': 'zippy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: but this one was a piece of trash .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut this one was a piece of trash .\n->", + "output": "{\"text\": \"but this one was a piece of trash .\", \"labels\": \"[{'aspect': 'trash', 'opinion': 'trash', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: noisy hdr , better with ssd ) works quickly .\n->noisy hdr , better with ssd ) works quickly .\n[{'aspect': 'hdr', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}, {'aspect': 'ssd', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: we decided to eat in tea room which was small and cute .\n->we decided to eat in tea room which was small and cute .\n[{'aspect': 'tea room', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tea room', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}]\ntext: power cord broke within the first two weeks of use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npower cord broke within the first two weeks of use .\n->", + "output": "{\"text\": \"power cord broke within the first two weeks of use .\", \"labels\": \"[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so , i originally purchased this for the travel conveniences .\n->so , i originally purchased this for the travel conveniences .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: I do n't know who they think they are but they have no respect for the residents of the neighborhood ever since they opened their cabaret next door and blasts loud music till three in the morning every weekend during the summer .\n->I do n't know who they think they are but they have no respect for the residents of the neighborhood ever since they opened their cabaret next door and blasts loud music till three in the morning every weekend during the summer .\n[{'aspect': 'music', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}]\ntext: otherwise computer seems okay .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \notherwise computer seems okay .\n->", + "output": "{\"text\": \"otherwise computer seems okay .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , the problems are fairly minor and for the price i ' m happy with what i got .\n->overall , the problems are fairly minor and for the price i ' m happy with what i got .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' ve had it for less than two months it randomly shuts down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had it for less than two months it randomly shuts down .\n->", + "output": "{\"text\": \"i ' ve had it for less than two months it randomly shuts down .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n->i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this asus worked right out of the box and was very responsive .\n->this asus worked right out of the box and was very responsive .\n[{'aspect': 'asus', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: trackpad keeps breaking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntrackpad keeps breaking .\n->", + "output": "{\"text\": \"trackpad keeps breaking .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While the prices are nothing special , the portions are huge .\n->While the prices are nothing special , the portions are huge .\n[{'aspect': 'prices', 'opinion': 'special', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The wine list is extensive and impressive .\n->The wine list is extensive and impressive .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: acer support is awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer support is awful .\n->", + "output": "{\"text\": \"acer support is awful .\", \"labels\": \"[{'aspect': 'acer support', 'opinion': 'awful', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is reliable and the price is moderate .\n->The food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: best dining experience in the west village !\n->best dining experience in the west village !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\n->", + "output": "{\"text\": \"other people online seem to have this same problem with the trackpad , but acer pretends they have never heard of it .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * very solidly built and it transitions nicely from laptop to tablet mode .\n->* very solidly built and it transitions nicely from laptop to tablet mode .\n[{'aspect': 'built', 'opinion': 'solidly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'tablet', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the three of us standing in front of her should have been an indication of how many of us there were .\n->the three of us standing in front of her should have been an indication of how many of us there were .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: third time in 5 months that the touchpad became unresponsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthird time in 5 months that the touchpad became unresponsive .\n->", + "output": "{\"text\": \"third time in 5 months that the touchpad became unresponsive .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'unresponsive', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: therefore , my advice to you if you ' re a student and you need a laptop for school , this laptop is the best choice for you .\n->therefore , my advice to you if you ' re a student and you need a laptop for school , this laptop is the best choice for you .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it is a good product to buy .\n->it is a good product to buy .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: very low quality build and quality support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery low quality build and quality support .\n->", + "output": "{\"text\": \"very low quality build and quality support .\", \"labels\": \"[{'aspect': 'quality build', 'opinion': 'low', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'quality support', 'opinion': 'low', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am beyond impressed with this little machine , i would absolutly buy this again !\n->i am beyond impressed with this little machine , i would absolutly buy this again !\n[{'aspect': 'machine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the picture quality seems .\n->the picture quality seems .\n[{'aspect': 'picture quality', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: was able to get past setting up the log in info , but then once you log in the screen continuously goes black and comes back on and goes black and comes back on ; continuous cycle .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwas able to get past setting up the log in info , but then once you log in the screen continuously goes black and comes back on and goes black and comes back on ; continuous cycle .\n->", + "output": "{\"text\": \"was able to get past setting up the log in info , but then once you log in the screen continuously goes black and comes back on and goes black and comes back on ; continuous cycle .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the yellowtail was particularly good as well .\n->the yellowtail was particularly good as well .\n[{'aspect': 'yellowtail', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: bad staff\n->bad staff\n[{'aspect': 'staff', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i ' m thinking i got a dud , so i ' ll update this review once i can get it resolved hopefully with a replacement .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m thinking i got a dud , so i ' ll update this review once i can get it resolved hopefully with a replacement .\n->", + "output": "{\"text\": \"i ' m thinking i got a dud , so i ' ll update this review once i can get it resolved hopefully with a replacement .\", \"labels\": \"[{'aspect': 'dud', 'opinion': 'dud', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: works great as a chromebook but chromebooks are still very limited for android compatibility , at least this one is .\n->works great as a chromebook but chromebooks are still very limited for android compatibility , at least this one is .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'limited', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n->Small servings for main entree , i had salmon ( wasnt impressed ) girlfriend had chicken , it was good .\n[{'aspect': 'salmon', 'opinion': 'wasnt impressed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'servings', 'opinion': 'Small', 'polarity': 'negative', 'category': 'NULL'}]\ntext: one of the two usb ports is defective , which is an enormous pain .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of the two usb ports is defective , which is an enormous pain .\n->", + "output": "{\"text\": \"one of the two usb ports is defective , which is an enormous pain .\", \"labels\": \"[{'aspect': 'usb ports', 'opinion': 'defective', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}, {'aspect': 'usb ports', 'opinion': 'pain', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: awesome\n->awesome\n[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n->if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n[{'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: also , chromeos does not allow color / temperature calibration of the display device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso , chromeos does not allow color / temperature calibration of the display device .\n->", + "output": "{\"text\": \"also , chromeos does not allow color / temperature calibration of the display device .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen backlight stopped working after just one month of light use .\n->screen backlight stopped working after just one month of light use .\n[{'aspect': 'screen backlight', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Even better , they know how to cook French classics like Steak au Poivre and Onglet without burning it to death or overcooking it .\n->Even better , they know how to cook French classics like Steak au Poivre and Onglet without burning it to death or overcooking it .\n[{'aspect': 'Steak au Poivre', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Onglet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i gave it 3 out of 5 stars , because there is no sd card slot !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni gave it 3 out of 5 stars , because there is no sd card slot !\n->", + "output": "{\"text\": \"i gave it 3 out of 5 stars , because there is no sd card slot !\", \"labels\": \"[{'aspect': 'sd card slot', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was good and food is wonderful .\n->Service was good and food is wonderful .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is an amazing place to try some roti rolls .\n->this is an amazing place to try some roti rolls .\n[{'aspect': 'roti rolls', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: nice screen and keyboard , touch pad is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice screen and keyboard , touch pad is great .\n->", + "output": "{\"text\": \"nice screen and keyboard , touch pad is great .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n->It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n[{'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n->My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n[{'aspect': 'cheese', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}]\ntext: then it just rebooted without prompt .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen it just rebooted without prompt .\n->", + "output": "{\"text\": \"then it just rebooted without prompt .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it would power on , start to boot , then abruptly power down .\n->it would power on , start to boot , then abruptly power down .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: on the other hand , it is not an easy task to open and replace hdd .\n->on the other hand , it is not an easy task to open and replace hdd .\n[{'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: it ' s already bricked and i did n ' t even use it for more than one day .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s already bricked and i did n ' t even use it for more than one day .\n->", + "output": "{\"text\": \"it ' s already bricked and i did n ' t even use it for more than one day .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'bricked', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n->with a solid cpu and 4gb of memory i can run a dozen or more open tabs with good performance .\n[{'aspect': 'cpu', 'opinion': 'solid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'solid', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'memory', 'opinion': 'good', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i found the food , service and value exceptional everytime i have been there .\n->i found the food , service and value exceptional everytime i have been there .\n[{'aspect': 'food', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: very disappointed with the wireless radio in this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery disappointed with the wireless radio in this chromebook .\n->", + "output": "{\"text\": \"very disappointed with the wireless radio in this chromebook .\", \"labels\": \"[{'aspect': 'wireless radio in this chromebook', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is amazing .\n->this chromebook is amazing .\n[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: if you ' re looking for a good chromebook , this is the one for you .\n->if you ' re looking for a good chromebook , this is the one for you .\n[{'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the sad part is i truly do like acer products , but this made me rethink this purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sad part is i truly do like acer products , but this made me rethink this purchase .\n->", + "output": "{\"text\": \"the sad part is i truly do like acer products , but this made me rethink this purchase .\", \"labels\": \"[{'aspect': 'acer products', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'acer products', 'opinion': 'sad', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our server was very helpful and friendly .\n->our server was very helpful and friendly .\n[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: i really wanted to like this chromebook .\n->i really wanted to like this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'wanted to like', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it ' s terrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s terrible .\n->", + "output": "{\"text\": \"it ' s terrible .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is so cool and the service is prompt and curtious .\n->The place is so cool and the service is prompt and curtious .\n[{'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'curtious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service was excellent and the food was delicious .\n->The service was excellent and the food was delicious .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\n->", + "output": "{\"text\": \"i could have bought a new pc for the money , but figured the slim chromebook was handy , and the word on line was it was great for exactly the reasons i wanted it , as a wireless entertainment center .\", \"labels\": \"[{'aspect': 'word on line', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The first time the sushi was outstanding , the second time it was a little bland .\n->The first time the sushi was outstanding , the second time it was a little bland .\n[{'aspect': 'sushi', 'opinion': 'outstanding', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Outstanding Bagels , but you get what you pay for .\n->Outstanding Bagels , but you get what you pay for .\n[{'aspect': 'Bagels', 'opinion': 'Outstanding', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i wish i could give it away at this point .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wish i could give it away at this point .\n->", + "output": "{\"text\": \"i wish i could give it away at this point .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place was nice and calm .\n->The place was nice and calm .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'calm', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the service varys from day to day - sometimes they ' re very nice , and sometimes not .\n->the service varys from day to day - sometimes they ' re very nice , and sometimes not .\n[{'aspect': 'service', 'opinion': 'varys', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: acer ' s customer service is by far the worst .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer ' s customer service is by far the worst .\n->", + "output": "{\"text\": \"acer ' s customer service is by far the worst .\", \"labels\": \"[{'aspect': \"acer ' s customer service\", 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my first mac and i ' m in love .\n->my first mac and i ' m in love .\n[{'aspect': 'mac', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i wound up returning it .\n->i wound up returning it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n->", + "output": "{\"text\": \"very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'sound', 'opinion': 'big', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'boot times', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service is awful .\n->The service is awful .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n->not only did i get the laptop with a dented corner , the screen constantly flickers and cuts out on occasion , the entire laptop freezes for no discernible reason , and the mouse has completely stopped working twice .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: the track pad is one of the best i have seen for a non - apple touch pad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe track pad is one of the best i have seen for a non - apple touch pad .\n->", + "output": "{\"text\": \"the track pad is one of the best i have seen for a non - apple touch pad .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But the staff was so horrible to us .\n->But the staff was so horrible to us .\n[{'aspect': 'staff', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i love this chromebook !\n->i love this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: feels nice and looks good but it truly is the worst chromebook on the market !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfeels nice and looks good but it truly is the worst chromebook on the market !\n->", + "output": "{\"text\": \"feels nice and looks good but it truly is the worst chromebook on the market !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n->although it can be a little slow at times , overall it runs great , and can run most popular games at a decent fps on medium settings .\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: we thought that this place is using too much of msg cooking in the foods .\n->we thought that this place is using too much of msg cooking in the foods .\n[{'aspect': 'foods', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: picks up wireless signals weakly !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npicks up wireless signals weakly !\n->", + "output": "{\"text\": \"picks up wireless signals weakly !\", \"labels\": \"[{'aspect': 'wireless signals', 'opinion': 'weakly', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n->Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch buffet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n->i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\ntext: the screen flickers , freezes , and the machine even restarts itself on some occasions .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen flickers , freezes , and the machine even restarts itself on some occasions .\n->", + "output": "{\"text\": \"the screen flickers , freezes , and the machine even restarts itself on some occasions .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is easy to use , light , and you have the ability to download apps for just about any need .\n->it is easy to use , light , and you have the ability to download apps for just about any need .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: great device until battery won ' t charge .\n->great device until battery won ' t charge .\n[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: the screen display isn ' t bright at all !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen display isn ' t bright at all !\n->", + "output": "{\"text\": \"the screen display isn ' t bright at all !\", \"labels\": \"[{'aspect': 'screen display', 'opinion': \"' t bright at all\", 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n->i loved everything about this chromebook until it stop charging 2 moths after perches , light just keeps flashing green .\n[{'aspect': 'chromebook', 'opinion': 'loved', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}]\nExample:\ntext: i ' m saving up for my next visit .\n->i ' m saving up for my next visit .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: even the keyboard is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven the keyboard is fantastic .\n->", + "output": "{\"text\": \"even the keyboard is fantastic .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n->Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n[{'aspect': 'fresh mozzerella slices', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozzerella slices', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Plain Cheese slice', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: lenovo should put a better battery in it , and should make a retrofit available .\n->lenovo should put a better battery in it , and should make a retrofit available .\n[{'aspect': 'better', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#DESIGN_FEATURES'}]\ntext: overall , worst chromebook ever and i can ' t wait until it dies !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , worst chromebook ever and i can ' t wait until it dies !\n->", + "output": "{\"text\": \"overall , worst chromebook ever and i can ' t wait until it dies !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent price , i bought it for a beginner in art design field .\n->excellent price , i bought it for a beginner in art design field .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: we asked for sides which the waiter than admitted that he forgot to put in that part of our order .\n->we asked for sides which the waiter than admitted that he forgot to put in that part of our order .\n[{'aspect': 'waiter', 'opinion': 'forgot', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i would not recommend this as a primary chromebook or even to buy it at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would not recommend this as a primary chromebook or even to buy it at all .\n->", + "output": "{\"text\": \"i would not recommend this as a primary chromebook or even to buy it at all .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: crisp screen .\n->crisp screen .\n[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: i love this chromebook !\n->i love this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: update - this thing frustrated me so much over the past month that i just threw it in the trash ( where it belongs ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupdate - this thing frustrated me so much over the past month that i just threw it in the trash ( where it belongs ) .\n->", + "output": "{\"text\": \"update - this thing frustrated me so much over the past month that i just threw it in the trash ( where it belongs ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'frustrated', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not a great place for family or general dining .\n->not a great place for family or general dining .\n[{'aspect': 'place', 'opinion': 'not a great', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Patroon features a nice cigar bar and has great staff .\n->Patroon features a nice cigar bar and has great staff .\n[{'aspect': 'cigar bar', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: personally , i would steer clear of this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npersonally , i would steer clear of this chromebook .\n->", + "output": "{\"text\": \"personally , i would steer clear of this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one special roll and one regular roll is enough to fill you up , but save room for dessert !\n->one special roll and one regular roll is enough to fill you up , but save room for dessert !\n[{'aspect': 'dessert', 'opinion': 'save room', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'special roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'regular roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: 11 months after the purchase , it died .\n->11 months after the purchase , it died .\n[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it started to get slow a week ago .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit started to get slow a week ago .\n->", + "output": "{\"text\": \"it started to get slow a week ago .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - android support is only in beta mode as of february 5 , 2017 .\n->- android support is only in beta mode as of february 5 , 2017 .\n[{'aspect': 'android support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n->it was crashing , it would restart and then the monitor would not work until i plugged it into an external one , and even then it would usually require a restart again .\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: now the curser / track pad is gone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow the curser / track pad is gone .\n->", + "output": "{\"text\": \"now the curser / track pad is gone .\", \"labels\": \"[{'aspect': 'curser / track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The owners and employees are friendly and their pizza is fantastic .\n->The owners and employees are friendly and their pizza is fantastic .\n[{'aspect': 'owners', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'employees', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n->the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n[{'aspect': 'computer', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'well - made', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: terrible product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nterrible product .\n->", + "output": "{\"text\": \"terrible product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fast service .\n->Fast service .\n[{'aspect': 'service', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n->They are still living in the dark ages and do not have an answering machine , so if you want to make a reservation you are limited .\n[{'aspect': 'reservation', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\ntext: if you use wix to edit websites the scroll bar will not appear once you enter editor page , thus making it impossible to design / build / update websites .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you use wix to edit websites the scroll bar will not appear once you enter editor page , thus making it impossible to design / build / update websites .\n->", + "output": "{\"text\": \"if you use wix to edit websites the scroll bar will not appear once you enter editor page , thus making it impossible to design / build / update websites .\", \"labels\": \"[{'aspect': 'wix', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + great and fast cpu and overall fast pc performance\n->+ great and fast cpu and overall fast pc performance\n[{'aspect': 'cpu', 'opinion': 'great', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'cpu', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'pc performance', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: This is some really good , inexpensive sushi .\n->This is some really good , inexpensive sushi .\n[{'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n->", + "output": "{\"text\": \"i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\", \"labels\": \"[{'aspect': 'touch pad', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is very easy to use the set up was awesome .\n->it is very easy to use the set up was awesome .\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}, {'aspect': 'set up', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: i like the size but dislike the battery life .\n->i like the size but dislike the battery life .\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'dislike', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: do not buy this machine if you ' re hoping to run android apps .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not buy this machine if you ' re hoping to run android apps .\n->", + "output": "{\"text\": \"do not buy this machine if you ' re hoping to run android apps .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Our server was very helpful and friendly .\n->Our server was very helpful and friendly .\n[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i had lobster bisque it has 2 oz . of maine lobster in it .\n->i had lobster bisque it has 2 oz . of maine lobster in it .\n[{'aspect': 'lobster bisque', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: after charging the unit for 2 hours i discovered that the unit will only operate while the charger is connected .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter charging the unit for 2 hours i discovered that the unit will only operate while the charger is connected .\n->", + "output": "{\"text\": \"after charging the unit for 2 hours i discovered that the unit will only operate while the charger is connected .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n->Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n->the menu looked great , and the waiter was very nice , but when the food came , it was average .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: keys are a bit thin and have an odd feel to them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeys are a bit thin and have an odd feel to them .\n->", + "output": "{\"text\": \"keys are a bit thin and have an odd feel to them .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'thin', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: really happy with this laptop !\n->really happy with this laptop !\n[{'aspect': 'laptop', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: this unit is a great compromise between powerful cpu and gpu with good battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis unit is a great compromise between powerful cpu and gpu with good battery life .\n->", + "output": "{\"text\": \"this unit is a great compromise between powerful cpu and gpu with good battery life .\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'gpu', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}, {'aspect': 'unit', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: do n ' t be fooled by crowds of people .\n->do n ' t be fooled by crowds of people .\n[{'aspect': 'NULL', 'opinion': 'fooled', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: The red curry is weak and tasteless , the pad thai is stuck together and lumpy , the rice is often overcooked , and the seafood is pretty sketchy .\n->The red curry is weak and tasteless , the pad thai is stuck together and lumpy , the rice is often overcooked , and the seafood is pretty sketchy .\n[{'aspect': 'red curry', 'opinion': 'weak', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'red curry', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pad thai', 'opinion': 'lumpy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rice', 'opinion': 'overcooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'sketchy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - timeout on keyboard backlight not adjustable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- timeout on keyboard backlight not adjustable .\n->", + "output": "{\"text\": \"- timeout on keyboard backlight not adjustable .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'not adjustable', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my chow fun and chow see was really bland and oily .\n->my chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n->this computer was phenomenal for 12 days and then the charger broke and it is no longer functional .\n[{'aspect': 'computer', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: acer refused to pay for shipping it back to them for warranty repairs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nacer refused to pay for shipping it back to them for warranty repairs .\n->", + "output": "{\"text\": \"acer refused to pay for shipping it back to them for warranty repairs .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n->i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the keyboard / mousepad isn ' t super comfortable for casual use ( like on your lap , sitting on a couch ) , so i think it is more meant to be used more or less exclusively for gaming at a desk or table .\n->the keyboard / mousepad isn ' t super comfortable for casual use ( like on your lap , sitting on a couch ) , so i think it is more meant to be used more or less exclusively for gaming at a desk or table .\n[{'aspect': 'keyboard / mousepad', 'opinion': \"' t super comfortable\", 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\ntext: that seemed to correct problem , but problem returned next day and the battery would only charge up to 1 % with charger plugged in .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat seemed to correct problem , but problem returned next day and the battery would only charge up to 1 % with charger plugged in .\n->", + "output": "{\"text\": \"that seemed to correct problem , but problem returned next day and the battery would only charge up to 1 % with charger plugged in .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while under the warranty , asus sent me a fedex account with no instructions .\n->while under the warranty , asus sent me a fedex account with no instructions .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: sound is .\n->sound is .\n[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: monitor went out 35 days after receiving .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmonitor went out 35 days after receiving .\n->", + "output": "{\"text\": \"monitor went out 35 days after receiving .\", \"labels\": \"[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Compared to Ess-a , Tal offers a less doughy bagel !\n->Compared to Ess-a , Tal offers a less doughy bagel !\n[{'aspect': 'bagel', 'opinion': 'less doughy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: love the cortana !\n->love the cortana !\n[{'aspect': 'cortana', 'opinion': 'love', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: powered it down and back up a few time to check boot times and now ibhave huge black lines running down the screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npowered it down and back up a few time to check boot times and now ibhave huge black lines running down the screen .\n->", + "output": "{\"text\": \"powered it down and back up a few time to check boot times and now ibhave huge black lines running down the screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 've never had bad service and the fish is fresh and delicious .\n->I 've never had bad service and the fish is fresh and delicious .\n[{'aspect': 'service', 'opinion': 'never had bad', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We were also seated promptly at the time of our reservation and the service was very quick and professional .\n->We were also seated promptly at the time of our reservation and the service was very quick and professional .\n[{'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: display driver fails 3x a day .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndisplay driver fails 3x a day .\n->", + "output": "{\"text\": \"display driver fails 3x a day .\", \"labels\": \"[{'aspect': 'display driver', 'opinion': 'fails', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Hats off to the chef .\n->Hats off to the chef .\n[{'aspect': 'chef', 'opinion': 'Hats off', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - screen feels smaller than other of the same size .\n->- screen feels smaller than other of the same size .\n[{'aspect': 'screen', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: loses wifi connection every hour .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nloses wifi connection every hour .\n->", + "output": "{\"text\": \"loses wifi connection every hour .\", \"labels\": \"[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - keyboard keys have a shorter touch .\n->- keyboard keys have a shorter touch .\n[{'aspect': 'keyboard keys', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: The atmosphere was crowded but it was a great bistro-type vibe .\n->The atmosphere was crowded but it was a great bistro-type vibe .\n[{'aspect': 'bistro-type vibe', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: they are simply unreliable , poorly made laptops .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey are simply unreliable , poorly made laptops .\n->", + "output": "{\"text\": \"they are simply unreliable , poorly made laptops .\", \"labels\": \"[{'aspect': 'laptops', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptops', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was slow had to wait to order and get food although not crowded .\n->Service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: ok battery life , 4 or 5 hours of continuous use , but i ' m never far away from a plug so no big deal really .\n->ok battery life , 4 or 5 hours of continuous use , but i ' m never far away from a plug so no big deal really .\n[{'aspect': 'battery life', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: its a hardware issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits a hardware issue .\n->", + "output": "{\"text\": \"its a hardware issue .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The prices were CHEAP compared to the quality of service and food .\n->The prices were CHEAP compared to the quality of service and food .\n[{'aspect': 'prices', 'opinion': 'CHEAP', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Ambience is delightful , service impeccable .\n->Ambience is delightful , service impeccable .\n[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the picture was very bright and clear , the back - lit keyboard was a very nice feature , and it seemed like a good value for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe picture was very bright and clear , the back - lit keyboard was a very nice feature , and it seemed like a good value for the price .\n->", + "output": "{\"text\": \"the picture was very bright and clear , the back - lit keyboard was a very nice feature , and it seemed like a good value for the price .\", \"labels\": \"[{'aspect': 'picture', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'picture', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'back - lit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service was friendly and the atmosphere was casual .\n->the service was friendly and the atmosphere was casual .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'casual', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: everything is very smooth and fast .\n->everything is very smooth and fast .\n[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the internet download speed with the acer was akin to an old dial - up modem speed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe internet download speed with the acer was akin to an old dial - up modem speed .\n->", + "output": "{\"text\": \"the internet download speed with the acer was akin to an old dial - up modem speed .\", \"labels\": \"[{'aspect': 'internet download speed with the acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great machine out of the box .\n->great machine out of the box .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: what more can you ask for ?\n->what more can you ask for ?\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: as of this writing , the computer ' s dedicated video card is completely non - functional , the computer routinely switches off in the middle of executing a process , and i can ' t even use the hdmi out port .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas of this writing , the computer ' s dedicated video card is completely non - functional , the computer routinely switches off in the middle of executing a process , and i can ' t even use the hdmi out port .\n->", + "output": "{\"text\": \"as of this writing , the computer ' s dedicated video card is completely non - functional , the computer routinely switches off in the middle of executing a process , and i can ' t even use the hdmi out port .\", \"labels\": \"[{'aspect': \"computer ' s dedicated video card\", 'opinion': 'non - functional', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hdmi out port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the machine looks amazing doesn ' t it !\n->the machine looks amazing doesn ' t it !\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n->lexicographers take note : a new and fascinating definition of rudeness is alive and flourishing right here in brooklyn .\n[{'aspect': 'NULL', 'opinion': 'rudeness', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: however with that being said i bought this laptop about 3 days ago and it ' s already not working .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever with that being said i bought this laptop about 3 days ago and it ' s already not working .\n->", + "output": "{\"text\": \"however with that being said i bought this laptop about 3 days ago and it ' s already not working .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i went online and found that the 1 year acer warranty had already expired .\n->i went online and found that the 1 year acer warranty had already expired .\n[{'aspect': 'acer warranty', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'WARRANTY#GENERAL'}]\nExample:\ntext: the metal case is really well built , and the fit and finish are virtually perfect .\n->the metal case is really well built , and the fit and finish are virtually perfect .\n[{'aspect': 'metal case', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'fit', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'finish', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: i called acer tech support but nothing worked .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni called acer tech support but nothing worked .\n->", + "output": "{\"text\": \"i called acer tech support but nothing worked .\", \"labels\": \"[{'aspect': 'acer tech support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It 's easy to get a table for a large group and you do n't get hustled out .\n->It 's easy to get a table for a large group and you do n't get hustled out .\n[{'aspect': 'table', 'opinion': 'easy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: manufacturing seems unreliable .\n->manufacturing seems unreliable .\n[{'aspect': 'manufacturing', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: will not recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwill not recommend it .\n->", + "output": "{\"text\": \"will not recommend it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n->It can not be the ambience , because the place is very cramped and some guests have to sit in an aisle .\n[{'aspect': 'place', 'opinion': 'cramped', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\n->Consequently , their burgers fell apart in their hands and made such a mess that they did'nt feel like finishing them .\n[{'aspect': 'burgers', 'opinion': 'fell apart', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it is an ok laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is an ok laptop .\n->", + "output": "{\"text\": \"it is an ok laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n->purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n[{'aspect': '8th gen i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: great vibe , lots of people .\n->great vibe , lots of people .\n[{'aspect': 'vibe', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the problem with it is that it freezes from time to time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe problem with it is that it freezes from time to time .\n->", + "output": "{\"text\": \"the problem with it is that it freezes from time to time .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this happened 7 times within 20 minutes when i was working on something .\n->this happened 7 times within 20 minutes when i was working on something .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: as far as gaming performance , the m370x does quite well .\n->as far as gaming performance , the m370x does quite well .\n[{'aspect': 'm370x', 'opinion': 'well', 'polarity': 'positive', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: this looks too big , it ' s just 1 day of use so far this is my review .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis looks too big , it ' s just 1 day of use so far this is my review .\n->", + "output": "{\"text\": \"this looks too big , it ' s just 1 day of use so far this is my review .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'big', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the coolest thing is the touch screen on something this size .\n->the coolest thing is the touch screen on something this size .\n[{'aspect': 'touch screen', 'opinion': 'coolest', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n->Probably much busier for lunch , it 's seldom crowded for dinner ( too close to downtown ) .\n[{'aspect': 'lunch', 'opinion': 'busier', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'seldom crowded', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: my computer was used on average a couple hours a day .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy computer was used on average a couple hours a day .\n->", + "output": "{\"text\": \"my computer was used on average a couple hours a day .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have been to Casimir over 5 times and I have always had a great time there .\n->I have been to Casimir over 5 times and I have always had a great time there .\n[{'aspect': 'Casimir', 'opinion': 'great time', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: We had the lobster sandwich and it was FANTASTIC .\n->We had the lobster sandwich and it was FANTASTIC .\n[{'aspect': 'lobster sandwich', 'opinion': 'FANTASTIC', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 11 months after the purchase , it died .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n11 months after the purchase , it died .\n->", + "output": "{\"text\": \"11 months after the purchase , it died .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n->they did give a 15 % discount at the end , was n't enough , as they knew the service was horrible .\n[{'aspect': 'discount', 'opinion': \"was n't enough\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the keyboard also feels nice and the backlighting is great .\n->the keyboard also feels nice and the backlighting is great .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlighting', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: it worked great , no issues besides the mouse began to freeze when the computer was idled for too long .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit worked great , no issues besides the mouse began to freeze when the computer was idled for too long .\n->", + "output": "{\"text\": \"it worked great , no issues besides the mouse began to freeze when the computer was idled for too long .\", \"labels\": \"[{'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speakers sound tinny .\n->speakers sound tinny .\n[{'aspect': 'speakers', 'opinion': 'tinny', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: The atmosphere was crowded but it was a great bistro-type vibe .\n->The atmosphere was crowded but it was a great bistro-type vibe .\n[{'aspect': 'bistro-type vibe', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n->", + "output": "{\"text\": \"first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'graphics', 'opinion': 'failed', 'polarity': 'negative', 'category': 'GRAPHICS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Should you happen to be impressed by the cuisine definitely try it .\n->Should you happen to be impressed by the cuisine definitely try it .\n[{'aspect': 'cuisine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n->Three courses - choices include excellent mussels , puff pastry goat cheese and salad with a delicious dressing , and a hanger steak au poivre that is out of this world .\n[{'aspect': 'mussels', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'puff pastry goat cheese', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salad with a delicious dressing', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hanger steak au poivre', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\ntext: nice computer that came with a bad fan .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice computer that came with a bad fan .\n->", + "output": "{\"text\": \"nice computer that came with a bad fan .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'fan', 'opinion': 'bad', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and it ' s light which is a big plus since i carry it to school .\n->and it ' s light which is a big plus since i carry it to school .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Super friendly and knowledgable staff , fabulous bistro fare and a wonderful jazz brunch with great live jazz ( the chilaquiles were awesome !\n->Super friendly and knowledgable staff , fabulous bistro fare and a wonderful jazz brunch with great live jazz ( the chilaquiles were awesome !\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bistro fare', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chilaquiles', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'jazz brunch', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'live jazz', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the problem is a complete lack of local hardware support in the us unless you happen to live in temple , tx .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe problem is a complete lack of local hardware support in the us unless you happen to live in temple , tx .\n->", + "output": "{\"text\": \"the problem is a complete lack of local hardware support in the us unless you happen to live in temple , tx .\", \"labels\": \"[{'aspect': 'hardware support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m not at all happy about that .\n->i ' m not at all happy about that .\n[{'aspect': 'NULL', 'opinion': 'not at all happy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the display is fantastic on this laptop .\n->the display is fantastic on this laptop .\n[{'aspect': 'display', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: i am giving this a one star because of its faulty design .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am giving this a one star because of its faulty design .\n->", + "output": "{\"text\": \"i am giving this a one star because of its faulty design .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the atmosphere was great .\n->the atmosphere was great .\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the food was good .\n->the food was good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the screen sits too close to the keyboard and you will end up with scratches on the screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen sits too close to the keyboard and you will end up with scratches on the screen .\n->", + "output": "{\"text\": \"the screen sits too close to the keyboard and you will end up with scratches on the screen .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hold on before you decide to pay $ 2500 for this laptop .\n->hold on before you decide to pay $ 2500 for this laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n->It 's constantly open , catering to the Pakistani cabbies lined up on Crosby St. , so there 's more turnover with the food than you 'd expect ( i.e. , surprisingly fresh ) .\n[{'aspect': 'food', 'opinion': 'surprisingly fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: laptop failed after only six months of use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop failed after only six months of use .\n->", + "output": "{\"text\": \"laptop failed after only six months of use .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this acer is a web surfer that ' s easy to travel with .\n->this acer is a web surfer that ' s easy to travel with .\n[{'aspect': 'acer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n->Downtown Dinner 2002 - Prixe fix : Appetizers were ok , waiter gave me poor suggestion ... try the potato stuff kanish best one .\n[{'aspect': 'Appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'potato stuff kanish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: worked fine until 3 months after i bought it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworked fine until 3 months after i bought it .\n->", + "output": "{\"text\": \"worked fine until 3 months after i bought it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: however , it charges insanely quickly : you can get a full charge in under an hour .\n->however , it charges insanely quickly : you can get a full charge in under an hour .\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: We got a little tipsy from the sake but is n't that what Saturday nights with the girlfriends are all about ?\n->We got a little tipsy from the sake but is n't that what Saturday nights with the girlfriends are all about ?\n[{'aspect': 'sake', 'opinion': 'tipsy', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: laptop was working fine until just under 3 months of use when it bsod ' d and wouldn ' t turn back on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop was working fine until just under 3 months of use when it bsod ' d and wouldn ' t turn back on .\n->", + "output": "{\"text\": \"laptop was working fine until just under 3 months of use when it bsod ' d and wouldn ' t turn back on .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I got an excellent piece of cheesecake and we had several other nice pastries .\n->I got an excellent piece of cheesecake and we had several other nice pastries .\n[{'aspect': 'cheesecake', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastries', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n->what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlooking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\n->", + "output": "{\"text\": \"looking past the bsod problem , the battery is internal so you can ' t just pop it off , you need access the mobo just to replace it .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great computer .\n->great computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n->i ' ve also discovered that the battery is better than i first thought , and will last 8 hours for light - duty activities such as web - surfing and word processing ; longer with power management on .\n[{'aspect': 'battery', 'opinion': 'better', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: this laptop ' s construction is cheap and flimsy , the battery is not removable and the back case is nearly impossible to take off without damaging it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop ' s construction is cheap and flimsy , the battery is not removable and the back case is nearly impossible to take off without damaging it .\n->", + "output": "{\"text\": \"this laptop ' s construction is cheap and flimsy , the battery is not removable and the back case is nearly impossible to take off without damaging it .\", \"labels\": \"[{'aspect': \"laptop ' s construction\", 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': \"laptop ' s construction\", 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'not removable', 'polarity': 'negative', 'category': 'BATTERY#DESIGN_FEATURES'}, {'aspect': 'back case', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: love pizza 33 . . .\n->love pizza 33 . . .\n[{'aspect': 'pizza 33', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: reliability is completely terrible - i use it for work but definitely wouldn ' t buy for personal use as it ' s had to be repaired and replaced twice already - once for just momentarily stopping and then for the touch screen not working with hand input ( worked for the pen which btw is kind of useless except in a few android apps as the handwriting recognition is limited to a lame popup panel - there ' s no integration ) .\n->reliability is completely terrible - i use it for work but definitely wouldn ' t buy for personal use as it ' s had to be repaired and replaced twice already - once for just momentarily stopping and then for the touch screen not working with hand input ( worked for the pen which btw is kind of useless except in a few android apps as the handwriting recognition is limited to a lame popup panel - there ' s no integration ) .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: it would not start up after 2 months of purchase and then re - set button didn ' t work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit would not start up after 2 months of purchase and then re - set button didn ' t work .\n->", + "output": "{\"text\": \"it would not start up after 2 months of purchase and then re - set button didn ' t work .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 're - set button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we asked for sides which the waiter than admitted that he forgot to put in that part of our order .\n->we asked for sides which the waiter than admitted that he forgot to put in that part of our order .\n[{'aspect': 'waiter', 'opinion': 'forgot', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it took hours to restore to factory default settings , and it crashed once again days later .\n->it took hours to restore to factory default settings , and it crashed once again days later .\n[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: its a good computer for the price but it needs to work awhile without crashing within 6 months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits a good computer for the price but it needs to work awhile without crashing within 6 months .\n->", + "output": "{\"text\": \"its a good computer for the price but it needs to work awhile without crashing within 6 months .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n->the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n[{'aspect': 'meat', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauces', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchi', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'salad', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'meal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchpad is quite sensitive i ' ve noticed , not a big issue to me .\n->", + "output": "{\"text\": \"the touchpad is quite sensitive i ' ve noticed , not a big issue to me .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'sensitive', 'polarity': 'neutral', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i especially loved the high ( er ) resolution display , compared to most other chromebooks .\n->i especially loved the high ( er ) resolution display , compared to most other chromebooks .\n[{'aspect': 'display', 'opinion': 'loved', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: the actual laptop is very much darker and blue .\n->the actual laptop is very much darker and blue .\n[{'aspect': 'actual laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i will keep my rating at 3 stars , as the issues with the screen quality / shine - and volume / brightness keys being unusable and nonexistent , to be large issues for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will keep my rating at 3 stars , as the issues with the screen quality / shine - and volume / brightness keys being unusable and nonexistent , to be large issues for me .\n->", + "output": "{\"text\": \"i will keep my rating at 3 stars , as the issues with the screen quality / shine - and volume / brightness keys being unusable and nonexistent , to be large issues for me .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'volume', 'opinion': 'unusable', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'volume', 'opinion': 'nonexistent', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n->overall , if you just need a computer to do basic software development , remote work , or browse the internet , it ' s perfectly fine .\n[{'aspect': 'computer', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n->The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the laptop build is cheap looking and basic , but functional to say the least while i save up money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop build is cheap looking and basic , but functional to say the least while i save up money .\n->", + "output": "{\"text\": \"the laptop build is cheap looking and basic , but functional to say the least while i save up money .\", \"labels\": \"[{'aspect': 'laptop build', 'opinion': 'cheap looking', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop build', 'opinion': 'basic', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop build', 'opinion': 'functional', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n->the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: The rest of the menu is limited by everything is good eats .\n->The rest of the menu is limited by everything is good eats .\n[{'aspect': 'eats', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i will say , however , that the screen is kind of awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will say , however , that the screen is kind of awful .\n->", + "output": "{\"text\": \"i will say , however , that the screen is kind of awful .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'awful', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their pad penang is delicious and everything else is fantastic .\n->Their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the menu has so many fish items and oysters .\n->the menu has so many fish items and oysters .\n[{'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: it looks awful , feels awful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit looks awful , feels awful .\n->", + "output": "{\"text\": \"it looks awful , feels awful .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'awful', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n->Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n[{'aspect': 'location', 'opinion': 'nice quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 10 months in my battery will no longer charge .\n->10 months in my battery will no longer charge .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: overall i have to remind myself that this laptop is only 600 dollars , and with certain gains i ' ll lose some things unless i ' m willing to spend out of budget .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall i have to remind myself that this laptop is only 600 dollars , and with certain gains i ' ll lose some things unless i ' m willing to spend out of budget .\n->", + "output": "{\"text\": \"overall i have to remind myself that this laptop is only 600 dollars , and with certain gains i ' ll lose some things unless i ' m willing to spend out of budget .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The prices were fantastic .\n->The prices were fantastic .\n[{'aspect': 'prices', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i have known about this secret for the last 13 years , emilio ( the godfather ) has continued to serve food and wine for the gods at mortal prices .\n->i have known about this secret for the last 13 years , emilio ( the godfather ) has continued to serve food and wine for the gods at mortal prices .\n[{'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'wine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\ntext: this item is two months old and the power button repeatedly does not work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis item is two months old and the power button repeatedly does not work .\n->", + "output": "{\"text\": \"this item is two months old and the power button repeatedly does not work .\", \"labels\": \"[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'Out_Of_Scope#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very nicely packed in original box .\n->very nicely packed in original box .\n[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n->as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: screen broke 2 weeks after having it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen broke 2 weeks after having it .\n->", + "output": "{\"text\": \"screen broke 2 weeks after having it .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: great little computer .\n->great little computer .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i had this laptop repaired within the first 6 months of owning it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni had this laptop repaired within the first 6 months of owning it .\n->", + "output": "{\"text\": \"i had this laptop repaired within the first 6 months of owning it .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n->while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n[{'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n->i ' ll being with a couple of positives : cool decor , good pita and hummus , and grilled octopus that was actually pretty tasty .\n[{'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'pita', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hummus', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'grilled octopus', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: works good but right click on mouse pad wont wok have to use external mouse\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworks good but right click on mouse pad wont wok have to use external mouse\n->", + "output": "{\"text\": \"works good but right click on mouse pad wont wok have to use external mouse\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but , now i realize the design is flawed .\n->but , now i realize the design is flawed .\n[{'aspect': 'design', 'opinion': 'flawed', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: zero ambiance to boot .\n->zero ambiance to boot .\n[{'aspect': 'ambiance', 'opinion': 'zero', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: when i play a game , there is noise on the screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i play a game , there is noise on the screen .\n->", + "output": "{\"text\": \"when i play a game , there is noise on the screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely a steal at the price i bought this for .\n->definitely a steal at the price i bought this for .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n->We made early dinner reservations and were thoroughly impressed , reminds me of my grandfather , its old school Italian scenery with lots of fun stuff to admire .\n[{'aspect': 'scenery', 'opinion': 'fun', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner reservations', 'opinion': 'early', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the laptop itself seemed fine at first .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop itself seemed fine at first .\n->", + "output": "{\"text\": \"the laptop itself seemed fine at first .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n->i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n[{'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the mbp trackpad is best in class and if you are not using a mouse , this makes a huge difference .\n->the mbp trackpad is best in class and if you are not using a mouse , this makes a huge difference .\n[{'aspect': 'mbp trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: but the backlit keyboard was no longer lighting - up and the computer would no longer turn - on or boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the backlit keyboard was no longer lighting - up and the computer would no longer turn - on or boot .\n->", + "output": "{\"text\": \"but the backlit keyboard was no longer lighting - up and the computer would no longer turn - on or boot .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is very disappointing an causing me big issues while i write .\n->this is very disappointing an causing me big issues while i write .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n->in addition , in my short time using the device , i realized how much i dislike having fingerprints on my computer screen .\n[{'aspect': 'fingerprints', 'opinion': 'dislike', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}]\ntext: i left it plugged in to charge for the night and tried it again the next morning , with the same results of not being able to turn it back on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni left it plugged in to charge for the night and tried it again the next morning , with the same results of not being able to turn it back on .\n->", + "output": "{\"text\": \"i left it plugged in to charge for the night and tried it again the next morning , with the same results of not being able to turn it back on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tapping it on either end is hit or miss .\n->tapping it on either end is hit or miss .\n[{'aspect': 'tapping', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: It 's great to go for a quick lunch either alone or with a friend .\n->It 's great to go for a quick lunch either alone or with a friend .\n[{'aspect': 'lunch', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}]\ntext: at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nat this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\n->", + "output": "{\"text\": \"at this point i no longer trusted this model and was afraid of having the same issue with a replacement if i exchanged it , and then not having enough time to do another exchange and still be able to charge and configure it in time to wrap and put under the christmas tree for my son , so i contacted amazon and they gladly e - mailed me a return shipping label and refunded my money in full in 3 days , after receiving it back from me .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'afraid', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'amazon', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: $ 20 for all you can eat sushi can not be beaten .\n->$ 20 for all you can eat sushi can not be beaten .\n[{'aspect': 'all you can eat sushi', 'opinion': 'beaten', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: The food is delicious .\n->The food is delicious .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i hate to knock acer as they ' re the only brand i ' ve ever purchased and this is the first major issue i ' ve ever had with them , but it was frustrating spending that much time charging and almost finishing the configurations and set - up of a brand - new product and then not being able to turn it on to use it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni hate to knock acer as they ' re the only brand i ' ve ever purchased and this is the first major issue i ' ve ever had with them , but it was frustrating spending that much time charging and almost finishing the configurations and set - up of a brand - new product and then not being able to turn it on to use it .\n->", + "output": "{\"text\": \"i hate to knock acer as they ' re the only brand i ' ve ever purchased and this is the first major issue i ' ve ever had with them , but it was frustrating spending that much time charging and almost finishing the configurations and set - up of a brand - new product and then not being able to turn it on to use it .\", \"labels\": \"[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}, {'aspect': 'product', 'opinion': 'frustrating', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n->The staff is very kind and well trained , they 're fast , they are always prompt to jump behind the bar and fix drinks , they know details of every item in the menu and make excellent recomendations .\n[{'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'well trained', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the click is now provided by a haptic engine and provides more functionality .\n->the click is now provided by a haptic engine and provides more functionality .\n[{'aspect': 'click', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: then i called amazon - acer again , the customer service was good !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen i called amazon - acer again , the customer service was good !\n->", + "output": "{\"text\": \"then i called amazon - acer again , the customer service was good !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'customer service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service varys from day to day- sometimes they 're very nice , and sometimes not .\n->The service varys from day to day- sometimes they 're very nice , and sometimes not .\n[{'aspect': 'service', 'opinion': 'varys', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the rest of the dim sum , though pricey by chinatown standards , is worth it .\n->the rest of the dim sum , though pricey by chinatown standards , is worth it .\n[{'aspect': 'dim sum', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'dim sum', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: overall this is a very capable machine , better life is great as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall this is a very capable machine , better life is great as well .\n->", + "output": "{\"text\": \"overall this is a very capable machine , better life is great as well .\", \"labels\": \"[{'aspect': 'machine ,', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'better life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The first 2 courses were very good , but the chocolate sampler was too rich for me and the dessert wine far too sweet .\n->The first 2 courses were very good , but the chocolate sampler was too rich for me and the dessert wine far too sweet .\n[{'aspect': 'courses', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chocolate sampler', 'opinion': 'too rich', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert wine', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: no gimmicks here - - the food speaks for itself in its freshness and preparation .\n->no gimmicks here - - the food speaks for itself in its freshness and preparation .\n[{'aspect': 'food', 'opinion': 'freshness', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'preparation', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: not sure if this is simply a deal accident , but my ssd failed within 4 months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot sure if this is simply a deal accident , but my ssd failed within 4 months .\n->", + "output": "{\"text\": \"not sure if this is simply a deal accident , but my ssd failed within 4 months .\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'failed', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will be going back and heartily recommend it !\n->i will be going back and heartily recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: adobe apps are also power hogs , but that is to be expected .\n->adobe apps are also power hogs , but that is to be expected .\n[{'aspect': 'adobe apps', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n->", + "output": "{\"text\": \"but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'erroneous', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'way too sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'cover / lid', 'opinion': 'cheaply', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it came in a regular brown box and the power cord was a bit scratched .\n->it came in a regular brown box and the power cord was a bit scratched .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\nExample:\ntext: it has the specs but that ' s it ' s main downfall .\n->it has the specs but that ' s it ' s main downfall .\n[{'aspect': 'specs', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i got the computer working fine i set everything up linked it to my phone ect , then i had steam messages open , discord open and watching youtube and the screen again froze completely no audio no nothing just a frozen screen displaying youtube .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni got the computer working fine i set everything up linked it to my phone ect , then i had steam messages open , discord open and watching youtube and the screen again froze completely no audio no nothing just a frozen screen displaying youtube .\n->", + "output": "{\"text\": \"i got the computer working fine i set everything up linked it to my phone ect , then i had steam messages open , discord open and watching youtube and the screen again froze completely no audio no nothing just a frozen screen displaying youtube .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + play store compatibility is available now .\n->+ play store compatibility is available now .\n[{'aspect': 'play store', 'opinion': 'available', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n->The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it had hard failure after a month and i had to send it our for a service .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit had hard failure after a month and i had to send it our for a service .\n->", + "output": "{\"text\": \"it had hard failure after a month and i had to send it our for a service .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'failure', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not only is the touchpad not great in use but it also feels poorly made .\n->not only is the touchpad not great in use but it also feels poorly made .\n[{'aspect': 'touchpad', 'opinion': 'not great', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: Good drink .\n->Good drink .\n[{'aspect': 'drink', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: power turning on failed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npower turning on failed .\n->", + "output": "{\"text\": \"power turning on failed .\", \"labels\": \"[{'aspect': 'power', 'opinion': 'failed', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen maximum brightness is still not bright enough\n->screen maximum brightness is still not bright enough\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the keys are easy to type on and the laptop itself is thin yet feels solid and well constructed .\n->the keys are easy to type on and the laptop itself is thin yet feels solid and well constructed .\n[{'aspect': 'keys', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'laptop', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'well constructed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: computer wo n ' t turn on have had it less then a year .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomputer wo n ' t turn on have had it less then a year .\n->", + "output": "{\"text\": \"computer wo n ' t turn on have had it less then a year .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n->the computer constantly pauses or stutters and then continues when it comes to loading web pages and working on documents .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: they were dry and disgusting , i did n ' t even finish my first piece .\n->they were dry and disgusting , i did n ' t even finish my first piece .\n[{'aspect': 'NULL', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the laptop died after just one month .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe laptop died after just one month .\n->", + "output": "{\"text\": \"the laptop died after just one month .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the display is amazing and the new force click trackpad is an awesome addition .\n->the display is amazing and the new force click trackpad is an awesome addition .\n[{'aspect': 'display', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'force click trackpad', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: Despite the fact that the space is large , they 've overcrowded the floor with tables .\n->Despite the fact that the space is large , they 've overcrowded the floor with tables .\n[{'aspect': 'space', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'overcrowded', 'polarity': 'negative', 'category': 'NULL'}]\ntext: poor screen quality , extremely dim , lacks clarity , slow photo uploads .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npoor screen quality , extremely dim , lacks clarity , slow photo uploads .\n->", + "output": "{\"text\": \"poor screen quality , extremely dim , lacks clarity , slow photo uploads .\", \"labels\": \"[{'aspect': 'screen quality', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all in all , the food was great ( except for the dessserts ) .\n->all in all , the food was great ( except for the dessserts ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessserts', 'opinion': 'except', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i gave it 3 out of 5 stars , because there is no sd card slot !\n->i gave it 3 out of 5 stars , because there is no sd card slot !\n[{'aspect': 'sd card slot', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\ntext: received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreceived it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\n->", + "output": "{\"text\": \"received it , launched up a game , cpu reaches 99 degrees celsius , game crashes .\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was also very good .\n->Service was also very good .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n->this is actually the first laptop that fitted all my needs , big clear screen , battery life sucks but this is a power house laptop meant to be in one place since its so big , very good computer replacement , amazing sound and screen and very fast .\n[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'computer replacement', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: twice in 10 days i had an issue , pointer , where they said turn it over and put a pin in it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntwice in 10 days i had an issue , pointer , where they said turn it over and put a pin in it .\n->", + "output": "{\"text\": \"twice in 10 days i had an issue , pointer , where they said turn it over and put a pin in it .\", \"labels\": \"[{'aspect': 'pointer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'Out_Of_Scope#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only problem that i have found about lenovo is that it comes with a program called migration , which is supposed to migrate your things from your old computer to your new computer .\n->the only problem that i have found about lenovo is that it comes with a program called migration , which is supposed to migrate your things from your old computer to your new computer .\n[{'aspect': 'migration', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\nExample:\ntext: thanks bloom ' s for a lovely trip .\n->thanks bloom ' s for a lovely trip .\n[{'aspect': \"bloom ' s\", 'opinion': 'lovely', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\n->", + "output": "{\"text\": \"it ' s far too early in this machine ' s life to be encountering problems , the company has very poor customer service , and i would strongly recommend against purchasing .\", \"labels\": \"[{'aspect': 'company', 'opinion': 'poor', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now audio in and out are combined in just one port .\n->now audio in and out are combined in just one port .\n[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#DESIGN_FEATURES'}]\nExample:\ntext: however , it charges insanely quickly : you can get a full charge in under an hour .\n->however , it charges insanely quickly : you can get a full charge in under an hour .\n[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\n->", + "output": "{\"text\": \"it works well , but the only thing i don ' t like about it is that it makes it extremely difficult to find things that i ' m looking for .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we started off with a delightful sashimi amuse bouche .\n->we started off with a delightful sashimi amuse bouche .\n[{'aspect': 'sashimi amuse bouche', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s lightweight and fits easily in a tote or backpack .\n->it ' s lightweight and fits easily in a tote or backpack .\n[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the computer hold its ground , but it has specs to be a killer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer hold its ground , but it has specs to be a killer .\n->", + "output": "{\"text\": \"the computer hold its ground , but it has specs to be a killer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: always a nice crowd , but never loud .\n->always a nice crowd , but never loud .\n[{'aspect': 'crowd', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'crowd', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'never loud', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n->My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n[{'aspect': 'french fries', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: barely have it for 6 months and everything ' s going haywire .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbarely have it for 6 months and everything ' s going haywire .\n->", + "output": "{\"text\": \"barely have it for 6 months and everything ' s going haywire .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'haywire', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n->In summer-eat outside on a terrace ( another great feature of Suan ) ! ! !\n[{'aspect': 'terrace', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: keyboard does seem a little off size as i seem to often be one key offset when get in typing position .\n->keyboard does seem a little off size as i seem to often be one key offset when get in typing position .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: now i ca n ' t even get into my laptop because the startup is all jacked up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnow i ca n ' t even get into my laptop because the startup is all jacked up .\n->", + "output": "{\"text\": \"now i ca n ' t even get into my laptop because the startup is all jacked up .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For appetizers , I recommend the shrimp fritters and dumplings .\n->For appetizers , I recommend the shrimp fritters and dumplings .\n[{'aspect': 'shrimp fritters', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dumplings', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n->most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: however , the 1 - star review is due to the advertising with the computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , the 1 - star review is due to the advertising with the computer .\n->", + "output": "{\"text\": \"however , the 1 - star review is due to the advertising with the computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n->the one i purchased wasn ' t the cheapest but for peace of mind i gladly paid it .\n[{'aspect': 'NULL', 'opinion': 'gladly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: very good purchase .\n->very good purchase .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: battery is not as long\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery is not as long\n->", + "output": "{\"text\": \"battery is not as long\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n->The fillings may be unconventional but the dosa batter is definitely authentic and the combinations very tasty .\n[{'aspect': 'fillings', 'opinion': 'unconventional', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dosa batter', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: fast shipping too !\n->fast shipping too !\n[{'aspect': 'shipping', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\ntext: buyer beware - computer is complete trash .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuyer beware - computer is complete trash .\n->", + "output": "{\"text\": \"buyer beware - computer is complete trash .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'trash', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service is friendly , and never had a problem walking in and getting a table .\n->service is friendly , and never had a problem walking in and getting a table .\n[{'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it take a bit of getting used to .\n->it take a bit of getting used to .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}]\ntext: it just freezes up on you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit just freezes up on you .\n->", + "output": "{\"text\": \"it just freezes up on you .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: definitely recommend this chromebook , it ' s a beautiful machine .\n->definitely recommend this chromebook , it ' s a beautiful machine .\n[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: looks great .\n->looks great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: super off balance with respect to screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuper off balance with respect to screen .\n->", + "output": "{\"text\": \"super off balance with respect to screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'off balance', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m giving this five stars considering the price .\n->i ' m giving this five stars considering the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the keyboard is a nice size and the pads clicks on touch and not stiff or hard .\n->the keyboard is a nice size and the pads clicks on touch and not stiff or hard .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: keyboard lighting is primitive and keeps shutting off .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard lighting is primitive and keeps shutting off .\n->", + "output": "{\"text\": \"keyboard lighting is primitive and keeps shutting off .\", \"labels\": \"[{'aspect': 'keyboard lighting', 'opinion': 'primitive', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n->microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is a good product based on my experience - i have used this for almost a whole month .\n->this is a good product based on my experience - i have used this for almost a whole month .\n[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the battery life is around 2 hrs , not 10 + as advertised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is around 2 hrs , not 10 + as advertised .\n->", + "output": "{\"text\": \"the battery life is around 2 hrs , not 10 + as advertised .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m pretty sure i ordered the biggest size , and i got the smaller one , but whatever it shipped fast and it works great .\n->i ' m pretty sure i ordered the biggest size , and i got the smaller one , but whatever it shipped fast and it works great .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n->Shockingly easy to throw a group dinner here : simple contract , deposit only to hold the date the entire 2nd fl mezz for our grp of 20 .\n[{'aspect': 'group dinner', 'opinion': 'easy', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had good luck with acer products but this pc was my 1st time i needed them for technical support .\n->", + "output": "{\"text\": \"i have had good luck with acer products but this pc was my 1st time i needed them for technical support .\", \"labels\": \"[{'aspect': 'pc', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer makes a good unit too .\n->acer makes a good unit too .\n[{'aspect': 'acer', 'opinion': 'good', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: google is very concerned about arc to chromeos connections for security , etc etc .\n->google is very concerned about arc to chromeos connections for security , etc etc .\n[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\ntext: i call acer support and after an hour they can not help me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni call acer support and after an hour they can not help me .\n->", + "output": "{\"text\": \"i call acer support and after an hour they can not help me .\", \"labels\": \"[{'aspect': 'acer support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very affordable and excellent ambient !\n->very affordable and excellent ambient !\n[{'aspect': 'ambient', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'affordable', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: service is not exactly five star , but thats not really a big deal .\n->service is not exactly five star , but thats not really a big deal .\n[{'aspect': 'service', 'opinion': 'not exactly five star', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\ntext: bought an acer computer that did not work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought an acer computer that did not work .\n->", + "output": "{\"text\": \"bought an acer computer that did not work .\", \"labels\": \"[{'aspect': 'acer computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the only thing is that it does ' t have too much storage room .\n->the only thing is that it does ' t have too much storage room .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: the touch screen never seemed to work properly and now i understand why .\n->the touch screen never seemed to work properly and now i understand why .\n[{'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\n->", + "output": "{\"text\": \"i ' ve called apple support numerous times and was never asked if i had the ability to read words as i ' m actually reading words .\", \"labels\": \"[{'aspect': 'apple support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and it was a very good price .\n->and it was a very good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: i would buy this for myself as well .\n->i would buy this for myself as well .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: thank you to amazon for taking this brick of acer garbage back .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthank you to amazon for taking this brick of acer garbage back .\n->", + "output": "{\"text\": \"thank you to amazon for taking this brick of acer garbage back .\", \"labels\": \"[{'aspect': 'amazon', 'opinion': 'thank you', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'acer garbage', 'opinion': 'garbage', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the trackpad was very glitchy .\n->the trackpad was very glitchy .\n[{'aspect': 'trackpad', 'opinion': 'glitchy', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: next , is that the track pad is insanely wobbly .\n->next , is that the track pad is insanely wobbly .\n[{'aspect': 'track pad', 'opinion': 'wobbly', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: could have been a great computer if not for the terrible keyboard construction .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncould have been a great computer if not for the terrible keyboard construction .\n->", + "output": "{\"text\": \"could have been a great computer if not for the terrible keyboard construction .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard construction', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: tiny dessert was $ 8 . 00 . . . just plain overpriced for what it is .\n->tiny dessert was $ 8 . 00 . . . just plain overpriced for what it is .\n[{'aspect': 'dessert', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'dessert', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'dessert', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: - the computer is 1 pound ( approx .\n->- the computer is 1 pound ( approx .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: constantly got the blue screen , already tryed everything to fix it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nconstantly got the blue screen , already tryed everything to fix it .\n->", + "output": "{\"text\": \"constantly got the blue screen , already tryed everything to fix it .\", \"labels\": \"[{'aspect': 'blue screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this for my daughter for school and she loves it .\n->i bought this for my daughter for school and she loves it .\n[{'aspect': 'NULL', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: I have had so many dinners here and it 's always been perfect - on a date with my husband , with my mom , with girlfriends and larger groups .\n->I have had so many dinners here and it 's always been perfect - on a date with my husband , with my mom , with girlfriends and larger groups .\n[{'aspect': 'dinners', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the seller doesn ' t reply my e - mails\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe seller doesn ' t reply my e - mails\n->", + "output": "{\"text\": \"the seller doesn ' t reply my e - mails\", \"labels\": \"[{'aspect': 'seller', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the build quality is great but not necessarily impressive .\n->- the build quality is great but not necessarily impressive .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'build quality', 'opinion': 'not necessarily impressive', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the 302 would be just about the perfect chromebook if it had smaller bezels , though .\n->the 302 would be just about the perfect chromebook if it had smaller bezels , though .\n[{'aspect': '302', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i ' ve had this for 3 days and so far the laptop is fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had this for 3 days and so far the laptop is fine .\n->", + "output": "{\"text\": \"i ' ve had this for 3 days and so far the laptop is fine .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you ' ll be there for every anniversary , birthday , valentines day . . .\n->you ' ll be there for every anniversary , birthday , valentines day . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: 2 ssd as it will not fit the slot available .\n->2 ssd as it will not fit the slot available .\n[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\ntext: i ' m seriously considering returning it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m seriously considering returning it !\n->", + "output": "{\"text\": \"i ' m seriously considering returning it !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: vanison was good but not amazing .\n->vanison was good but not amazing .\n[{'aspect': 'vanison', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'vanison', 'opinion': 'not amazing', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: looks wise it ' s beautiful , i love the minimal design and layout .\n->looks wise it ' s beautiful , i love the minimal design and layout .\n[{'aspect': 'NULL', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'layout', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i don ' t want bluetooth speakers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t want bluetooth speakers .\n->", + "output": "{\"text\": \"i don ' t want bluetooth speakers .\", \"labels\": \"[{'aspect': 'bluetooth speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: buy it for school , buy it for home , buy on for the grandkids .\n->buy it for school , buy it for home , buy on for the grandkids .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Spice is great Thai food , love the inexpensive appetizers .\n->Spice is great Thai food , love the inexpensive appetizers .\n[{'aspect': 'Thai food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizers', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizers', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i would not buy this again !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would not buy this again !\n->", + "output": "{\"text\": \"i would not buy this again !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i expected quite a bit more from such an expensive menu .\n->i expected quite a bit more from such an expensive menu .\n[{'aspect': 'menu', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'menu', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: when in use , the lower screen is flickering .\n->when in use , the lower screen is flickering .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: good touchpad\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood touchpad\n->", + "output": "{\"text\": \"good touchpad\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'good', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was efficient courteous .\n->service was efficient courteous .\n[{'aspect': 'service', 'opinion': 'efficient courteous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: when family came in he gave them apps to test their palets , and then ordered for them .\n->when family came in he gave them apps to test their palets , and then ordered for them .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: below medium build quality\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbelow medium build quality\n->", + "output": "{\"text\": \"below medium build quality\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'below medium', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: technical support was easy to reach , but not able to stop the problem i was having .\n->technical support was easy to reach , but not able to stop the problem i was having .\n[{'aspect': 'technical support', 'opinion': 'easy', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: oh and this is a beautiful machine and the lid is amazing .\n->oh and this is a beautiful machine and the lid is amazing .\n[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'lid', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: usb ports are too hard to plug and unplug\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nusb ports are too hard to plug and unplug\n->", + "output": "{\"text\": \"usb ports are too hard to plug and unplug\", \"labels\": \"[{'aspect': 'usb ports', 'opinion': 'hard', 'polarity': 'negative', 'category': 'PORTS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n->this laptop has been my traveling partner as i ' ve been traveling for 4 months , and i ' ve been extremely happy with the battery life , performance , and form of this machine .\n[{'aspect': 'performance', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'happy', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: i ' m very very happy with this fast lightweight convertible chromebook and my search has concluded .\n->i ' m very very happy with this fast lightweight convertible chromebook and my search has concluded .\n[{'aspect': 'chromebook', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: comes with hdd and this makes this laptop very slow\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncomes with hdd and this makes this laptop very slow\n->", + "output": "{\"text\": \"comes with hdd and this makes this laptop very slow\", \"labels\": \"[{'aspect': 'hdd', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not super confident this asus unit will last half that long .\n->not super confident this asus unit will last half that long .\n[{'aspect': 'asus unit', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n->First of all , this place is *not* romantic , as claimed by Citysearch 's editorial review .\n[{'aspect': 'place', 'opinion': '*not* romantic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: about 4 hours of battery\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nabout 4 hours of battery\n->", + "output": "{\"text\": \"about 4 hours of battery\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would recommend reservations on weekends though .\n->i would recommend reservations on weekends though .\n[{'aspect': 'reservations', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the keyboard is clicky , has decent amount of travel , and is backlit .\n->the keyboard is clicky , has decent amount of travel , and is backlit .\n[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: maintenance and opening case is too hard\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmaintenance and opening case is too hard\n->", + "output": "{\"text\": \"maintenance and opening case is too hard\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'hard', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you can not go wrong with this place .\n->you can not go wrong with this place .\n[{'aspect': 'place', 'opinion': 'wrong', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: While the ambiance and atmosphere were great , the food and service could have been a lot better .\n->While the ambiance and atmosphere were great , the food and service could have been a lot better .\n[{'aspect': 'ambiance', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'could have been a lot better', 'polarity': 'negative', 'category': 'NULL'}]\ntext: in contrast of that , this laptop ' s cpu is very powerful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin contrast of that , this laptop ' s cpu is very powerful .\n->", + "output": "{\"text\": \"in contrast of that , this laptop ' s cpu is very powerful .\", \"labels\": \"[{'aspect': \"laptop ' s cpu\", 'opinion': 'powerful', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: highly recommend , great value , use computer mainly for online business and is quick and easy to use\n->highly recommend , great value , use computer mainly for online business and is quick and easy to use\n[{'aspect': 'computer', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: on the other hand , it is not an easy task to open and replace hdd .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non the other hand , it is not an easy task to open and replace hdd .\n->", + "output": "{\"text\": \"on the other hand , it is not an easy task to open and replace hdd .\", \"labels\": \"[{'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the charging issue i can live with as well , even though it is annoying .\n->the charging issue i can live with as well , even though it is annoying .\n[{'aspect': 'charging', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: first time chrome os user , very streamlined and easy to use .\n->first time chrome os user , very streamlined and easy to use .\n[{'aspect': 'chrome os', 'opinion': 'streamlined', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'chrome os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: i don ' t understand this kind of design .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t understand this kind of design .\n->", + "output": "{\"text\": \"i don ' t understand this kind of design .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after really enjoying ourselves at the bar we sat down at a table and had dinner .\n->after really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: fast shipping .\n->fast shipping .\n[{'aspect': 'shipping', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SHIPPING#OPERATION_PERFORMANCE'}]\ntext: i can enjoy the real potential of this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can enjoy the real potential of this laptop .\n->", + "output": "{\"text\": \"i can enjoy the real potential of this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i haven ' t had issues with the track pad as others have .\n->i haven ' t had issues with the track pad as others have .\n[{'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'HARDWARE#USABILITY'}]\nExample:\ntext: most importantly , food is excellent .\n->most importantly , food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: 1 ) the delete button is right next to the power button\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n1 ) the delete button is right next to the power button\n->", + "output": "{\"text\": \"1 ) the delete button is right next to the power button\", \"labels\": \"[{'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: works well .\n->works well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is inventive but still keeps traditional indian flavoring .\n->The food is inventive but still keeps traditional indian flavoring .\n[{'aspect': 'food', 'opinion': 'inventive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'traditional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 2 ) slow start up and performance given\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n2 ) slow start up and performance given\n->", + "output": "{\"text\": \"2 ) slow start up and performance given\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service was excellent and the food was delicious .\n->the service was excellent and the food was delicious .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food is usually good but it certainly is n't a relaxing place to go .\n->The food is usually good but it certainly is n't a relaxing place to go .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': \"is n't a relaxing\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: it ' s fast at processing , fast for web browsing and has a quick startup .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s fast at processing , fast for web browsing and has a quick startup .\n->", + "output": "{\"text\": \"it ' s fast at processing , fast for web browsing and has a quick startup .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re going to be doing a lot of heavy lifting , this might not be the chromebook for you .\n->if you ' re going to be doing a lot of heavy lifting , this might not be the chromebook for you .\n[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: none of their android versions were what i would call usable .\n->none of their android versions were what i would call usable .\n[{'aspect': 'android versions', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: in seconds , even with an hdd it ' s still starts up in seconds .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin seconds , even with an hdd it ' s still starts up in seconds .\n->", + "output": "{\"text\": \"in seconds , even with an hdd it ' s still starts up in seconds .\", \"labels\": \"[{'aspect': 'starts up', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'hdd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n->we never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'tired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the color of everything is so very much brighter and clearer it makes the extra cost is worth more for just that .\n->the color of everything is so very much brighter and clearer it makes the extra cost is worth more for just that .\n[{'aspect': 'color', 'opinion': 'brighter', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'color', 'opinion': 'clearer', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'color', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the fingerprint sensor is a nice touch and the color and feel of the laptop material is also nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fingerprint sensor is a nice touch and the color and feel of the laptop material is also nice .\n->", + "output": "{\"text\": \"the fingerprint sensor is a nice touch and the color and feel of the laptop material is also nice .\", \"labels\": \"[{'aspect': 'fingerprint sensor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'laptop material', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was very good , a great deal , and the place its self was great .\n->The food was very good , a great deal , and the place its self was great .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great , original taste .\n->great , original taste .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'original', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: there is really no excuse why it can ' t have one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is really no excuse why it can ' t have one .\n->", + "output": "{\"text\": \"there is really no excuse why it can ' t have one .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was good not great not worth the wait or another visit\n->Food was good not great not worth the wait or another visit\n[{'aspect': 'Food', 'opinion': 'good not great not worth the wait or another visit', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n->do not try unless you ' re just going there to hang out like the rest of the hipsters who apparently have no sense of taste .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: it has the specs but that ' s it ' s main downfall .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has the specs but that ' s it ' s main downfall .\n->", + "output": "{\"text\": \"it has the specs but that ' s it ' s main downfall .\", \"labels\": \"[{'aspect': 'specs', 'opinion': 'downfall', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n->i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n->The people with carts of food do n't understand you because they do n't speak English , their job is to give you the delicious food you point at .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 15 ` ` screen , full sized keyboard and speed fast enough for some low quality games such as lol .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n15 ` ` screen , full sized keyboard and speed fast enough for some low quality games such as lol .\n->", + "output": "{\"text\": \"15 ` ` screen , full sized keyboard and speed fast enough for some low quality games such as lol .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'speed fast', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the nakgi - bokum was horrible .\n->the nakgi - bokum was horrible .\n[{'aspect': 'nakgi - bokum', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n->I 'd highly recommend it for a special occasion -- it provides and intimate setting and nice service .\n[{'aspect': 'setting', 'opinion': 'intimate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s light and thin , and overall the screen is pretty good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s light and thin , and overall the screen is pretty good .\n->", + "output": "{\"text\": \"it ' s light and thin , and overall the screen is pretty good .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'back room', 'opinion': 'secret', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n->I would highly recommend this place to anyone who is looking for a fine Indian dining experience that is definitely a value for your dollar .\n[{'aspect': 'Indian dining experience', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n->", + "output": "{\"text\": \"the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not usable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza here is consistently good .\n->Pizza here is consistently good .\n[{'aspect': 'Pizza', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n->the crust has a great bite and a good chew , the sauce is light with a nice acidity to it , the salt from the cheese is great , really heightens the flavor of all the other components .\n[{'aspect': 'crust', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crust', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'light', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauce', 'opinion': 'nice', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cheese', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: after the ssd upgrade , the computer is very fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter the ssd upgrade , the computer is very fast .\n->", + "output": "{\"text\": \"after the ssd upgrade , the computer is very fast .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I really liked this place .\n->I really liked this place .\n[{'aspect': 'place', 'opinion': 'liked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n->Ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the build quality is cheap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is cheap .\n->", + "output": "{\"text\": \"the build quality is cheap .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n->it does not interface well with any printer i could find as nothing would download for a driver because it needs to be a mac or windows .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n->food was very good as well , considering that we tried the budget selection ( though i wish the pork belly that i ordered was roasted a bit longer , so that fat was more of a melt - in - your - mouth experience ) .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork belly', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: everything is plastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything is plastic .\n->", + "output": "{\"text\": \"everything is plastic .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'plastic', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n->While most people can attest to spending over $ 50 on drinks in New York bars and hardly feeling a thing , the drinks here are plentiful and unique .\n[{'aspect': 'drinks', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I have eaten at Saul , many times , the food is always consistently , outrageously good .\n->I have eaten at Saul , many times , the food is always consistently , outrageously good .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the hard drive sounds like a distant lawn mower .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hard drive sounds like a distant lawn mower .\n->", + "output": "{\"text\": \"the hard drive sounds like a distant lawn mower .\", \"labels\": \"[{'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is expensive , but it is absolutely , positively worth it .\n->it is expensive , but it is absolutely , positively worth it .\n[{'aspect': 'NULL', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n->It is nearly impossible to get a table , so if you ever have the chance to go here for dinner , DO NOT pass it up .\n[{'aspect': 'table', 'opinion': 'impossible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this thing is slow !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis thing is slow !\n->", + "output": "{\"text\": \"this thing is slow !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: loved it !\n->loved it !\n[{'aspect': 'NULL', 'opinion': 'loved', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n->all in all , as long as you update windows often and tinker with it a little , you can get that laptop to do practically anything you want it to do .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' m trying to do online college college courses with it and it gets hung up in some intimate nevernever land of unresponsiveness .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m trying to do online college college courses with it and it gets hung up in some intimate nevernever land of unresponsiveness .\n->", + "output": "{\"text\": \"i ' m trying to do online college college courses with it and it gets hung up in some intimate nevernever land of unresponsiveness .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'unresponsiveness', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was a outstanding upgrade from the early 2013 650m model .\n->this was a outstanding upgrade from the early 2013 650m model .\n[{'aspect': 'NULL', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: this has got to be one of the most overrated restaurants in brooklyn .\n->this has got to be one of the most overrated restaurants in brooklyn .\n[{'aspect': 'NULL', 'opinion': 'overrated', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: - the audio for this laptop was poorly planned out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the audio for this laptop was poorly planned out .\n->", + "output": "{\"text\": \"- the audio for this laptop was poorly planned out .\", \"labels\": \"[{'aspect': 'audio for this laptop', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we ' d go back again\n->we ' d go back again\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the display is clear .\n->the display is clear .\n[{'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: so the audio can easily be muffled .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso the audio can easily be muffled .\n->", + "output": "{\"text\": \"so the audio can easily be muffled .\", \"labels\": \"[{'aspect': 'audio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wound up returning it .\n->i wound up returning it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food was average to above - average ; the french onion soup filling yet not overly impressive , and the desserts not brilliant in any way .\n->the food was average to above - average ; the french onion soup filling yet not overly impressive , and the desserts not brilliant in any way .\n[{'aspect': 'food', 'opinion': 'average to above - average', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'french onion soup', 'opinion': 'not overly impressive', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'desserts', 'opinion': 'not brilliant', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: - even so those are forgivable offenses compared to the next and worst thing , the touchpad .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- even so those are forgivable offenses compared to the next and worst thing , the touchpad .\n->", + "output": "{\"text\": \"- even so those are forgivable offenses compared to the next and worst thing , the touchpad .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'worst', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: decent sized track pad .\n->decent sized track pad .\n[{'aspect': 'track pad', 'opinion': 'decent', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n->the wine list was extensive - though the staff did not seem knowledgeable about wine pairings .\n[{'aspect': 'wine list', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'staff', 'opinion': 'not seem knowledgeable', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: this laptop ' s tocuhpad is by far the worst i have ever used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop ' s tocuhpad is by far the worst i have ever used .\n->", + "output": "{\"text\": \"this laptop ' s tocuhpad is by far the worst i have ever used .\", \"labels\": \"[{'aspect': \"laptop ' s tocuhpad\", 'opinion': 'worst', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagel was huge .\n->The bagel was huge .\n[{'aspect': 'bagel', 'opinion': 'huge', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The food is outstanding and the service is quick , friendly and very professional .\n->The food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i bought 2 , one stopped working after 9 months , was sent for ` ` repair ` ` it wasn ' t came right back with the same issue .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought 2 , one stopped working after 9 months , was sent for ` ` repair ` ` it wasn ' t came right back with the same issue .\n->", + "output": "{\"text\": \"i bought 2 , one stopped working after 9 months , was sent for ` ` repair ` ` it wasn ' t came right back with the same issue .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this battery lasts for 5 + hours on some of the most taxing apps my phone can run .\n->this battery lasts for 5 + hours on some of the most taxing apps my phone can run .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the problem with it is that it freezes from time to time .\n->the problem with it is that it freezes from time to time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the other one had the same issue after 9 months of use , wasn ' t used right away when purchased .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe other one had the same issue after 9 months of use , wasn ' t used right away when purchased .\n->", + "output": "{\"text\": \"the other one had the same issue after 9 months of use , wasn ' t used right away when purchased .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: long story short , i loved asus and have been buying them for years .\n->long story short , i loved asus and have been buying them for years .\n[{'aspect': 'asus', 'opinion': 'loved', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: not only is the food\n->not only is the food\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the customer service is the worst i have ever experienced , asking the same questions over and over , not helpful at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe customer service is the worst i have ever experienced , asking the same questions over and over , not helpful at all .\n->", + "output": "{\"text\": \"the customer service is the worst i have ever experienced , asking the same questions over and over , not helpful at all .\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'worst', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'customer service', 'opinion': 'not helpful', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there are no negatives to speak of .\n->there are no negatives to speak of .\n[{'aspect': 'NULL', 'opinion': 'no negatives', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n->we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: do not buy !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not buy !\n->", + "output": "{\"text\": \"do not buy !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i run around with it a lot , it doesn ' t just sit on a desk , and it ' s portability , ease of use , and flexibility in how i use it has been wonderful .\n->i run around with it a lot , it doesn ' t just sit on a desk , and it ' s portability , ease of use , and flexibility in how i use it has been wonderful .\n[{'aspect': 'NULL', 'opinion': 'ease', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: one year later and the laptop is still in great condition !\n->one year later and the laptop is still in great condition !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: today tried to turned it on , but to a blank screen !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntoday tried to turned it on , but to a blank screen !\n->", + "output": "{\"text\": \"today tried to turned it on , but to a blank screen !\", \"labels\": \"[{'aspect': 'blank screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: super fast boot .\n->super fast boot .\n[{'aspect': 'boot', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\nExample:\ntext: after logging in to the replacement , the screen flashes black every fives seconds and restart the chrome browser .\n->after logging in to the replacement , the screen flashes black every fives seconds and restart the chrome browser .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'chrome browser', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: this is a painfully slow computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a painfully slow computer .\n->", + "output": "{\"text\": \"this is a painfully slow computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'painfully', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is very disappointing an causing me big issues while i write .\n->this is very disappointing an causing me big issues while i write .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: superb value for money and powerful performance from this quad core computer .\n->superb value for money and powerful performance from this quad core computer .\n[{'aspect': 'quad core computer', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'quad core computer', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: - acceptable amount of flex\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- acceptable amount of flex\n->", + "output": "{\"text\": \"- acceptable amount of flex\", \"labels\": \"[{'aspect': 'flex', 'opinion': 'acceptable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: long battery life .\n->long battery life .\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the touch screen is nice , and i like to use it for free handing things when i need to .\n->the touch screen is nice , and i like to use it for free handing things when i need to .\n[{'aspect': 'touch screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: - fingerprint reader is working well\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- fingerprint reader is working well\n->", + "output": "{\"text\": \"- fingerprint reader is working well\", \"labels\": \"[{'aspect': 'fingerprint reader', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: more importantly i appreciate the uncanny speed to boot up or wake up .\n->more importantly i appreciate the uncanny speed to boot up or wake up .\n[{'aspect': 'speed', 'opinion': 'appreciate', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: we are loving this chromebook !\n->we are loving this chromebook !\n[{'aspect': 'chromebook', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: - track pad is accurate\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- track pad is accurate\n->", + "output": "{\"text\": \"- track pad is accurate\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Eating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\n->Eating in , the atmosphere saves it , but at your desk , it 's a very disappointing experience .\n[{'aspect': 'atmosphere', 'opinion': 'saves', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n->Although the tables may be closely situated , the candle-light , food quality and service overcompensate .\n[{'aspect': 'candle-light', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'overcompensate', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'closely situated', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - screen looks good\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- screen looks good\n->", + "output": "{\"text\": \"- screen looks good\", \"labels\": \"[{'aspect': 'screen looks', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is quite disappointing .\n->it is quite disappointing .\n[{'aspect': 'NULL', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n->sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n[{'aspect': 'server', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: - took literally 5 + hours for windows update / setup\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- took literally 5 + hours for windows update / setup\n->", + "output": "{\"text\": \"- took literally 5 + hours for windows update / setup\", \"labels\": \"[{'aspect': 'windows', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: every time in new york i make it a point to visit restaurant saul on smith street .\n->every time in new york i make it a point to visit restaurant saul on smith street .\n[{'aspect': 'restaurant saul', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: we did n ' t want a bottle of bubbly on a weekday so we each got little bottles of korbett it was just enough .\n->we did n ' t want a bottle of bubbly on a weekday so we each got little bottles of korbett it was just enough .\n[{'aspect': 'bottles of korbett', 'opinion': 'enough', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: - the webcam is a bad joke\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the webcam is a bad joke\n->", + "output": "{\"text\": \"- the webcam is a bad joke\", \"labels\": \"[{'aspect': 'webcam', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n->he served me an uni hand roll , which i never had before , and let me tell you . . . it was heaven !\n[{'aspect': 'uni hand roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: If you live in new york city , you 'll find better food at small restaurants outside of time square and spend half the amount .\n->If you live in new york city , you 'll find better food at small restaurants outside of time square and spend half the amount .\n[{'aspect': 'food', 'opinion': 'better', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the hard drive is definitely slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe hard drive is definitely slow .\n->", + "output": "{\"text\": \"the hard drive is definitely slow .\", \"labels\": \"[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n->my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n[{'aspect': 'meal', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this laptop is great for a lot of modern games .\n->this laptop is great for a lot of modern games .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: after only a little over a month , it totally died and will not work at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter only a little over a month , it totally died and will not work at all .\n->", + "output": "{\"text\": \"after only a little over a month , it totally died and will not work at all .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n->i tried messing around in the dev mode to adjust the microphone sensitivity but nothing seems to work .\n[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: this is my first macbook pro i ' m impress !\n->this is my first macbook pro i ' m impress !\n[{'aspect': 'macbook pro', 'opinion': 'impress', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i love asus but this one is super slow !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love asus but this one is super slow !\n->", + "output": "{\"text\": \"i love asus but this one is super slow !\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'love', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n->the menu looked great , and the waiter was very nice , but when the food came , it was average .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The best burger I have had in the Village .\n->The best burger I have had in the Village .\n[{'aspect': 'burger', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: extremely slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nextremely slow .\n->", + "output": "{\"text\": \"extremely slow .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was well prepared and the service impecable .\n->The food was well prepared and the service impecable .\n[{'aspect': 'food', 'opinion': 'well prepared', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impecable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n->All the pastas are fantastic and the homemade lasagna is some of the best that I have had in the City .\n[{'aspect': 'pastas', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade lasagna', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very junky\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery junky\n->", + "output": "{\"text\": \"very junky\", \"labels\": \"[{'aspect': 'junky', 'opinion': 'junky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n->i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n[{'aspect': 'look', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'speed', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: so be forwarded , if you buy this , you are jumping right into a machine that won ' t get updates anymore ( which is something most chromebook owners want and a feature that makes them better than android and it ' s fragmented market ) .\n->so be forwarded , if you buy this , you are jumping right into a machine that won ' t get updates anymore ( which is something most chromebook owners want and a feature that makes them better than android and it ' s fragmented market ) .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: sound went out in less than a week .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsound went out in less than a week .\n->", + "output": "{\"text\": \"sound went out in less than a week .\", \"labels\": \"[{'aspect': 'sound went', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sushi was awful !\n->the sushi was awful !\n[{'aspect': 'sushi', 'opinion': 'awful', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: after years of using amazon for hundreds of orders , this is my very first negative review :\n->after years of using amazon for hundreds of orders , this is my very first negative review :\n[{'aspect': 'amazon', 'opinion': 'negative', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: this item cost me money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis item cost me money .\n->", + "output": "{\"text\": \"this item cost me money .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In such a crappy part of town to find a good value for lunch , this place is great .\n->In such a crappy part of town to find a good value for lunch , this place is great .\n[{'aspect': 'value', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Each bite of food at Kai was indeed delicious , fresh , and elegant .\n->Each bite of food at Kai was indeed delicious , fresh , and elegant .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'elegant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: after running through the setup wizard , the laptop failed to boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter running through the setup wizard , the laptop failed to boot .\n->", + "output": "{\"text\": \"after running through the setup wizard , the laptop failed to boot .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'failed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best Reuben sandwich ever !\n->Best Reuben sandwich ever !\n[{'aspect': 'Reuben sandwich', 'opinion': 'Best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i did not get what i originally paid for .\n->i did not get what i originally paid for .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: though it is competitively priced for the specs , the laptop felt cheap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthough it is competitively priced for the specs , the laptop felt cheap .\n->", + "output": "{\"text\": \"though it is competitively priced for the specs , the laptop felt cheap .\", \"labels\": \"[{'aspect': 'specs', 'opinion': 'competitively', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'laptop', 'opinion': 'cheap', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not impressed with the food .\n->Not impressed with the food .\n[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: keep up the good work .\n->keep up the good work .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i eventually returned it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni eventually returned it .\n->", + "output": "{\"text\": \"i eventually returned it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n->i have never left a restaurant feeling as if i was abused , and wasted my hard earned money .\n[{'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'abused', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: we were greeted promptly by the waiter who was very nice and cordial .\n->we were greeted promptly by the waiter who was very nice and cordial .\n[{'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'cordial', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'promptly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n->", + "output": "{\"text\": \"ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\", \"labels\": \"[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n->this place has the the correct ambience and an excellent staff to make you feel like a guest and a friend at the same time .\n[{'aspect': 'ambience', 'opinion': 'correct', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'staff', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n->sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n[{'aspect': 'server', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: they are clearly working with more than one person at a time , and not effective multi - taskers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey are clearly working with more than one person at a time , and not effective multi - taskers .\n->", + "output": "{\"text\": \"they are clearly working with more than one person at a time , and not effective multi - taskers .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'not effective', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n->also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n[{'aspect': 'service', 'opinion': 'place', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'the', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'just', 'opinion': \"' re\", 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: What an amazing meal and experience !\n->What an amazing meal and experience !\n[{'aspect': 'meal', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: laptop screen goes blank after four weeks minimally used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlaptop screen goes blank after four weeks minimally used .\n->", + "output": "{\"text\": \"laptop screen goes blank after four weeks minimally used .\", \"labels\": \"[{'aspect': 'laptop screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the decor is night tho . . . but they really need to clean that vent in the ceiling . . . its quite un - appetizing , and kills your effort to make this place look sleek and modern .\n->the decor is night tho . . . but they really need to clean that vent in the ceiling . . . its quite un - appetizing , and kills your effort to make this place look sleek and modern .\n[{'aspect': 'place', 'opinion': 'sleek', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'modern', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'night', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'vent', 'opinion': 'un - appetizing', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it is also very gnu + linux friendly if you want to replace the os entirely .\n->it is also very gnu + linux friendly if you want to replace the os entirely .\n[{'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: then it would not boot up .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen it would not boot up .\n->", + "output": "{\"text\": \"then it would not boot up .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They have authentic Indian at amazin prices .\n->They have authentic Indian at amazin prices .\n[{'aspect': 'Indian', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'amazin', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n->With the exception of our lemon salad that had so much pepper on it that our eyes started watering , the food here was decent , not great .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'not great', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'pepper', 'opinion': 'much', 'polarity': 'negative', 'category': 'NULL'}]\ntext: screen turn black and won ' t turn on within a month rarely use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen turn black and won ' t turn on within a month rarely use .\n->", + "output": "{\"text\": \"screen turn black and won ' t turn on within a month rarely use .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portion sizes here are huge , and the sushi is good .\n->The portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: He offers subpar service and has no personality .\n->He offers subpar service and has no personality .\n[{'aspect': 'service', 'opinion': 'subpar', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - excellent cpu\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- excellent cpu\n->", + "output": "{\"text\": \"- excellent cpu\", \"labels\": \"[{'aspect': 'cpu', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wifi was pretty bad .\n->wifi was pretty bad .\n[{'aspect': 'wifi was', 'opinion': 'bad', 'polarity': 'negative', 'category': 'PORTS#QUALITY'}]\nExample:\ntext: The fried dumplings are GREAT !\n->The fried dumplings are GREAT !\n[{'aspect': 'fried dumplings', 'opinion': 'GREAT', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - good ram\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- good ram\n->", + "output": "{\"text\": \"- good ram\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'good', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'homemade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'herbs', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my only complaint is that the trackpad is just awful .\n->my only complaint is that the trackpad is just awful .\n[{'aspect': 'trackpad', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'awful', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\ntext: then the light bleed becomes annoying and distracting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen the light bleed becomes annoying and distracting .\n->", + "output": "{\"text\": \"then the light bleed becomes annoying and distracting .\", \"labels\": \"[{'aspect': 'light bleed', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'light bleed', 'opinion': 'distracting', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food and the prices are very reasonable .\n->Great food and the prices are very reasonable .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the usb - c ports are nice and i found that a completely dead battery to fully charged time was about an hour .\n->the usb - c ports are nice and i found that a completely dead battery to fully charged time was about an hour .\n[{'aspect': 'usb - c ports', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'charged time', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: - the hard drive is really slow and really loud .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the hard drive is really slow and really loud .\n->", + "output": "{\"text\": \"- the hard drive is really slow and really loud .\", \"labels\": \"[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'hard drive', 'opinion': 'loud', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i use it for gaming and it runs rocket league at max graphics and it looks amazing !\n->i use it for gaming and it runs rocket league at max graphics and it looks amazing !\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n->I LOVE their spicy scallop roll , and my boyfriend consistently gets the sesame chicken .\n[{'aspect': 'scallop roll', 'opinion': 'LOVE', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overall : the specs for this computer looked pretty good , but after using it for a few weeks , the negatives outweigh the positives , and i ' m going to have to return it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall : the specs for this computer looked pretty good , but after using it for a few weeks , the negatives outweigh the positives , and i ' m going to have to return it .\n->", + "output": "{\"text\": \"overall : the specs for this computer looked pretty good , but after using it for a few weeks , the negatives outweigh the positives , and i ' m going to have to return it .\", \"labels\": \"[{'aspect': 'specs for this computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n->Even the pasta is delicious here ( a rarity in New York pizza restaurants ) .\n[{'aspect': 'pasta', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i was very excited at the prospect of buying this laptop .\n->i was very excited at the prospect of buying this laptop .\n[{'aspect': 'laptop', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: beware this product seems to have no quality control .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbeware this product seems to have no quality control .\n->", + "output": "{\"text\": \"beware this product seems to have no quality control .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the force touch trackpad works great .\n->the force touch trackpad works great .\n[{'aspect': 'force touch trackpad', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n->for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i was using it and the screen started to flicker and then went completely dim .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was using it and the screen started to flicker and then went completely dim .\n->", + "output": "{\"text\": \"i was using it and the screen started to flicker and then went completely dim .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'flicker', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the kind of place you ' d like to take all your friends to and still keep a secret .\n->this is the kind of place you ' d like to take all your friends to and still keep a secret .\n[{'aspect': 'place', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: - perfect balance of speed and battery life\n->- perfect balance of speed and battery life\n[{'aspect': 'battery life', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: also tech support gave incorrect product info !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nalso tech support gave incorrect product info !\n->", + "output": "{\"text\": \"also tech support gave incorrect product info !\", \"labels\": \"[{'aspect': 'tech support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n->The staff has always been attentive and kind , and I 've always been amazed at how they 've handled all the various different group sizes that come in .\n[{'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'kind', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n->In fact , while leaving the place we saw two people looking at the menu , and I could n't help telling them that the food was horrible .\n[{'aspect': 'food', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it ' s a good enough laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a good enough laptop .\n->", + "output": "{\"text\": \"it ' s a good enough laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i will say , however , that the screen is kind of awful .\n->i will say , however , that the screen is kind of awful .\n[{'aspect': 'screen', 'opinion': 'awful', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: we were seated outside and the waiter spilled red wine and hot tea on myself and my date .\n->we were seated outside and the waiter spilled red wine and hot tea on myself and my date .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: customer service told me it ' s faulty\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncustomer service told me it ' s faulty\n->", + "output": "{\"text\": \"customer service told me it ' s faulty\", \"labels\": \"[{'aspect': 'customer service', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I plan to come here again and look forward to trying their assortment of bruschetta , panini 's ... ..\n->I plan to come here again and look forward to trying their assortment of bruschetta , panini 's ... ..\n[{'aspect': 'bruschetta', 'opinion': 'look forward', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'panini', 'opinion': 'look forward', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the crust was imazingly cooked well and pizza was fully loaded : ) : ) : )\n->the crust was imazingly cooked well and pizza was fully loaded : ) : ) : )\n[{'aspect': 'crust', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pizza', 'opinion': 'fully loaded', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: power plug doesn ' t fit well - connection is erratic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npower plug doesn ' t fit well - connection is erratic .\n->", + "output": "{\"text\": \"power plug doesn ' t fit well - connection is erratic .\", \"labels\": \"[{'aspect': 'power plug', 'opinion': \"' t fit well\", 'polarity': 'negative', 'category': 'POWER_SUPPLY#CONNECTIVITY'}, {'aspect': 'power plug', 'opinion': 'erratic', 'polarity': 'negative', 'category': 'POWER_SUPPLY#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recommend it , definitely\n->i recommend it , definitely\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food is all shared so we get to order together and eat together .\n->the food is all shared so we get to order together and eat together .\n[{'aspect': 'food', 'opinion': 'shared', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the vivobook f510ua is a great laptop with fantastic specs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe vivobook f510ua is a great laptop with fantastic specs .\n->", + "output": "{\"text\": \"the vivobook f510ua is a great laptop with fantastic specs .\", \"labels\": \"[{'aspect': 'vivobook f510ua', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'specs', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: During the course of the past 3 months , the chef and staff changed and it was not for the better .\n->During the course of the past 3 months , the chef and staff changed and it was not for the better .\n[{'aspect': 'chef', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'changed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: do n ' t miss bloom ' s on your next trip to manhatten .\n->do n ' t miss bloom ' s on your next trip to manhatten .\n[{'aspect': \"bloom ' s\", 'opinion': \"n ' t miss\", 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: however i ' m happy to report that the keyboard is great and i ' ve already gotten use to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever i ' m happy to report that the keyboard is great and i ' ve already gotten use to it .\n->", + "output": "{\"text\": \"however i ' m happy to report that the keyboard is great and i ' ve already gotten use to it .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'happy', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this laptop is actually horrible .\n->this laptop is actually horrible .\n[{'aspect': 'laptop', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The first time the sushi was outstanding , the second time it was a little bland .\n->The first time the sushi was outstanding , the second time it was a little bland .\n[{'aspect': 'sushi', 'opinion': 'outstanding', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}]\ntext: with my first day of use everything was terribly slow and updates took hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith my first day of use everything was terribly slow and updates took hours .\n->", + "output": "{\"text\": \"with my first day of use everything was terribly slow and updates took hours .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great for a romantic evening , or a fun evening with friends . . .\n->great for a romantic evening , or a fun evening with friends . . .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'NULL', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: best laptop i ' ve ever owned .\n->best laptop i ' ve ever owned .\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: be prepared to wait a good five hours give or take until the system runs smoother .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbe prepared to wait a good five hours give or take until the system runs smoother .\n->", + "output": "{\"text\": \"be prepared to wait a good five hours give or take until the system runs smoother .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'good', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'system', 'opinion': 'smoother', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n->chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n[{'aspect': 'chow fun', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'pork shu mai', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'loud', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'rude', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: the build quality on this laptop is awesome for the price .\n->the build quality on this laptop is awesome for the price .\n[{'aspect': 'build quality', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: 2 ssd as it will not fit the slot available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n2 ssd as it will not fit the slot available .\n->", + "output": "{\"text\": \"2 ssd as it will not fit the slot available .\", \"labels\": \"[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n->i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n[{'aspect': 'asus customer service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: Decent wine selection too .\n->Decent wine selection too .\n[{'aspect': 'wine selection', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 2 stars taken for horrible sound quality\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n2 stars taken for horrible sound quality\n->", + "output": "{\"text\": \"2 stars taken for horrible sound quality\", \"labels\": \"[{'aspect': 'sound quality', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n->i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n[{'aspect': 'size', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: this chromebook is fast .\n->this chromebook is fast .\n[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this computer completely shut down after 2 months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer completely shut down after 2 months .\n->", + "output": "{\"text\": \"this computer completely shut down after 2 months .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google is amazing .\n->google is amazing .\n[{'aspect': 'google', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n->My boyfriend and I recently had an early dinner at Artisanal and was satisfied with our experience .\n[{'aspect': 'dinner', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i can ' t get it to work at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni can ' t get it to work at all .\n->", + "output": "{\"text\": \"i can ' t get it to work at all .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also bought a wireless mouse , which paired perfectly .\n->i also bought a wireless mouse , which paired perfectly .\n[{'aspect': 'wireless mouse', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n->this is completely different , feels solid , looks great , operates like a great samsung chromebook should , and for $ 169 , you just can ' t go wrong .\n[{'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'samsung chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'samsung chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it ' s one of the worst laptops i ' ve ever had .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s one of the worst laptops i ' ve ever had .\n->", + "output": "{\"text\": \"it ' s one of the worst laptops i ' ve ever had .\", \"labels\": \"[{'aspect': 'laptops', 'opinion': 'worst', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality , screen and keyboard are terrific .\n->the build quality , screen and keyboard are terrific .\n[{'aspect': 'build quality', 'opinion': 'terrific', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'terrific', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'terrific', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: It was so bad I actually refused to pay for my food .\n->It was so bad I actually refused to pay for my food .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this is junk .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is junk .\n->", + "output": "{\"text\": \"this is junk .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'junk', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: samsung has not been helpful in getting this fixed .\n->samsung has not been helpful in getting this fixed .\n[{'aspect': 'samsung', 'opinion': 'not been helpful', 'polarity': 'negative', 'category': 'COMPANY#OPERATION_PERFORMANCE'}]\nExample:\ntext: it is also very gnu + linux friendly if you want to replace the os entirely .\n->it is also very gnu + linux friendly if you want to replace the os entirely .\n[{'aspect': 'NULL', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: it is going on 3 hours now and is only 17 % done .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is going on 3 hours now and is only 17 % done .\n->", + "output": "{\"text\": \"it is going on 3 hours now and is only 17 % done .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: update : device now has full support for the play store on the beta channel .\n->update : device now has full support for the play store on the beta channel .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: Food is excellent .\n->Food is excellent .\n[{'aspect': 'Food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the item is good but the sound of speakers is very low .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe item is good but the sound of speakers is very low .\n->", + "output": "{\"text\": \"the item is good but the sound of speakers is very low .\", \"labels\": \"[{'aspect': 'item', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sound of speakers', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Big Wong is a great place to eat and fill your stomach .\n->Big Wong is a great place to eat and fill your stomach .\n[{'aspect': 'Big Wong', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 1 ) the delete button is right next to the power button\n->1 ) the delete button is right next to the power button\n[{'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: otherwise , it has everything i can / might / could love .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \notherwise , it has everything i can / might / could love .\n->", + "output": "{\"text\": \"otherwise , it has everything i can / might / could love .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sent it in , arrived back in an inadequate box ( i shipped it in the oroginal with protective foam came back in the box from samsung w / o any protection ) and 3 days after i recieved the item back the stylus fell apart .\n->sent it in , arrived back in an inadequate box ( i shipped it in the oroginal with protective foam came back in the box from samsung w / o any protection ) and 3 days after i recieved the item back the stylus fell apart .\n[{'aspect': 'stylus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#QUALITY'}]\nExample:\ntext: my main gripe is incompatibility with amazon prime videos and gogo .\n->my main gripe is incompatibility with amazon prime videos and gogo .\n[{'aspect': 'amazon prime videos and gogo', 'opinion': 'gripe', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\ntext: my only other complaint is trackpad sensitivity .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy only other complaint is trackpad sensitivity .\n->", + "output": "{\"text\": \"my only other complaint is trackpad sensitivity .\", \"labels\": \"[{'aspect': 'trackpad sensitivity', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We ate at this Thai place following the reviews but very unhappy with the foods .\n->We ate at this Thai place following the reviews but very unhappy with the foods .\n[{'aspect': 'foods', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n->wanting something maybe a pinch faster , but more importantly , a flippy touchscreen with google play support .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhaving win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n->", + "output": "{\"text\": \"having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\", \"labels\": \"[{'aspect': 'win 8', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n->The parathas and kebabs are made when ordered ensuring a level of freshness that is unsurpassed .\n[{'aspect': 'parathas', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kebabs', 'opinion': 'unsurpassed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: as of now , highly recommended refurbished products\n->as of now , highly recommended refurbished products\n[{'aspect': 'products', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n->", + "output": "{\"text\": \"this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lenovo has not disappointed with their products .\n->lenovo has not disappointed with their products .\n[{'aspect': 'lenovo', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'products', 'opinion': 'not disappointed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: with how slim this thing is i really don ' t see a need for the macbook air line .\n->with how slim this thing is i really don ' t see a need for the macbook air line .\n[{'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: screen maximum brightness is still not bright enough\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen maximum brightness is still not bright enough\n->", + "output": "{\"text\": \"screen maximum brightness is still not bright enough\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the display is beautiful and the amount if software you get makes it worth the price !\n->the display is beautiful and the amount if software you get makes it worth the price !\n[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'software', 'opinion': 'worth', 'polarity': 'positive', 'category': 'SOFTWARE#PRICE'}]\nExample:\ntext: not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n->not only is it light the battery charger is tiny and with the thin bezels this is a super compact design .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'battery charger', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: two stars because the current laptop i have works great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntwo stars because the current laptop i have works great .\n->", + "output": "{\"text\": \"two stars because the current laptop i have works great .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is not worth the prices .\n->this place is not worth the prices .\n[{'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'place', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: An excellent service\n->An excellent service\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it felt flimsy , but decent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit felt flimsy , but decent .\n->", + "output": "{\"text\": \"it felt flimsy , but decent .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , i ' d be super upset if that were my employee .\n->again , i ' d be super upset if that were my employee .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the food was absolutely amazing ! !\n->the food was absolutely amazing ! !\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: very slow , and always hangs\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery slow , and always hangs\n->", + "output": "{\"text\": \"very slow , and always hangs\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: dessert is a joke . . . dont bother\n->dessert is a joke . . . dont bother\n[{'aspect': 'dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: note : i haven ' t had any issues with the touchscreen at all .\n->note : i haven ' t had any issues with the touchscreen at all .\n[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: it ' s a pretty good laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a pretty good laptop .\n->", + "output": "{\"text\": \"it ' s a pretty good laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook is an amazing product with one huge glaring flaw - you can not use the sd card with any android apps , nor use android apps with wifi tethering or anything reaching ` ` outside the sandbox ` ` .\n->the chromebook is an amazing product with one huge glaring flaw - you can not use the sd card with any android apps , nor use android apps with wifi tethering or anything reaching ` ` outside the sandbox ` ` .\n[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'sd card', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The service is awful .\n->The service is awful .\n[{'aspect': 'service', 'opinion': 'awful', 'polarity': 'negative', 'category': 'NULL'}]\ntext: nicely sized , thin and portable , the works .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnicely sized , thin and portable , the works .\n->", + "output": "{\"text\": \"nicely sized , thin and portable , the works .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place itself is beautiful the bar scene seems to be happening .\n->The place itself is beautiful the bar scene seems to be happening .\n[{'aspect': 'place', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar scene', 'opinion': 'happening', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the waiter was attentive , the food was delicious and the views of the city were great .\n->the waiter was attentive , the food was delicious and the views of the city were great .\n[{'aspect': 'waiter', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'views of the city', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: the only issue i ' m having is battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only issue i ' m having is battery life .\n->", + "output": "{\"text\": \"the only issue i ' m having is battery life .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ( the asparagus , truffle oil , parmesan bruschetta is a winner ! )\n->( the asparagus , truffle oil , parmesan bruschetta is a winner ! )\n[{'aspect': 'asparagus', 'opinion': 'winner', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'truffle oil', 'opinion': 'winner', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'parmesan bruschetta', 'opinion': 'winner', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: we were n ' t !\n->we were n ' t !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\n->", + "output": "{\"text\": \"thing says 8 hours , but i can guarantee it ' s not making it past 4 or 5 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a chrome book it is excellent , but android support is unsatisfying .\n->as a chrome book it is excellent , but android support is unsatisfying .\n[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'android support', 'opinion': 'unsatisfying', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\nExample:\ntext: another thing , it is much thinner than expected .\n->another thing , it is much thinner than expected .\n[{'aspect': 'NULL', 'opinion': 'thinner', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: but yeah overall good\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut yeah overall good\n->", + "output": "{\"text\": \"but yeah overall good\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was efficient courteous .\n->service was efficient courteous .\n[{'aspect': 'service', 'opinion': 'efficient courteous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: bought it for a xmas gift , dead in less than 3 months .\n->bought it for a xmas gift , dead in less than 3 months .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: it runs pretty fast but the keyboard is not lit , the speakers are on the bottom and the track pad is a pos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit runs pretty fast but the keyboard is not lit , the speakers are on the bottom and the track pad is a pos .\n->", + "output": "{\"text\": \"it runs pretty fast but the keyboard is not lit , the speakers are on the bottom and the track pad is a pos .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'not lit', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}, {'aspect': 'track pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\n->The mussles were the fishiest things I 've ever tasted , the seabass was bland , the goat cheese salad was missing the goat cheese , the penne w/ chicken had bones in it ... It was disgusting .\n[{'aspect': 'mussles', 'opinion': 'fishiest', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seabass', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'goat cheese salad', 'opinion': 'missing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'penne w/ chicken', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: they charge different prices all the time .\n->they charge different prices all the time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: besides that it runs ok\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbesides that it runs ok\n->", + "output": "{\"text\": \"besides that it runs ok\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not a small feat for good french food in the area .\n->Not a small feat for good french food in the area .\n[{'aspect': 'french food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this device would be a good choice if it weren ' t so poorly constructed .\n->this device would be a good choice if it weren ' t so poorly constructed .\n[{'aspect': 'device', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'poorly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: update - dead pixel in middle screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nupdate - dead pixel in middle screen .\n->", + "output": "{\"text\": \"update - dead pixel in middle screen .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'dead', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: inexpensive , unassuming , great time !\n->inexpensive , unassuming , great time !\n[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'unassuming', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the steelsteries keyboard feels great and looks awesome , but the keyboard surface of the laptop can get warm while sitting idle or gaming even when i have a fan pushing cold air underneath the laptop .\n->the steelsteries keyboard feels great and looks awesome , but the keyboard surface of the laptop can get warm while sitting idle or gaming even when i have a fan pushing cold air underneath the laptop .\n[{'aspect': 'steelsteries keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'steelsteries keyboard', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nreplacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\n->", + "output": "{\"text\": \"replacement ordered and arrived , upon first ten minutes of use the keyboard stops working and is barely functioning sound on max is extremely undesirable .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n->we recently decided to try this location , and to our delight , they have outdoor seating , perfect since i had my yorkie with me .\n[{'aspect': 'outdoor seating', 'opinion': 'delight', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'outdoor seating', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The place was quiet and delightful .\n->The place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: second update - speakers no longer work after 1 month of use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsecond update - speakers no longer work after 1 month of use .\n->", + "output": "{\"text\": \"second update - speakers no longer work after 1 month of use .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->The bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n->the hdd that comes with the computer is slow and loud , in fact the computer is not usable by todays standard , it ' s terribly slow .\n[{'aspect': 'NULL', 'opinion': 'not usable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\ntext: runs ok , cheapish , good for price i guess\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nruns ok , cheapish , good for price i guess\n->", + "output": "{\"text\": \"runs ok , cheapish , good for price i guess\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'cheapish', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the design is very cool .\n->the design is very cool .\n[{'aspect': 'design', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: looks brand new and the battery life lasts a long time ( see photos )\n->looks brand new and the battery life lasts a long time ( see photos )\n[{'aspect': 'NULL', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: the battery broke after just 4 months from baying it am so disappointed with the product\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery broke after just 4 months from baying it am so disappointed with the product\n->", + "output": "{\"text\": \"the battery broke after just 4 months from baying it am so disappointed with the product\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}, {'aspect': 'product', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was the only thing good about this restaurant .\n->The service was the only thing good about this restaurant .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you can watch 1080p video on it and it looks great .\n->you can watch 1080p video on it and it looks great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: do not buy this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndo not buy this laptop .\n->", + "output": "{\"text\": \"do not buy this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop died after just one month .\n->the laptop died after just one month .\n[{'aspect': 'laptop', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the screen is excellent .\n->the screen is excellent .\n[{'aspect': 'screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: i returned it twice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni returned it twice .\n->", + "output": "{\"text\": \"i returned it twice .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n->The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n[{'aspect': 'three course meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: happy i got a great machine for half the cost !\n->happy i got a great machine for half the cost !\n[{'aspect': 'machine', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: and when it did work it was very slow .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand when it did work it was very slow .\n->", + "output": "{\"text\": \"and when it did work it was very slow .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .\n->the lobster sandwich is $ 24 and although it was good it was not nearly enough to warrant that price .\n[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster sandwich', 'opinion': 'not nearly enough', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: the svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->the svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'must', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: windows 10 alone runs slow on this but once you install some 3rd party programs like adobe photoshop or illistrator , this laptop is unuseable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwindows 10 alone runs slow on this but once you install some 3rd party programs like adobe photoshop or illistrator , this laptop is unuseable .\n->", + "output": "{\"text\": \"windows 10 alone runs slow on this but once you install some 3rd party programs like adobe photoshop or illistrator , this laptop is unuseable .\", \"labels\": \"[{'aspect': 'windows 10', 'opinion': 'slow', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'unuseable', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n->the bagels always warm , soft on the inside , crispy on the outside and enormous in size .\n[{'aspect': 'bagels', 'opinion': 'warm', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'soft', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'bagels', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i absolutely love this chromebook .\n->i absolutely love this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: even having a couple of programs open & running , slows down the overall performance of the system dramatically .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven having a couple of programs open & running , slows down the overall performance of the system dramatically .\n->", + "output": "{\"text\": \"even having a couple of programs open & running , slows down the overall performance of the system dramatically .\", \"labels\": \"[{'aspect': 'system', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my friends settled for rice dishes , but we came back the following day to try the dim sum , which was good . . . not outstanding , but good .\n->my friends settled for rice dishes , but we came back the following day to try the dim sum , which was good . . . not outstanding , but good .\n[{'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'dim sum', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'dim sum', 'opinion': 'not outstanding', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n->i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\ntext: i ' ve been waiting for this chromebook for some time now and having only had it for about two weeks , i can say that the device and purchase were well worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been waiting for this chromebook for some time now and having only had it for about two weeks , i can say that the device and purchase were well worth it .\n->", + "output": "{\"text\": \"i ' ve been waiting for this chromebook for some time now and having only had it for about two weeks , i can say that the device and purchase were well worth it .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now my main complaint is that is it very slow , especially for a new computer !\n->now my main complaint is that is it very slow , especially for a new computer !\n[{'aspect': 'computer', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: a thoroughly disappointing machine .\n->a thoroughly disappointing machine .\n[{'aspect': 'machine', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: 5 ` ` ips screen that is gorgeous to look at , extremely bright and full 1080p resolution .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n5 ` ` ips screen that is gorgeous to look at , extremely bright and full 1080p resolution .\n->", + "output": "{\"text\": \"5 ` ` ips screen that is gorgeous to look at , extremely bright and full 1080p resolution .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is only 3 .\n->battery life is only 3 .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: What an amazing meal and experience !\n->What an amazing meal and experience !\n[{'aspect': 'meal', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\n->", + "output": "{\"text\": \"a solid little m3 processor , 4gb of ram and 64gb of ssd storage make this machine capable of handling heavy usage and earn it a solid octane 2 .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}, {'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great laptop for web browsing , skype , and more simple games .\n->this is a great laptop for web browsing , skype , and more simple games .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: lucky strike is a great casual place to just grab a bite to eat .\n->lucky strike is a great casual place to just grab a bite to eat .\n[{'aspect': 'lucky strike', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'lucky strike', 'opinion': 'casual', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the surface texture of the case is a satiny paper - like touch , which is both clean and easy to wipe down , but also not so smooth that you have to be concerned with it slipping out of your hand when carrying it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe surface texture of the case is a satiny paper - like touch , which is both clean and easy to wipe down , but also not so smooth that you have to be concerned with it slipping out of your hand when carrying it .\n->", + "output": "{\"text\": \"the surface texture of the case is a satiny paper - like touch , which is both clean and easy to wipe down , but also not so smooth that you have to be concerned with it slipping out of your hand when carrying it .\", \"labels\": \"[{'aspect': 'surface texture of the case', 'opinion': 'clean', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'surface texture of the case', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n->the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n[{'aspect': 'keyboard', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\nExample:\ntext: very disappointing , samsung !\n->very disappointing , samsung !\n[{'aspect': 'samsung', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\n->", + "output": "{\"text\": \"even with the 2 - in - 1 flip hinge , the device has a solid feel and there ' s very little wobble in the screen while typing .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pizza is terrific , as is homemade pasta .\n->Pizza is terrific , as is homemade pasta .\n[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve had mine 10 months and the motherboard has crapped out twice already .\n->i ' ve had mine 10 months and the motherboard has crapped out twice already .\n[{'aspect': 'motherboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MOTHERBOARD#OPERATION_PERFORMANCE'}]\ntext: the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\n->", + "output": "{\"text\": \"the pop - up keyboard looks just like the android screen keyboard and the orientation detection and capacitive touch screen are snappy and very responsive .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it will however win with substance .\n->it will however win with substance .\n[{'aspect': 'NULL', 'opinion': 'win', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: tapping it on either end is hit or miss .\n->tapping it on either end is hit or miss .\n[{'aspect': 'tapping', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\n->", + "output": "{\"text\": \"one place that does frustrate me in the tablet mode is that chromeos has a number of ui features that don ' t lend themselves to touch interaction .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'frustrate', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is exactly what i needed in a laptop .\n->this is exactly what i needed in a laptop .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is a decent laptop no thanks to asus support .\n->this is a decent laptop no thanks to asus support .\n[{'aspect': 'laptop', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it has a glassy - smooth surface that resists finger - prints and goop , is very large , and has a firm but not too - clunky feeling click to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has a glassy - smooth surface that resists finger - prints and goop , is very large , and has a firm but not too - clunky feeling click to it .\n->", + "output": "{\"text\": \"it has a glassy - smooth surface that resists finger - prints and goop , is very large , and has a firm but not too - clunky feeling click to it .\", \"labels\": \"[{'aspect': 'surface', 'opinion': 'glassy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'surface', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'surface', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'surface', 'opinion': 'firm', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n->To be completely fair , the only redeeming factor was the food , which was above average , but could n't make up for all the other deficiencies of Teodora .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Pizza was a little soggy .\n->Pizza was a little soggy .\n[{'aspect': 'Pizza', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it takes up more than 1 / 3rd of the palm - area horizontal space and makes thumb - tapping mouse tweaks very easy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit takes up more than 1 / 3rd of the palm - area horizontal space and makes thumb - tapping mouse tweaks very easy .\n->", + "output": "{\"text\": \"it takes up more than 1 / 3rd of the palm - area horizontal space and makes thumb - tapping mouse tweaks very easy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: was installing an update and the computer went black .\n->was installing an update and the computer went black .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: chromebook is 2 months old and charger stopped working .\n->chromebook is 2 months old and charger stopped working .\n[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: finally , there are the ports .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfinally , there are the ports .\n->", + "output": "{\"text\": \"finally , there are the ports .\", \"labels\": \"[{'aspect': 'ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambiance -- relaxed and stylish .\n->Ambiance -- relaxed and stylish .\n[{'aspect': 'Ambiance', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambiance', 'opinion': 'stylish', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it ' s fast and very easy to use if you are familiar with google drive .\n->it ' s fast and very easy to use if you are familiar with google drive .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: - beautiful , bright ips screen with full 1080p resolution\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- beautiful , bright ips screen with full 1080p resolution\n->", + "output": "{\"text\": \"- beautiful , bright ips screen with full 1080p resolution\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n->so if you want to buy this computer , make sure you ' re prepared for some early booting issues .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\nExample:\ntext: build quality seems excellent .\n->build quality seems excellent .\n[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: - backlit keyboard rocks\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- backlit keyboard rocks\n->", + "output": "{\"text\": \"- backlit keyboard rocks\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great food , great decor , great service .\n->great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The pizza is delicious and the proprietor is one of the nicest in NYC .\n->The pizza is delicious and the proprietor is one of the nicest in NYC .\n[{'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'proprietor', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - battery life is pretty amazing at 10 - 11hrs\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- battery life is pretty amazing at 10 - 11hrs\n->", + "output": "{\"text\": \"- battery life is pretty amazing at 10 - 11hrs\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great pizza and fantastic service .\n->Great pizza and fantastic service .\n[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: after years of using amazon for hundreds of orders , this is my very first negative review :\n->after years of using amazon for hundreds of orders , this is my very first negative review :\n[{'aspect': 'amazon', 'opinion': 'negative', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: - 4gb ram cap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- 4gb ram cap .\n->", + "output": "{\"text\": \"- 4gb ram cap .\", \"labels\": \"[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: certain apps ( especially flash based apps ) will get the machine very hot .\n->certain apps ( especially flash based apps ) will get the machine very hot .\n[{'aspect': 'apps', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: probably would not go back here .\n->probably would not go back here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: - many ui elements are not user - friendly in tablet mode\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- many ui elements are not user - friendly in tablet mode\n->", + "output": "{\"text\": \"- many ui elements are not user - friendly in tablet mode\", \"labels\": \"[{'aspect': 'ui elements', 'opinion': 'not user - friendly', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is so cheap and the waiters are nice .\n->The food is so cheap and the waiters are nice .\n[{'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: battery life appears good but will depend on your brightness and how much streaming your doing .\n->battery life appears good but will depend on your brightness and how much streaming your doing .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: - port minimalism .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- port minimalism .\n->", + "output": "{\"text\": \"- port minimalism .\", \"labels\": \"[{'aspect': 'port', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen quality is perfect and matte so no annoying glare !\n->screen quality is perfect and matte so no annoying glare !\n[{'aspect': 'screen quality', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: has nice features for the price and nice video for streaming movies .\n->has nice features for the price and nice video for streaming movies .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'video', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\ntext: out of the box , the asus c302ca is a powerhouse for stock chromeos usage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nout of the box , the asus c302ca is a powerhouse for stock chromeos usage .\n->", + "output": "{\"text\": \"out of the box , the asus c302ca is a powerhouse for stock chromeos usage .\", \"labels\": \"[{'aspect': 'asus c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook is quite ideal for those who use their computer mostly for surfing the internet .\n->the chromebook is quite ideal for those who use their computer mostly for surfing the internet .\n[{'aspect': 'chromebook', 'opinion': 'ideal', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\nExample:\ntext: After all that , they complained to me about the small tip .\n->After all that , they complained to me about the small tip .\n[{'aspect': 'tip', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\ntext: performance - wise , i can easily have 12 - 24 tabs open simultaneously and see no slow - down in performance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nperformance - wise , i can easily have 12 - 24 tabs open simultaneously and see no slow - down in performance .\n->", + "output": "{\"text\": \"performance - wise , i can easily have 12 - 24 tabs open simultaneously and see no slow - down in performance .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: moreover i ' m quite upset because it seems amazon will not pay me back the shipping fees , which for me amount to about 100 $ as i live in france .\n->moreover i ' m quite upset because it seems amazon will not pay me back the shipping fees , which for me amount to about 100 $ as i live in france .\n[{'aspect': 'amazon', 'opinion': 'upset', 'polarity': 'negative', 'category': 'SHIPPING#GENERAL'}]\nExample:\ntext: if you need a decent computer that runs quality this is it , especially if you are starting out .\n->if you need a decent computer that runs quality this is it , especially if you are starting out .\n[{'aspect': 'computer', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: one place that i find lacking in chromeos is the settings management .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none place that i find lacking in chromeos is the settings management .\n->", + "output": "{\"text\": \"one place that i find lacking in chromeos is the settings management .\", \"labels\": \"[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: was surprisingly disappointed .\n->was surprisingly disappointed .\n[{'aspect': 'NULL', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the power button is no longer on the keyboard , but is instead on the side of the machine which is fine .\n->the power button is no longer on the keyboard , but is instead on the side of the machine which is fine .\n[{'aspect': 'power button', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: for the most - part you don ' t have much freedom to make any significant changes to settings .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the most - part you don ' t have much freedom to make any significant changes to settings .\n->", + "output": "{\"text\": \"for the most - part you don ' t have much freedom to make any significant changes to settings .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice build quality , very fast and beautiful display .\n->nice build quality , very fast and beautiful display .\n[{'aspect': 'build quality', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'display', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: great device until battery won ' t charge .\n->great device until battery won ' t charge .\n[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: - clean and secure operating system that is very lean and gets the most out of the systems modest specs\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- clean and secure operating system that is very lean and gets the most out of the systems modest specs\n->", + "output": "{\"text\": \"- clean and secure operating system that is very lean and gets the most out of the systems modest specs\", \"labels\": \"[{'aspect': 'operating system', 'opinion': 'clean', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'operating system', 'opinion': 'secure', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'operating system', 'opinion': 'clean', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'specs', 'opinion': 'modest', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the metal case is really well built , and the fit and finish are virtually perfect .\n->the metal case is really well built , and the fit and finish are virtually perfect .\n[{'aspect': 'metal case', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'fit', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'finish', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: nothing on it feels cheap at all .\n->nothing on it feels cheap at all .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: - with the combination of web - based productivity tools and development apps / services , this chromebook can provide a breadth of very viable usage scenarios without bogging the system down with locally install applications .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- with the combination of web - based productivity tools and development apps / services , this chromebook can provide a breadth of very viable usage scenarios without bogging the system down with locally install applications .\n->", + "output": "{\"text\": \"- with the combination of web - based productivity tools and development apps / services , this chromebook can provide a breadth of very viable usage scenarios without bogging the system down with locally install applications .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Pair you food with the excellent beers on tap or their well priced wine list .\n->Pair you food with the excellent beers on tap or their well priced wine list .\n[{'aspect': 'beers on tap', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'well', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - the battery life is at least 8 hours .\n->- the battery life is at least 8 hours .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: - android apps and google play store are real game changers for the chromeos landscape .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- android apps and google play store are real game changers for the chromeos landscape .\n->", + "output": "{\"text\": \"- android apps and google play store are real game changers for the chromeos landscape .\", \"labels\": \"[{'aspect': 'google play store', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n->furthermore , the rice had no seasoning , so the sushi was bland and disgusting .\n[{'aspect': 'rice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'disgusting', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - boot time , sleep time and wake time are crazy fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- boot time , sleep time and wake time are crazy fast .\n->", + "output": "{\"text\": \"- boot time , sleep time and wake time are crazy fast .\", \"labels\": \"[{'aspect': 'boot time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'boot time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'sleep time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'OS#QUALITY'}, {'aspect': 'wake time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keeps disconnecting from my wifi at work .\n->keeps disconnecting from my wifi at work .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n->your new retina screen will get stains on it from the top layer seperating and or dead pixels .\n[{'aspect': 'retina screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: - system settings could be more robust and better organized\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- system settings could be more robust and better organized\n->", + "output": "{\"text\": \"- system settings could be more robust and better organized\", \"labels\": \"[{'aspect': 'system settings', 'opinion': 'robust', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'system settings', 'opinion': 'better', 'polarity': 'neutral', 'category': 'OS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the have a great cocktail with citrus vodka and lemon and lime juice and mint leaves that is to die for !\n->the have a great cocktail with citrus vodka and lemon and lime juice and mint leaves that is to die for !\n[{'aspect': 'cocktail with citrus vodka and lemon and lime juice and mint leaves', 'opinion': 'great', 'polarity': 'positive', 'category': 'DRINKS#QUALITY'}]\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - tablet - mode still needs some work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- tablet - mode still needs some work .\n->", + "output": "{\"text\": \"- tablet - mode still needs some work .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is great quality .\n->it is great quality .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: love the laptop ; great quality ; sent as expected , on time .\n->love the laptop ; great quality ; sent as expected , on time .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: so , this is where this little chromebook really shines .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso , this is where this little chromebook really shines .\n->", + "output": "{\"text\": \"so , this is where this little chromebook really shines .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'shines', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: still , a very nice compact chromebook .\n->still , a very nice compact chromebook .\n[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: he has thoroughly enjoyed it\n->he has thoroughly enjoyed it\n[{'aspect': 'NULL', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: apps start very fast , graphics are much more responsive and capable when not being shared with chromeos and there are a number of ways you can tweak the ui / ux to your own liking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \napps start very fast , graphics are much more responsive and capable when not being shared with chromeos and there are a number of ways you can tweak the ui / ux to your own liking .\n->", + "output": "{\"text\": \"apps start very fast , graphics are much more responsive and capable when not being shared with chromeos and there are a number of ways you can tweak the ui / ux to your own liking .\", \"labels\": \"[{'aspect': 'graphics', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'GRAPHICS#USABILITY'}, {'aspect': 'graphics', 'opinion': 'capable', 'polarity': 'positive', 'category': 'GRAPHICS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am returning immediately , no patience for this .\n->i am returning immediately , no patience for this .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the place was quiet and delightful .\n->the place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\n->", + "output": "{\"text\": \"all that being said , you get a lot of bang for the buck with these three enhanced desktop / application options and i use all three in some capacity every day , so don ' t think your ' e isolated to just one approach .\", \"labels\": \"[{'aspect': 'desktop / application options', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes i get good food and ok service .\n->sometimes i get good food and ok service .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: she loves this laptop .\n->she loves this laptop .\n[{'aspect': 'laptop', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n->", + "output": "{\"text\": \"while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\", \"labels\": \"[{'aspect': 'stock aluminum case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the pizza is yummy and i like the atmoshpere .\n->the pizza is yummy and i like the atmoshpere .\n[{'aspect': 'pizza', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'atmoshpere', 'opinion': 'like', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n->i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\ntext: d ) a bluetooth mouse - while this unarguably has one of the best trackpads in chromebook land , there are still times you just need the precision of a mouse .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nd ) a bluetooth mouse - while this unarguably has one of the best trackpads in chromebook land , there are still times you just need the precision of a mouse .\n->", + "output": "{\"text\": \"d ) a bluetooth mouse - while this unarguably has one of the best trackpads in chromebook land , there are still times you just need the precision of a mouse .\", \"labels\": \"[{'aspect': 'trackpads', 'opinion': 'best', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i still had some of those kinds of mishaps with the chromebook - - but not nearly as many .\n->i still had some of those kinds of mishaps with the chromebook - - but not nearly as many .\n[{'aspect': 'chromebook', 'opinion': 'mishaps', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Friendly and informative staff , very attentive and prompt raw bar service .\n->Friendly and informative staff , very attentive and prompt raw bar service .\n[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'informative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar service', 'opinion': 'raw', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 0 type - a low - profile memory stick on a usb - c adaptor , but that would be silly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n0 type - a low - profile memory stick on a usb - c adaptor , but that would be silly .\n->", + "output": "{\"text\": \"0 type - a low - profile memory stick on a usb - c adaptor , but that would be silly .\", \"labels\": \"[{'aspect': 'usb - c adaptor', 'opinion': 'silly', 'polarity': 'negative', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while their kitchen food is delicious , their sushi is out of this world .\n->while their kitchen food is delicious , their sushi is out of this world .\n[{'aspect': 'kitchen food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n->We started with lox and mussels ( the best ive ever had , ever ) and had the cod and trout for dinner .\n[{'aspect': 'lox', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mussels', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it was an excellent machine for the money and it also got me spoiled with its touch screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit was an excellent machine for the money and it also got me spoiled with its touch screen .\n->", + "output": "{\"text\": \"it was an excellent machine for the money and it also got me spoiled with its touch screen .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'touch screen', 'opinion': 'spoiled', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Sauce was watery and the food did n't have much flavor .\n->Sauce was watery and the food did n't have much flavor .\n[{'aspect': 'Sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': \"did n't have much flavor\", 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: with the great variety on the menu , i eat here often and never get bored .\n->with the great variety on the menu , i eat here often and never get bored .\n[{'aspect': 'menu', 'opinion': 'great variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: this made me realize that arm processors are not ready for desktop - class browsing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis made me realize that arm processors are not ready for desktop - class browsing .\n->", + "output": "{\"text\": \"this made me realize that arm processors are not ready for desktop - class browsing .\", \"labels\": \"[{'aspect': 'arm processors', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n->The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .\n[{'aspect': 'anti-pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this thing is slow !\n->this thing is slow !\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: let me tell you , this thing is snappy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlet me tell you , this thing is snappy .\n->", + "output": "{\"text\": \"let me tell you , this thing is snappy .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The people that work there are always so friendly you forget you are in New York sometimes .\n->The people that work there are always so friendly you forget you are in New York sometimes .\n[{'aspect': 'people', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n->but i would have never bought it if i knew that windows 10 was the worst , god awful garbage operating system i have ever encountered .\n[{'aspect': 'windows 10', 'opinion': 'worst', 'polarity': 'negative', 'category': 'OS#GENERAL'}, {'aspect': 'windows 10', 'opinion': 'awful', 'polarity': 'negative', 'category': 'OS#GENERAL'}]\ntext: the build quality is nice and the hinge is sturdy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is nice and the hinge is sturdy .\n->", + "output": "{\"text\": \"the build quality is nice and the hinge is sturdy .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n->the restaurant has a family feel , not least with regard to the portions which are enormous ; the veal alone could have single - handedly solved third world famine .\n[{'aspect': 'restaurant', 'opinion': 'family feel', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'portions', 'opinion': 'enormous', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'veal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the keyboard also feels nice and the backlighting is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard also feels nice and the backlighting is great .\n->", + "output": "{\"text\": \"the keyboard also feels nice and the backlighting is great .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlighting', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - screen brightness is generally ` ` enough ` ` but maybe not enough for watching dark movies in bright lights .\n->- screen brightness is generally ` ` enough ` ` but maybe not enough for watching dark movies in bright lights .\n[{'aspect': 'screen brightness', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i have had my chromebook for 4 years still works great for internet , netflix , adult education classes .\n->i have had my chromebook for 4 years still works great for internet , netflix , adult education classes .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i use my chromebook a lot in the dark and it ' s a real treat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use my chromebook a lot in the dark and it ' s a real treat .\n->", + "output": "{\"text\": \"i use my chromebook a lot in the dark and it ' s a real treat .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'treat', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great indian food and the service is incredible .\n->great indian food and the service is incredible .\n[{'aspect': 'indian food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n->having win 10 is already a horrible mistake as so , but if you try to install win 8 , drivers won ' t work properly !\n[{'aspect': 'win 8', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: while the chromebook pro does have a nice high - resolution 3 : 2 screen and the s - pen ( which is truly a gimmick anyway ) , it has half the storage , shorter battery life , and no backlit keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile the chromebook pro does have a nice high - resolution 3 : 2 screen and the s - pen ( which is truly a gimmick anyway ) , it has half the storage , shorter battery life , and no backlit keyboard .\n->", + "output": "{\"text\": \"while the chromebook pro does have a nice high - resolution 3 : 2 screen and the s - pen ( which is truly a gimmick anyway ) , it has half the storage , shorter battery life , and no backlit keyboard .\", \"labels\": \"[{'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MEMORY#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n->The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i will never return .\n->i will never return .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: this is among the best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is among the best .\n->", + "output": "{\"text\": \"this is among the best .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * * returning the device - - that unhappy with the item .\n->* * returning the device - - that unhappy with the item .\n[{'aspect': 'device', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'item', 'opinion': 'unhappy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The atmosphere is unheralded , the service impeccable , and the food magnificant .\n->The atmosphere is unheralded , the service impeccable , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s much smoother with web pages and android apps , and the touch screen is more responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s much smoother with web pages and android apps , and the touch screen is more responsive .\n->", + "output": "{\"text\": \"it ' s much smoother with web pages and android apps , and the touch screen is more responsive .\", \"labels\": \"[{'aspect': 'web pages', 'opinion': 'smoother', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'smoother', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is light - weight , durable and beautiful .\n->this chromebook is light - weight , durable and beautiful .\n[{'aspect': 'chromebook', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: after about 60 days use the power adapter / charger stopped working .\n->after about 60 days use the power adapter / charger stopped working .\n[{'aspect': 'power adapter / charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: keyboard is a bit nicer , not tons .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard is a bit nicer , not tons .\n->", + "output": "{\"text\": \"keyboard is a bit nicer , not tons .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nicer', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of the best hot dogs i have ever eaten .\n->one of the best hot dogs i have ever eaten .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the waitress was very patient with us and the food is phenomenal !\n->the waitress was very patient with us and the food is phenomenal !\n[{'aspect': 'waitress', 'opinion': 'patient', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: for casual use , this will work fine for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor casual use , this will work fine for me .\n->", + "output": "{\"text\": \"for casual use , this will work fine for me .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: When we sat , we got great and fast service .\n->When we sat , we got great and fast service .\n[{'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - display glass not glued well on one side .\n->- display glass not glued well on one side .\n[{'aspect': 'display glass', 'opinion': 'not glued well', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: the default scaling 1536x864 looks excellent and sharp to me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe default scaling 1536x864 looks excellent and sharp to me .\n->", + "output": "{\"text\": \"the default scaling 1536x864 looks excellent and sharp to me .\", \"labels\": \"[{'aspect': 'default scaling', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'default scaling', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and the price is excellent for what you get .\n->and the price is excellent for what you get .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the issue is that i got a faulty laptop and that ' s why the negative review .\n->the issue is that i got a faulty laptop and that ' s why the negative review .\n[{'aspect': 'laptop', 'opinion': 'faulty', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'negative', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: there ' s nothing i do n ' t like about this device , so in order of importance , here ' s what i love about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere ' s nothing i do n ' t like about this device , so in order of importance , here ' s what i love about it .\n->", + "output": "{\"text\": \"there ' s nothing i do n ' t like about this device , so in order of importance , here ' s what i love about it .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: of course , it is crowded but who cares .\n->of course , it is crowded but who cares .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: this is a light - use business laptop that we ' ve had for a month .\n->this is a light - use business laptop that we ' ve had for a month .\n[{'aspect': 'laptop', 'opinion': 'light - use', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: i ' m typing this review in a dark room , and it reminds me how much i love this backlit keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m typing this review in a dark room , and it reminds me how much i love this backlit keyboard .\n->", + "output": "{\"text\": \"i ' m typing this review in a dark room , and it reminds me how much i love this backlit keyboard .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i thought i had died and gone to heaven .\n->i thought i had died and gone to heaven .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: terrible waste of money . . scammers\n->terrible waste of money . . scammers\n[{'aspect': 'NULL', 'opinion': 'scammers', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\ntext: i love how quick this thing is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love how quick this thing is .\n->", + "output": "{\"text\": \"i love how quick this thing is .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s very easy to set up and use\n->it ' s very easy to set up and use\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: the keyboard and os takes some getting used to .\n->the keyboard and os takes some getting used to .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#USABILITY'}]\ntext: this thing is n ' t that fast , but for loading web pages , it ' s pretty close .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis thing is n ' t that fast , but for loading web pages , it ' s pretty close .\n->", + "output": "{\"text\": \"this thing is n ' t that fast , but for loading web pages , it ' s pretty close .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service is top notch .\n->Service is top notch .\n[{'aspect': 'Service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: I love and I know gourmet food by excellence !\n->I love and I know gourmet food by excellence !\n[{'aspect': 'gourmet food', 'opinion': 'love', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'gourmet food', 'opinion': 'excellence', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the brightness and clarity are awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe brightness and clarity are awesome .\n->", + "output": "{\"text\": \"the brightness and clarity are awesome .\", \"labels\": \"[{'aspect': 'brightness', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'clarity', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this looks too big , it ' s just 1 day of use so far this is my review .\n->this looks too big , it ' s just 1 day of use so far this is my review .\n[{'aspect': 'NULL', 'opinion': 'big', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: upon further inspection , i had noticed that one of the side speakers was pushed in and the plastic surrounding it had a crack .\n->upon further inspection , i had noticed that one of the side speakers was pushed in and the plastic surrounding it had a crack .\n[{'aspect': 'side speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\ntext: this is quality hardware , and it shows .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is quality hardware , and it shows .\n->", + "output": "{\"text\": \"this is quality hardware , and it shows .\", \"labels\": \"[{'aspect': 'hardware', 'opinion': 'quality', 'polarity': 'positive', 'category': 'HARDWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very good purchase .\n->very good purchase .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: they refuse to seat parties of 3 or more on weekends .\n->they refuse to seat parties of 3 or more on weekends .\n[{'aspect': 'NULL', 'opinion': 'refuse', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it ' s complementary , not revolutionary , which is much more intuitive and useful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s complementary , not revolutionary , which is much more intuitive and useful .\n->", + "output": "{\"text\": \"it ' s complementary , not revolutionary , which is much more intuitive and useful .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great pizza for lunch place .\n->Great pizza for lunch place .\n[{'aspect': 'pizza', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was authentic .\n->The food was authentic .\n[{'aspect': 'food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: again , this is where the build quality really shines .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nagain , this is where the build quality really shines .\n->", + "output": "{\"text\": \"again , this is where the build quality really shines .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'shines', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n->touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n[{'aspect': 'touchscreen', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'neat', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'awkward', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n->received this laptop on july 30 , 2018 and by august 5 , 2018 , not even 1 week later , it has stopped working .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the microsd slot leaves an inserted card perfectly flush .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe microsd slot leaves an inserted card perfectly flush .\n->", + "output": "{\"text\": \"the microsd slot leaves an inserted card perfectly flush .\", \"labels\": \"[{'aspect': 'microsd slot', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the tuna and wasabe potatoes are excellent .\n->the tuna and wasabe potatoes are excellent .\n[{'aspect': 'tuna', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wasabe potatoes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n->a gentleman , maybe the manager , came to our table , and without so much as a smile or greeting asked for our order .\n[{'aspect': 'gentleman', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: this flip 2 really is a fantastic chromebook , and the ability to run android apps only makes it better !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis flip 2 really is a fantastic chromebook , and the ability to run android apps only makes it better !\n->", + "output": "{\"text\": \"this flip 2 really is a fantastic chromebook , and the ability to run android apps only makes it better !\", \"labels\": \"[{'aspect': 'flip 2', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'android apps', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after really enjoying ourselves at the bar we sat down at a table and had dinner .\n->after really enjoying ourselves at the bar we sat down at a table and had dinner .\n[{'aspect': 'bar', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n->this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i absolutely love this beyond any computer / tablet device i have purchased in the last 10 years , trust me , i ' ve bought a lot of them along the way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely love this beyond any computer / tablet device i have purchased in the last 10 years , trust me , i ' ve bought a lot of them along the way .\n->", + "output": "{\"text\": \"i absolutely love this beyond any computer / tablet device i have purchased in the last 10 years , trust me , i ' ve bought a lot of them along the way .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet device', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is very good , but not outstanding .\n->the food is very good , but not outstanding .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'not outstanding', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: I just wonder how you can have such a delicious meal for such little money .\n->I just wonder how you can have such a delicious meal for such little money .\n[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'money', 'opinion': 'little', 'polarity': 'positive', 'category': 'NULL'}]\ntext: with the power of the internet and all the online productivity products the simplicity of this is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwith the power of the internet and all the online productivity products the simplicity of this is fantastic .\n->", + "output": "{\"text\": \"with the power of the internet and all the online productivity products the simplicity of this is fantastic .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is junk .\n->this is junk .\n[{'aspect': 'NULL', 'opinion': 'junk', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it is perfect for his college courses , work and fun .\n->it is perfect for his college courses , work and fun .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: the addition of the play store puts the device into a sweet spot no other device can come close to matching .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe addition of the play store puts the device into a sweet spot no other device can come close to matching .\n->", + "output": "{\"text\": \"the addition of the play store puts the device into a sweet spot no other device can come close to matching .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We concluded with tiramisu chocolate cake , both were delicious .\n->We concluded with tiramisu chocolate cake , both were delicious .\n[{'aspect': 'tiramisu chocolate cake', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we were less than impressed .\n->we were less than impressed .\n[{'aspect': 'NULL', 'opinion': 'less than impressed', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: this asus chromebook fits the bill .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis asus chromebook fits the bill .\n->", + "output": "{\"text\": \"this asus chromebook fits the bill .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n->it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n[{'aspect': 'NULL', 'opinion': 'not really bad', 'polarity': 'neutral', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: best meal in a long time !\n->best meal in a long time !\n[{'aspect': 'meal', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: can ' t say enough about my love of this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncan ' t say enough about my love of this chromebook .\n->", + "output": "{\"text\": \"can ' t say enough about my love of this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: best dining experience in the west village !\n->best dining experience in the west village !\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: I have never before eaten 40 pieces of relatively good nigiri .\n->I have never before eaten 40 pieces of relatively good nigiri .\n[{'aspect': 'nigiri', 'opinion': 'good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: then the asus flip c302 came into my life :\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthen the asus flip c302 came into my life :\n->", + "output": "{\"text\": \"then the asus flip c302 came into my life :\", \"labels\": \"[{'aspect': 'asus flip c302', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazon warehouse marked this as ` ` like new ` ` but upon receiving , this unit was brand new .\n->amazon warehouse marked this as ` ` like new ` ` but upon receiving , this unit was brand new .\n[{'aspect': 'this unit', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n->Chow fun was dry ; pork shu mai was more than usually greasy and had to share a table with loud and rude family .\n[{'aspect': 'Chow fun', 'opinion': 'dry', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'pork shu mai', 'opinion': 'greasy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: speaking of charges , it ' s so nice to be able to use usb c .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeaking of charges , it ' s so nice to be able to use usb c .\n->", + "output": "{\"text\": \"speaking of charges , it ' s so nice to be able to use usb c .\", \"labels\": \"[{'aspect': 'usb c', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was prompt , friendly and great .\n->Service was prompt , friendly and great .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i started out with a bombay beer which was big enough for two .\n->i started out with a bombay beer which was big enough for two .\n[{'aspect': 'bombay beer', 'opinion': 'big', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: our phones are usb c so one cable does everything for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nour phones are usb c so one cable does everything for me .\n->", + "output": "{\"text\": \"our phones are usb c so one cable does everything for me .\", \"labels\": \"[{'aspect': 'usb c', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the broken mic was not a dealbreaker but annoying in a brand new device .\n->the broken mic was not a dealbreaker but annoying in a brand new device .\n[{'aspect': 'mic', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: Try ordering from the regular menu , then you would not regret !\n->Try ordering from the regular menu , then you would not regret !\n[{'aspect': 'menu', 'opinion': 'regret', 'polarity': 'positive', 'category': 'NULL'}]\ntext: * keyboard * - the keyboard is alright .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* keyboard * - the keyboard is alright .\n->", + "output": "{\"text\": \"* keyboard * - the keyboard is alright .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'alright', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try green curry with vegetables .\n->Try green curry with vegetables .\n[{'aspect': 'green curry with vegetables', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n->you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: * track pad * - the trackpad is well done .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* track pad * - the trackpad is well done .\n->", + "output": "{\"text\": \"* track pad * - the trackpad is well done .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s lightweight , has a long battery life and although it ' s smaller than a standard laptop , the keyboard is easy to use .\n->it ' s lightweight , has a long battery life and although it ' s smaller than a standard laptop , the keyboard is easy to use .\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: The octopus eaters were floored by the Octopus salad .\n->The octopus eaters were floored by the Octopus salad .\n[{'aspect': 'Octopus salad', 'opinion': 'floored', 'polarity': 'positive', 'category': 'NULL'}]\ntext: 80 % of the apps it ' s a poor experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n80 % of the apps it ' s a poor experience .\n->", + "output": "{\"text\": \"80 % of the apps it ' s a poor experience .\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SOFTWARE#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as of today , they can not give me a date on the return of my chromebook .\n->as of today , they can not give me a date on the return of my chromebook .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\nExample:\ntext: awsome pizza especially the margheritta slice .\n->awsome pizza especially the margheritta slice .\n[{'aspect': 'pizza', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'margheritta slice', 'opinion': 'awsome', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the asus c302 is the best chromebook you can buy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe asus c302 is the best chromebook you can buy .\n->", + "output": "{\"text\": \"the asus c302 is the best chromebook you can buy .\", \"labels\": \"[{'aspect': 'asus c302', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen does not wobble as there is a good amount of friction holding it where you put it .\n->the screen does not wobble as there is a good amount of friction holding it where you put it .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: on the way out , we heard of other guests complaining about similar issues .\n->on the way out , we heard of other guests complaining about similar issues .\n[{'aspect': 'NULL', 'opinion': 'complaining', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the touch screen is super responsive and the keyboard is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touch screen is super responsive and the keyboard is excellent .\n->", + "output": "{\"text\": \"the touch screen is super responsive and the keyboard is excellent .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is a very good laptop .\n->it is a very good laptop .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this was , from start to finish , a mind - bogglingly uncomfortable experience .\n->this was , from start to finish , a mind - bogglingly uncomfortable experience .\n[{'aspect': 'NULL', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: it runs most of those apps and games beautifully and when combined with a ` ` logitech gamepad f310 ` ` you can play the games with a game controller !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit runs most of those apps and games beautifully and when combined with a ` ` logitech gamepad f310 ` ` you can play the games with a game controller !\n->", + "output": "{\"text\": \"it runs most of those apps and games beautifully and when combined with a ` ` logitech gamepad f310 ` ` you can play the games with a game controller !\", \"labels\": \"[{'aspect': 'apps', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this by far one of the best laptops i ' ve ever purchased .\n->this by far one of the best laptops i ' ve ever purchased .\n[{'aspect': 'laptops', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this one is pretty , but obviously not sturdy .\n->this one is pretty , but obviously not sturdy .\n[{'aspect': 'NULL', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not sturdy', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall this is a home run purchase and i can ' t say enough good things about the asus c302 .\n->", + "output": "{\"text\": \"overall this is a home run purchase and i can ' t say enough good things about the asus c302 .\", \"labels\": \"[{'aspect': 'asus c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food here is rather good , but only if you like to wait for it .\n->The food here is rather good , but only if you like to wait for it .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this laptop is absolutely everything i imagined .\n->this laptop is absolutely everything i imagined .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i mostly use it for playing a couple of games , surfing the web , managing my calendar / tasks lists , reading e - books , that sort of thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni mostly use it for playing a couple of games , surfing the web , managing my calendar / tasks lists , reading e - books , that sort of thing .\n->", + "output": "{\"text\": \"i mostly use it for playing a couple of games , surfing the web , managing my calendar / tasks lists , reading e - books , that sort of thing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n->as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n[{'aspect': 'chromebook device', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this is the best shabu - shabu restaurant in the try - state area .\n->this is the best shabu - shabu restaurant in the try - state area .\n[{'aspect': 'shabu - shabu restaurant', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the keyboard is small , a little weird and takes some getting used to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is small , a little weird and takes some getting used to .\n->", + "output": "{\"text\": \"the keyboard is small , a little weird and takes some getting used to .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'small', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'weird', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it starts up right away and has decent battery life that looks nice .\n->it starts up right away and has decent battery life that looks nice .\n[{'aspect': 'starts up', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'decent', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'nice', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n->i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n[{'aspect': 'pizza', 'opinion': 'ashamed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the keyboard is excellent ( and backlit ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is excellent ( and backlit ) .\n->", + "output": "{\"text\": \"the keyboard is excellent ( and backlit ) .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place blew me away . . . by far my new favorite restaurant on the uppereast side .\n->this place blew me away . . . by far my new favorite restaurant on the uppereast side .\n[{'aspect': 'place', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The server was really cool and served us our food and drinks with a smile .\n->The server was really cool and served us our food and drinks with a smile .\n[{'aspect': 'server', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the track pad is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe track pad is excellent .\n->", + "output": "{\"text\": \"the track pad is excellent .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you ' re on the fence i recommend this asus .\n->if you ' re on the fence i recommend this asus .\n[{'aspect': 'asus', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great food , great decor , great service .\n->great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the screen is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is excellent .\n->", + "output": "{\"text\": \"the screen is excellent .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The fried rice is really good too .\n->The fried rice is really good too .\n[{'aspect': 'fried rice', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: excellent computer , better than expected .\n->excellent computer , better than expected .\n[{'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this laptop performs in every way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop performs in every way .\n->", + "output": "{\"text\": \"this laptop performs in every way .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The hot and sour soup was unbearably hot and tasted of only pepper and nothing else .\n->The hot and sour soup was unbearably hot and tasted of only pepper and nothing else .\n[{'aspect': 'soup', 'opinion': 'unbearably hot', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Ambience is delightful , service impeccable .\n->Ambience is delightful , service impeccable .\n[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s light , but also has a good weight to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s light , but also has a good weight to it .\n->", + "output": "{\"text\": \"it ' s light , but also has a good weight to it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my first mac and i ' m in love .\n->my first mac and i ' m in love .\n[{'aspect': 'mac', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i love this computer .\n->i love this computer .\n[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: - the touchscreen is very responsive , fast , and so far everything has scaled just fine during use\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the touchscreen is very responsive , fast , and so far everything has scaled just fine during use\n->", + "output": "{\"text\": \"- the touchscreen is very responsive , fast , and so far everything has scaled just fine during use\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'fine', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent choice in processor .\n->excellent choice in processor .\n[{'aspect': 'processor', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\nExample:\ntext: the entree was also very good .\n->the entree was also very good .\n[{'aspect': 'entree', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: - the hinges are just perfect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the hinges are just perfect .\n->", + "output": "{\"text\": \"- the hinges are just perfect .\", \"labels\": \"[{'aspect': 'hinges', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little guy fits the bill perfectly .\n->this little guy fits the bill perfectly .\n[{'aspect': 'guy', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n->touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n[{'aspect': 'touchscreen', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'neat', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'awkward', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: - the speakers are to the sides and not underneath so the sound isn ' t muffled when it ' s resting on something other than a flat surface\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the speakers are to the sides and not underneath so the sound isn ' t muffled when it ' s resting on something other than a flat surface\n->", + "output": "{\"text\": \"- the speakers are to the sides and not underneath so the sound isn ' t muffled when it ' s resting on something other than a flat surface\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n->The tables are crammed way too close , the menu is typical of any Italian restaurant , and the wine list is simply overpriced .\n[{'aspect': 'tables', 'opinion': 'crammed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'too close', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'typical', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: this is a nice perk to save battery life and for folks who like to read / / browse late at night in bed and do n ' t want the backlighting on .\n->this is a nice perk to save battery life and for folks who like to read / / browse late at night in bed and do n ' t want the backlighting on .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: - this chromebook has access to the android beta channel for android apps\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- this chromebook has access to the android beta channel for android apps\n->", + "output": "{\"text\": \"- this chromebook has access to the android beta channel for android apps\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The best pad thai i 've ever had .\n->The best pad thai i 've ever had .\n[{'aspect': 'pad thai', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my only issue is that the fan is very noisy and gets stuck at times , causing worry about the laptop getting overheated .\n->my only issue is that the fan is very noisy and gets stuck at times , causing worry about the laptop getting overheated .\n[{'aspect': 'fan', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\ntext: and because of the constant usage of higher brightness , the battery does drain faster .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand because of the constant usage of higher brightness , the battery does drain faster .\n->", + "output": "{\"text\": \"and because of the constant usage of higher brightness , the battery does drain faster .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'faster', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: need to play with it a bit more .\n->need to play with it a bit more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: keyboard feels firm and no flex , screen is nice for the price range .\n->keyboard feels firm and no flex , screen is nice for the price range .\n[{'aspect': 'keyboard', 'opinion': 'firm', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\ntext: they both pick up oils and such pretty easily .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey both pick up oils and such pretty easily .\n->", + "output": "{\"text\": \"they both pick up oils and such pretty easily .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their exotic salad is basic ly a delicious little green salad with a peanut sauce that is perfect before their sweet basil fried tofu .\n->Their exotic salad is basic ly a delicious little green salad with a peanut sauce that is perfect before their sweet basil fried tofu .\n[{'aspect': 'exotic salad', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'green salad', 'opinion': 'delicious little', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'peanut sauce', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it runs android apps with power and quality .\n->it runs android apps with power and quality .\n[{'aspect': 'android apps', 'opinion': 'with power and quality', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: as for the asus , it is a wonderful laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas for the asus , it is a wonderful laptop .\n->", + "output": "{\"text\": \"as for the asus , it is a wonderful laptop .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ve only utilized the 360 degree opening once and so far i like it .\n->i ' ve only utilized the 360 degree opening once and so far i like it .\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: only thing is i am not sure if there is a delete key , something i use a lot\n->only thing is i am not sure if there is a delete key , something i use a lot\n[{'aspect': 'delete key', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i have had no issues with the track pad ; it works fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had no issues with the track pad ; it works fine .\n->", + "output": "{\"text\": \"i have had no issues with the track pad ; it works fine .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'fine', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was attentive .\n->The service was attentive .\n[{'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food is decent .\n->the food is decent .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: i have not fully tested battery life but it seems to last about as long as advertised .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have not fully tested battery life but it seems to last about as long as advertised .\n->", + "output": "{\"text\": \"i have not fully tested battery life but it seems to last about as long as advertised .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first one i received , the space bar got stuck and returned it for a replacement .\n->first one i received , the space bar got stuck and returned it for a replacement .\n[{'aspect': 'space bar', 'opinion': 'stuck', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: The prices were fantastic .\n->The prices were fantastic .\n[{'aspect': 'prices', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: one , the display is gorgeous ; watching video is a treat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none , the display is gorgeous ; watching video is a treat .\n->", + "output": "{\"text\": \"one , the display is gorgeous ; watching video is a treat .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'treat', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n->to make things worse , i currently live overseas and am highly doubtful that samsung would do anything about this , given that it was bought in the us .\n[{'aspect': 'samsung', 'opinion': 'doubtful', 'polarity': 'negative', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\nExample:\ntext: everything about the experience has been terrible .\n->everything about the experience has been terrible .\n[{'aspect': 'NULL', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: however , there is screen glare , even in a normally lit room , not just a brightly lit room .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , there is screen glare , even in a normally lit room , not just a brightly lit room .\n->", + "output": "{\"text\": \"however , there is screen glare , even in a normally lit room , not just a brightly lit room .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The service was excellent and the food was delicious .\n->The service was excellent and the food was delicious .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the current model only has two , which can be problematic if you hook up an external hard drive and an external bluray drive ( which requires two ports ) at the same time .\n->the current model only has two , which can be problematic if you hook up an external hard drive and an external bluray drive ( which requires two ports ) at the same time .\n[{'aspect': 'model', 'opinion': 'problematic', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i think that the bezels are a smidge thick , but that actually makes perfect sense .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think that the bezels are a smidge thick , but that actually makes perfect sense .\n->", + "output": "{\"text\": \"i think that the bezels are a smidge thick , but that actually makes perfect sense .\", \"labels\": \"[{'aspect': 'bezels', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a nice place to relax and have conversation .\n->it ' s a nice place to relax and have conversation .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n->The crackling calamari salad , which is usually a cheap disaster at many restaurants , is crispy and lightly dressed .\n[{'aspect': 'crackling calamari salad', 'opinion': 'crispy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crackling calamari salad', 'opinion': 'lightly dressed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s a real treat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s a real treat .\n->", + "output": "{\"text\": \"it ' s a real treat .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'treat', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great Indian food and the service is incredible .\n->Great Indian food and the service is incredible .\n[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this computer functions very well as a gaming laptop for the price .\n->this computer functions very well as a gaming laptop for the price .\n[{'aspect': 'computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'gaming laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'computer', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'gaming laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: my use experience has been riddled with lag and jitteriness , although using the touchscreen for a moment sorts everything out .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy use experience has been riddled with lag and jitteriness , although using the touchscreen for a moment sorts everything out .\n->", + "output": "{\"text\": \"my use experience has been riddled with lag and jitteriness , although using the touchscreen for a moment sorts everything out .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is decent .\n->the food is decent .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n->The other night we had the $ 30 three course meal and everything was delicious - if I could of licked the plate clean I would of .\n[{'aspect': 'three course meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the speakers get plenty loud , but they start to sound pretty bad when turned up a little past half volume .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speakers get plenty loud , but they start to sound pretty bad when turned up a little past half volume .\n->", + "output": "{\"text\": \"the speakers get plenty loud , but they start to sound pretty bad when turned up a little past half volume .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'loud', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we had a very nice time .\n->we had a very nice time .\n[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i will painfully learn the new pads .\n->i will painfully learn the new pads .\n[{'aspect': 'pads', 'opinion': 'painfully', 'polarity': 'negative', 'category': 'HARDWARE#USABILITY'}]\ntext: the core m3 allows this system to get fast and to stay quiet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe core m3 allows this system to get fast and to stay quiet .\n->", + "output": "{\"text\": \"the core m3 allows this system to get fast and to stay quiet .\", \"labels\": \"[{'aspect': 'core m3', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'core m3', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard has a nice quiet touch .\n->the keyboard has a nice quiet touch .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: we liked it so much , that we will always make it a point to dine here when we visit new york .\n->we liked it so much , that we will always make it a point to dine here when we visit new york .\n[{'aspect': 'NULL', 'opinion': 'liked', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: sometimes , under heavy loads , the bottom of the c302 gets warm to the touch , but not to the point of pain .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsometimes , under heavy loads , the bottom of the c302 gets warm to the touch , but not to the point of pain .\n->", + "output": "{\"text\": \"sometimes , under heavy loads , the bottom of the c302 gets warm to the touch , but not to the point of pain .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FANS&COOLING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this thing is slow !\n->this thing is slow !\n[{'aspect': 'NULL', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the only real complain is the same one everyone else has with this model and that is the battery life could be better .\n->the only real complain is the same one everyone else has with this model and that is the battery life could be better .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\n->", + "output": "{\"text\": \"all the while , it provides one of the fastest web browsing experiences that i ' ve ever experienced .\", \"labels\": \"[{'aspect': 'web browsing', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we will definitely go back .\n->we will definitely go back .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n->Great friendly service , Fast seating , Fast Delivery , Excellent sushi .\n[{'aspect': 'service', 'opinion': 'Great friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seating', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Delivery', 'opinion': 'Fast', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it will take years of bad programming to make this chromebook as slow as my last one got .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit will take years of bad programming to make this chromebook as slow as my last one got .\n->", + "output": "{\"text\": \"it will take years of bad programming to make this chromebook as slow as my last one got .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'bad', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all in all , i would return - as it was a beautiful restaurant - but i hope the staff pays more attention to the little details in the future .\n->all in all , i would return - as it was a beautiful restaurant - but i hope the staff pays more attention to the little details in the future .\n[{'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the food was bland oily .\n->the food was bland oily .\n[{'aspect': 'food', 'opinion': 'bland oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: 2 ) the touchpad is way too wonky - asus needs to fix this asap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n2 ) the touchpad is way too wonky - asus needs to fix this asap .\n->", + "output": "{\"text\": \"2 ) the touchpad is way too wonky - asus needs to fix this asap .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'wonky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food , great decor , great service .\n->Great food , great decor , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i will probably be sending it back as it seems too complicated .\n->i will probably be sending it back as it seems too complicated .\n[{'aspect': 'NULL', 'opinion': 'complicated', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: having had it for just over a month , i have to say i am thoroughly impressed by its versatility and how stable the os is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhaving had it for just over a month , i have to say i am thoroughly impressed by its versatility and how stable the os is .\n->", + "output": "{\"text\": \"having had it for just over a month , i have to say i am thoroughly impressed by its versatility and how stable the os is .\", \"labels\": \"[{'aspect': 'os', 'opinion': 'versatility', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'stable', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n->For the next hour and a half we stood in the crowded lobby area of this touristy restaurant listening to all types of explanations of why we were not being seated .\n[{'aspect': 'lobby area', 'opinion': 'crowded', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the ssd is not work after 4 month\n->the ssd is not work after 4 month\n[{'aspect': 'ssd', 'opinion': 'not work', 'polarity': 'negative', 'category': 'HARD_DISC#QUALITY'}]\ntext: on a side note , there is a slight defect on my chromebook as there is some creaking and loose feeling when pressing on the bottom left side of my screen , this can be especially annoying in tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \non a side note , there is a slight defect on my chromebook as there is some creaking and loose feeling when pressing on the bottom left side of my screen , this can be especially annoying in tablet mode .\n->", + "output": "{\"text\": \"on a side note , there is a slight defect on my chromebook as there is some creaking and loose feeling when pressing on the bottom left side of my screen , this can be especially annoying in tablet mode .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'defect', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: they have a huge selection of different cream cheeses and all of their salads are great .\n->they have a huge selection of different cream cheeses and all of their salads are great .\n[{'aspect': 'salads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'cream cheeses', 'opinion': 'huge', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'cream cheeses', 'opinion': 'different', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: the click is now provided by a haptic engine and provides more functionality .\n->the click is now provided by a haptic engine and provides more functionality .\n[{'aspect': 'click', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\ntext: boot up is of course , almost instant .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nboot up is of course , almost instant .\n->", + "output": "{\"text\": \"boot up is of course , almost instant .\", \"labels\": \"[{'aspect': 'boot up', 'opinion': 'instant', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this chromebook is great system that is lightweight , has excellent battery life , and offers a fantastic keyboard .\n->this chromebook is great system that is lightweight , has excellent battery life , and offers a fantastic keyboard .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'system', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'system', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n->i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n[{'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: - excellent keyboard in all aspects - feel , rigidity , and backlight\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- excellent keyboard in all aspects - feel , rigidity , and backlight\n->", + "output": "{\"text\": \"- excellent keyboard in all aspects - feel , rigidity , and backlight\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .\n->the appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .\n[{'aspect': 'fried oysters and clams', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fried oysters and clams', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: you will not be disapointed at all .\n->you will not be disapointed at all .\n[{'aspect': 'NULL', 'opinion': 'not be disapointed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: - excellent build quality - aluminium case is solid and has a premium feel\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- excellent build quality - aluminium case is solid and has a premium feel\n->", + "output": "{\"text\": \"- excellent build quality - aluminium case is solid and has a premium feel\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminium case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminium case', 'opinion': 'premium', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would never wait for a table to eat , it just is not THAT great .\n->I would never wait for a table to eat , it just is not THAT great .\n[{'aspect': 'table', 'opinion': 'never wait', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: I ate here a week ago and found most dishes average at best and too expensive .\n->I ate here a week ago and found most dishes average at best and too expensive .\n[{'aspect': 'dishes', 'opinion': 'average', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'too expensive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - perfect balance of speed and battery life\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- perfect balance of speed and battery life\n->", + "output": "{\"text\": \"- perfect balance of speed and battery life\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: really fast also lenovo ' s customer service excellent .\n->really fast also lenovo ' s customer service excellent .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: service was also very good .\n->service was also very good .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: - my only real gripe is that i wish it was .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- my only real gripe is that i wish it was .\n->", + "output": "{\"text\": \"- my only real gripe is that i wish it was .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'gripe', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I 've been to at Cafe Spice probably 5-8 times , it is probably still the best Indian restaurant around Union Square .\n->I 've been to at Cafe Spice probably 5-8 times , it is probably still the best Indian restaurant around Union Square .\n[{'aspect': 'Cafe Spice', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: service was also horrible and the ambience is not that great .\n->service was also horrible and the ambience is not that great .\n[{'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: other than my hope that it would be light enough to use as a tablet all the time , this is the chromebook i ' ve been wanting for a long time now .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother than my hope that it would be light enough to use as a tablet all the time , this is the chromebook i ' ve been wanting for a long time now .\n->", + "output": "{\"text\": \"other than my hope that it would be light enough to use as a tablet all the time , this is the chromebook i ' ve been wanting for a long time now .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then it just rebooted without prompt .\n->then it just rebooted without prompt .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Save your money and do n't waste your calories , go to Margharita 's on Washington Street instead , they have amazing food and the BEST service .\n->Save your money and do n't waste your calories , go to Margharita 's on Washington Street instead , they have amazing food and the BEST service .\n[{'aspect': 'food', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'BEST', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s extremely useful as a laptop as well as , most of the time at least , a tablet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s extremely useful as a laptop as well as , most of the time at least , a tablet .\n->", + "output": "{\"text\": \"it ' s extremely useful as a laptop as well as , most of the time at least , a tablet .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'tablet', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is excellent , and i can get several days use before needing to plug in .\n->battery life is excellent , and i can get several days use before needing to plug in .\n[{'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: but seemed very poorly made for the money .\n->but seemed very poorly made for the money .\n[{'aspect': 'NULL', 'opinion': 'poorly', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\ntext: note : i bought a refurbished / ` ` damaged ` ` model from amazon and it ' s in absolutely flawless condition .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnote : i bought a refurbished / ` ` damaged ` ` model from amazon and it ' s in absolutely flawless condition .\n->", + "output": "{\"text\": \"note : i bought a refurbished / ` ` damaged ` ` model from amazon and it ' s in absolutely flawless condition .\", \"labels\": \"[{'aspect': 'model', 'opinion': 'flawless', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: which lets face it . . . . at times it ' s a good thing .\n->which lets face it . . . . at times it ' s a good thing .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n->even the touchpad and keyboard perform as well as the very best chromebooks currently available .\n[{'aspect': 'touchpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: pros : lightweight , fast , portable , great battery life ( 12 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npros : lightweight , fast , portable , great battery life ( 12 .\n->", + "output": "{\"text\": \"pros : lightweight , fast , portable , great battery life ( 12 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n->I would highly recommend Nina 's to anyone who wants to have a romantic dinner in a heart warming surrounding filled with candles and family pictures .\n[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'surrounding', 'opinion': 'heart warming', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Love Pizza 33 ...\n->Love Pizza 33 ...\n[{'aspect': 'Pizza 33', 'opinion': 'Love', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\n->", + "output": "{\"text\": \"the keyboard and mouse on the asus were frequently rated as good but those on the pixelbook were raved about .\", \"labels\": \"[{'aspect': 'mouse', 'opinion': 'good', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n->the meat is fresh , the sauces are great , you get kimchi and a salad free with your meal and service is good too .\n[{'aspect': 'meat', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sauces', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'kimchi', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'salad', 'opinion': 'free', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'meal', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: it ' s lightning fast and handles games like skyrim and the witcher 3 surprisingly smoothly for the price i paid .\n->it ' s lightning fast and handles games like skyrim and the witcher 3 surprisingly smoothly for the price i paid .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the side mounted speakers are clear .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe side mounted speakers are clear .\n->", + "output": "{\"text\": \"the side mounted speakers are clear .\", \"labels\": \"[{'aspect': 'side mounted speakers', 'opinion': 'clear', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n->my picks are : - Scallion Pancake ( fried with vegetable juice , very special and tasty ) - Guizhou Chicken - Shredded Squid Family Style ( one of my personal favorites ) - Sichuan Spicy Soft Shell Crab - Shuizhu Fish ( this one is for hardcore Sichuan food fans , I would n't recommend to my American friends as it 's very spicy .\n[{'aspect': 'Scallion Pancake', 'opinion': 'special', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Scallion Pancake', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shredded Squid Family Style', 'opinion': 'favorites', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Shuizhu Fish', 'opinion': 'spicy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Chennai Garden is my favorite Indian restaurant in the city .\n->Chennai Garden is my favorite Indian restaurant in the city .\n[{'aspect': 'Chennai Garden', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the battery life is not anywhere near the advertised 8 hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is not anywhere near the advertised 8 hours .\n->", + "output": "{\"text\": \"the battery life is not anywhere near the advertised 8 hours .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this tiny restaurant is as cozy as it gets , with that certain parisian flair .\n->this tiny restaurant is as cozy as it gets , with that certain parisian flair .\n[{'aspect': 'restaurant', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: all in all the food was good - a little on the expensive side , but fresh .\n->all in all the food was good - a little on the expensive side , but fresh .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the design and style is topnotch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe design and style is topnotch .\n->", + "output": "{\"text\": \"the design and style is topnotch .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'topnotch', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'style', 'opinion': 'topnotch', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Go to Volare for 1st class service and terrific food .\n->Go to Volare for 1st class service and terrific food .\n[{'aspect': 'service', 'opinion': '1st class', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The only problem is that the manager is a complete incompetent .\n->The only problem is that the manager is a complete incompetent .\n[{'aspect': 'manager', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'NULL'}]\ntext: boot speed : likewise , very fast -\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nboot speed : likewise , very fast -\n->", + "output": "{\"text\": \"boot speed : likewise , very fast -\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: other than that , the key sizes are good and the backlighting works just fine .\n->other than that , the key sizes are good and the backlighting works just fine .\n[{'aspect': 'key', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'backlighting', 'opinion': 'fine', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n->everytime the charger is on the laptop , my left wrist will always get stung when it touches the side of the chassis .\n[{'aspect': 'charger', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#QUALITY'}]\ntext: many android apps work conditionally : e .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmany android apps work conditionally : e .\n->", + "output": "{\"text\": \"many android apps work conditionally : e .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: such a good investment , its so useful and works perfectly .\n->such a good investment , its so useful and works perfectly .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n->i bought this less than 6 months ago , and now it won ' t charge unless it ' s asleep .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: battery life is phenomenal again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is phenomenal again .\n->", + "output": "{\"text\": \"battery life is phenomenal again .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent product and experience with the purchase .\n->excellent product and experience with the purchase .\n[{'aspect': 'product', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: with the great variety on the menu , i eat here often and never get bored .\n->with the great variety on the menu , i eat here often and never get bored .\n[{'aspect': 'menu', 'opinion': 'great variety', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the keyboard is fully sized and comfortable , the key travel is good with great action , backlighting is enough and adaptive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is fully sized and comfortable , the key travel is good with great action , backlighting is enough and adaptive .\n->", + "output": "{\"text\": \"the keyboard is fully sized and comfortable , the key travel is good with great action , backlighting is enough and adaptive .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'fully sized', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'key travel', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'key travel', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'backlighting', 'opinion': 'enough', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'backlighting', 'opinion': 'adaptive', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n->it ' s a different computer overall due to the way you control certain things and what not , but that hasn ' t been a big deal .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->Two complaints -- their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n[{'aspect': 'appetizer selection', 'opinion': 'complaints', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - the aluminum build is firm and solid without feeling cheap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the aluminum build is firm and solid without feeling cheap .\n->", + "output": "{\"text\": \"- the aluminum build is firm and solid without feeling cheap .\", \"labels\": \"[{'aspect': 'aluminum build', 'opinion': 'firm', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminum build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'aluminum build', 'opinion': 'without feeling cheap', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: machine periodically crashed .\n->machine periodically crashed .\n[{'aspect': 'machine', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: she had no trouble learning how to use it .\n->she had no trouble learning how to use it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: - the screen is great in all aspects .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the screen is great in all aspects .\n->", + "output": "{\"text\": \"- the screen is great in all aspects .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: internal graphics only ; not recommended for high intensity gaming or 3d modeling .\n->internal graphics only ; not recommended for high intensity gaming or 3d modeling .\n[{'aspect': 'internal graphics', 'opinion': 'not recommended', 'polarity': 'negative', 'category': 'GRAPHICS#DESIGN_FEATURES'}]\ntext: - performance is solid for chromebook use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- performance is solid for chromebook use .\n->", + "output": "{\"text\": \"- performance is solid for chromebook use .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n->For all of you new to Indian food , try the Paneer Roll , it is a piece of heaven .\n[{'aspect': 'Paneer Roll', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n->They had scrapped the bottom of the vessel in which they make the rice -RESULT - WE HAD LARGE CHUNKS OF BURNT RICE IN OUR SERVING BOWL .\n[{'aspect': 'RICE', 'opinion': 'BURNT', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - battery life is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- battery life is great .\n->", + "output": "{\"text\": \"- battery life is great .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Friendly staff that actually lets you enjoy your meal and the company you 're with .\n->Friendly staff that actually lets you enjoy your meal and the company you 're with .\n[{'aspect': 'staff', 'opinion': 'Friendly', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the backlit keyboard is very nice and only comes on in low light .\n->the backlit keyboard is very nice and only comes on in low light .\n[{'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: everything is either good or excellent in quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything is either good or excellent in quality .\n->", + "output": "{\"text\": \"everything is either good or excellent in quality .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n->the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n[{'aspect': '1tb included drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'processing power', 'opinion': 'great', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\nExample:\ntext: the appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .\n->the appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .\n[{'aspect': 'fried oysters and clams', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fried oysters and clams', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: - screen brightness is generally ` ` enough ` ` but maybe not enough for watching dark movies in bright lights .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- screen brightness is generally ` ` enough ` ` but maybe not enough for watching dark movies in bright lights .\n->", + "output": "{\"text\": \"- screen brightness is generally ` ` enough ` ` but maybe not enough for watching dark movies in bright lights .\", \"labels\": \"[{'aspect': 'screen brightness', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The view is breathtaking the service is top notch ... the ambiance is wonderful .\n->The view is breathtaking the service is top notch ... the ambiance is wonderful .\n[{'aspect': 'view', 'opinion': 'breathtaking', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambiance', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the first time i went , and was completely taken by the live jazz band and atmosphere , i ordered the lobster cobb salad .\n->the first time i went , and was completely taken by the live jazz band and atmosphere , i ordered the lobster cobb salad .\n[{'aspect': 'live jazz band', 'opinion': 'taken', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'atmosphere', 'opinion': 'taken', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: - speakers can ` ` chatter ` ` after playing youtube videos for a long period of time .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- speakers can ` ` chatter ` ` after playing youtube videos for a long period of time .\n->", + "output": "{\"text\": \"- speakers can ` ` chatter ` ` after playing youtube videos for a long period of time .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the prices were cheap compared to the quality of service and food .\n->the prices were cheap compared to the quality of service and food .\n[{'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: the display is clear .\n->the display is clear .\n[{'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: - performance can be stuttering when under heavy load .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- performance can be stuttering when under heavy load .\n->", + "output": "{\"text\": \"- performance can be stuttering when under heavy load .\", \"labels\": \"[{'aspect': 'performance', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: really fast also lenovo ' s customer service excellent .\n->really fast also lenovo ' s customer service excellent .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'customer service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: I can not imagine better Indian food in all of the city .\n->I can not imagine better Indian food in all of the city .\n[{'aspect': 'Indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: again , overall i believe this is a great laptop with an amazing keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nagain , overall i believe this is a great laptop with an amazing keyboard .\n->", + "output": "{\"text\": \"again , overall i believe this is a great laptop with an amazing keyboard .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n->Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .\n[{'aspect': 'Ow Ley Soh', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ow Ley Soh', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: blackboard works fine for me .\n->blackboard works fine for me .\n[{'aspect': 'blackboard', 'opinion': 'fine', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: - i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\n->", + "output": "{\"text\": \"- i was worried that because the chromebooks are internet based it would run slow , but so far it is very fast at processing , installing , and uploading things .\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'worried', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'slow', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebooks', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s to die for !\n->it ' s to die for !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the steak tartare is a great bet , they fix it for you at the table .\n->the steak tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'steak tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: - physically it is appealing looking .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- physically it is appealing looking .\n->", + "output": "{\"text\": \"- physically it is appealing looking .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'appealing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The white bean brushetta to start was incredible and the pasta was phenomenal .\n->The white bean brushetta to start was incredible and the pasta was phenomenal .\n[{'aspect': 'white bean brushetta', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pasta', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n->Delicious food , excellent service , and a pretty atmosphere make this a great choice for dinner and the $ 5.99 lunch buffet makes it an even better choice for lunch !\n[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch buffet', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this is smart looking , thin , light weight , and portable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is smart looking , thin , light weight , and portable .\n->", + "output": "{\"text\": \"this is smart looking , thin , light weight , and portable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'smart', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'this', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I do n't know who they think they are but they have no respect for the residents of the neighborhood ever since they opened their cabaret next door and blasts loud music till three in the morning every weekend during the summer .\n->I do n't know who they think they are but they have no respect for the residents of the neighborhood ever since they opened their cabaret next door and blasts loud music till three in the morning every weekend during the summer .\n[{'aspect': 'music', 'opinion': 'loud', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: not only does make the best pizza in NY , maybe anywhere .\n->not only does make the best pizza in NY , maybe anywhere .\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but , it was packed well and arrived with no damage whatsoever !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut , it was packed well and arrived with no damage whatsoever !\n->", + "output": "{\"text\": \"but , it was packed well and arrived with no damage whatsoever !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'SHIPPING#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you \u2019 re planning to come here , make sure that your date is someone whom you really like since you \u2019 ll be ushered to private booths where there will be no people or food watching ( choose the ones on the ground level that have glass ceilings so you may see the stars in the sky ! ) .\n->if you \u2019 re planning to come here , make sure that your date is someone whom you really like since you \u2019 ll be ushered to private booths where there will be no people or food watching ( choose the ones on the ground level that have glass ceilings so you may see the stars in the sky ! ) .\n[{'aspect': 'private booths', 'opinion': 'ushered', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'glass ceilings', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The place 's decor and hidden bathrooms made for a good laugh .\n->The place 's decor and hidden bathrooms made for a good laugh .\n[{'aspect': 'decor', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hidden bathrooms', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the one annoyance is that office 365 doesn ' t provide a seamless way to save file offline and sync up when you ' re online .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe one annoyance is that office 365 doesn ' t provide a seamless way to save file offline and sync up when you ' re online .\n->", + "output": "{\"text\": \"the one annoyance is that office 365 doesn ' t provide a seamless way to save file offline and sync up when you ' re online .\", \"labels\": \"[{'aspect': 'office 365', 'opinion': 'annoyance', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this device would be a good choice if it weren ' t so poorly constructed .\n->this device would be a good choice if it weren ' t so poorly constructed .\n[{'aspect': 'device', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'poorly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i highly recommend to anyone to give this place a try .\n->i highly recommend to anyone to give this place a try .\n[{'aspect': 'place', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: - the keyboard is great and has a nice gentle / quiet click to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the keyboard is great and has a nice gentle / quiet click to it .\n->", + "output": "{\"text\": \"- the keyboard is great and has a nice gentle / quiet click to it .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard feels good .\n->keyboard feels good .\n[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: i ended up returning it even after getting a credit because the wireless did not work well and was extremely slow .\n->i ended up returning it even after getting a credit because the wireless did not work well and was extremely slow .\n[{'aspect': 'wireless', 'opinion': 'not work well', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}, {'aspect': 'wireless', 'opinion': 'slow', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: - the keys themselves don ' t travel that much which i prefer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the keys themselves don ' t travel that much which i prefer .\n->", + "output": "{\"text\": \"- the keys themselves don ' t travel that much which i prefer .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'prefer', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am reluctant to write because i would not want my jem of a pizza place to become overcrowded .\n->i am reluctant to write because i would not want my jem of a pizza place to become overcrowded .\n[{'aspect': 'pizza place', 'opinion': 'overcrowded', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: i see absolutely no lag on videos or streaming content .\n->i see absolutely no lag on videos or streaming content .\n[{'aspect': 'videos', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'streaming content', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: - the trackpad , while a little on the small size , has a nice glass smooth finish and it works reliable well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the trackpad , while a little on the small size , has a nice glass smooth finish and it works reliable well .\n->", + "output": "{\"text\": \"- the trackpad , while a little on the small size , has a nice glass smooth finish and it works reliable well .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'trackpad', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'trackpad', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great chromebook .\n->great chromebook .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n->The staff has always been friendly without seeming grating , and the chef has greeted us on a couple of occasions .\n[{'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - the touchscreen works great and is very responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the touchscreen works great and is very responsive .\n->", + "output": "{\"text\": \"- the touchscreen works great and is very responsive .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n->very good build quality , except for the keyboard apparently , sound was very big , very fast boot times and so much more .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'sound', 'opinion': 'big', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'boot times', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The highlight of the night was the mayonaisse for my side of fries I received from one of the food runners , which is not good considering the bill was nearly $ 100 .\n->The highlight of the night was the mayonaisse for my side of fries I received from one of the food runners , which is not good considering the bill was nearly $ 100 .\n[{'aspect': 'mayonaisse', 'opinion': 'highlight', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food runners', 'opinion': 'not good', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: - the build quality is great but not necessarily impressive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- the build quality is great but not necessarily impressive .\n->", + "output": "{\"text\": \"- the build quality is great but not necessarily impressive .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'build quality', 'opinion': 'not necessarily impressive', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is obvious that no one in the restaurant has any idea about or experience with japanese cuisine .\n->it is obvious that no one in the restaurant has any idea about or experience with japanese cuisine .\n[{'aspect': 'japanese cuisine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: He not only makes his own homemade mozzarella , but every pie is ultra fresh .\n->He not only makes his own homemade mozzarella , but every pie is ultra fresh .\n[{'aspect': 'mozzarella', 'opinion': 'homemade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pie', 'opinion': 'ultra fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: tablet works just fine though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntablet works just fine though .\n->", + "output": "{\"text\": \"tablet works just fine though .\", \"labels\": \"[{'aspect': 'tablet', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: maitre - d - ' ' eat and get out ' '\n->maitre - d - ' ' eat and get out ' '\n[{'aspect': 'maitre - d', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The atmosphere is unheralded , the service impeccable , and the food magnificant .\n->The atmosphere is unheralded , the service impeccable , and the food magnificant .\n[{'aspect': 'atmosphere', 'opinion': 'unheralded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'magnificant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: build quality and design are top notch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuild quality and design are top notch .\n->", + "output": "{\"text\": \"build quality and design are top notch .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'design', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you want good authentic thai this place is not the place to go .\n->if you want good authentic thai this place is not the place to go .\n[{'aspect': 'thai', 'opinion': 'good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'thai', 'opinion': 'authentic', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the metal case is really well built , and the fit and finish are virtually perfect .\n->the metal case is really well built , and the fit and finish are virtually perfect .\n[{'aspect': 'metal case', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'fit', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'finish', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: i absolutely love this chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely love this chromebook !\n->", + "output": "{\"text\": \"i absolutely love this chromebook !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: monitor went out 35 days after receiving .\n->monitor went out 35 days after receiving .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: Great food , great prices , great service .\n->Great food , great prices , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so , i originally purchased this for the travel conveniences .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso , i originally purchased this for the travel conveniences .\n->", + "output": "{\"text\": \"so , i originally purchased this for the travel conveniences .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - took literally 5 + hours for windows update / setup\n->- took literally 5 + hours for windows update / setup\n[{'aspect': 'windows', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the potato balls were not dry at all . . . in fact it was buttery .\n->the potato balls were not dry at all . . . in fact it was buttery .\n[{'aspect': 'potato balls', 'opinion': 'not dry', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'potato balls', 'opinion': 'buttery', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmost of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\n->", + "output": "{\"text\": \"most of my love for this laptop is based on the operating system but since you get that with every chromebook , i ' ll tell you what i like and dislike about the physical laptop only .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n->the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n[{'aspect': 'service', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Even though its good seafood , the prices are too high .\n->Even though its good seafood , the prices are too high .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the display is perfect for me , plenty of brightness and a decent resolution .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display is perfect for me , plenty of brightness and a decent resolution .\n->", + "output": "{\"text\": \"the display is perfect for me , plenty of brightness and a decent resolution .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'brightness', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchpad is hard and clunky .\n->the touchpad is hard and clunky .\n[{'aspect': 'touchpad', 'opinion': 'hard', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'clunky', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: He takes real pride in his food and his business .\n->He takes real pride in his food and his business .\n[{'aspect': 'food', 'opinion': 'pride', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the usb - c ports are nice and i found that a completely dead battery to fully charged time was about an hour .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe usb - c ports are nice and i found that a completely dead battery to fully charged time was about an hour .\n->", + "output": "{\"text\": \"the usb - c ports are nice and i found that a completely dead battery to fully charged time was about an hour .\", \"labels\": \"[{'aspect': 'usb - c ports', 'opinion': 'nice', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}, {'aspect': 'charged time', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as it turned out the thunderbolt ports are of no use .\n->as it turned out the thunderbolt ports are of no use .\n[{'aspect': 'thunderbolt ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#USABILITY'}]\nExample:\ntext: I recommend getting a reservation even though we saw people seated without one .\n->I recommend getting a reservation even though we saw people seated without one .\n[{'aspect': 'reservation', 'opinion': 'recommend', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i really liked that this chromebook came with 64gb of space but i really don ' t see how someone may fill that up unless they opt to sync their google drive offline or something .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really liked that this chromebook came with 64gb of space but i really don ' t see how someone may fill that up unless they opt to sync their google drive offline or something .\n->", + "output": "{\"text\": \"i really liked that this chromebook came with 64gb of space but i really don ' t see how someone may fill that up unless they opt to sync their google drive offline or something .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so i decide to report back to the waitress because it was completely inedible .\n->so i decide to report back to the waitress because it was completely inedible .\n[{'aspect': 'NULL', 'opinion': 'inedible', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: excellent choice in processor .\n->excellent choice in processor .\n[{'aspect': 'processor', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\ntext: i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\n->", + "output": "{\"text\": \"i wish the display was able to go about 1 or 2 clicks brighter and about 1 click darker at the lowest setting .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the build quality is good ( all aluminum body ) .\n->the build quality is good ( all aluminum body ) .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the three of us standing in front of her should have been an indication of how many of us there were .\n->the three of us standing in front of her should have been an indication of how many of us there were .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the speaker volume is decent but the quality of sound is terrible .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speaker volume is decent but the quality of sound is terrible .\n->", + "output": "{\"text\": \"the speaker volume is decent but the quality of sound is terrible .\", \"labels\": \"[{'aspect': 'speaker volume', 'opinion': 'decent', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'quality of sound', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even in the early rounds the frames are heavily compromised and the entire game feels very sluggish .\n->even in the early rounds the frames are heavily compromised and the entire game feels very sluggish .\n[{'aspect': 'NULL', 'opinion': 'sluggish', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the track pad is one of the best i have seen for a non - apple touch pad .\n->the track pad is one of the best i have seen for a non - apple touch pad .\n[{'aspect': 'track pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch pad', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the keyboard is awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is awesome .\n->", + "output": "{\"text\": \"the keyboard is awesome .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n->this is the most wonderful restaurant in all of new york city , not just brooklyn . . .\n[{'aspect': 'restaurant', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Dessert is a joke ... dont bother\n->Dessert is a joke ... dont bother\n[{'aspect': 'Dessert', 'opinion': 'joke', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - it ' s a bit heavy in tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- it ' s a bit heavy in tablet mode .\n->", + "output": "{\"text\": \"- it ' s a bit heavy in tablet mode .\", \"labels\": \"[{'aspect': 'tablet mode', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n->myagi is one of my favorite restaurants in the city ; the place the negative reviews describe sound like they were somewhere else .\n[{'aspect': 'myagi', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it is set far from the small street it ' s on , and there is no traffic noise .\n->it is set far from the small street it ' s on , and there is no traffic noise .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: - i wish the sound quality was better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- i wish the sound quality was better .\n->", + "output": "{\"text\": \"- i wish the sound quality was better .\", \"labels\": \"[{'aspect': 'sound quality', 'opinion': 'better', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n->i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n[{'aspect': 'machine', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: you ca n ' t go wrong here .\n->you ca n ' t go wrong here .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: despite these cons i think this was a great purchase .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndespite these cons i think this was a great purchase .\n->", + "output": "{\"text\": \"despite these cons i think this was a great purchase .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'cons', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the drinks are amazing and half off till 8pm .\n->the drinks are amazing and half off till 8pm .\n[{'aspect': 'drinks', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: a great laptop !\n->a great laptop !\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: performance wise , this is an excellent machine - it has a beautiful touchscreen , plenty of storage , converts to a tablet , and seamlessly connects to the google play store to run any of the millions of android apps available .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nperformance wise , this is an excellent machine - it has a beautiful touchscreen , plenty of storage , converts to a tablet , and seamlessly connects to the google play store to run any of the millions of android apps available .\n->", + "output": "{\"text\": \"performance wise , this is an excellent machine - it has a beautiful touchscreen , plenty of storage , converts to a tablet , and seamlessly connects to the google play store to run any of the millions of android apps available .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: please do n ' t fool us .\n->please do n ' t fool us .\n[{'aspect': 'NULL', 'opinion': 'fool', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: it just stopped working in the middle of my paper i was writing .\n->it just stopped working in the middle of my paper i was writing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the customer / warranty service that i received was first class and i am very impressed by asus for this reason alone .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe customer / warranty service that i received was first class and i am very impressed by asus for this reason alone .\n->", + "output": "{\"text\": \"the customer / warranty service that i received was first class and i am very impressed by asus for this reason alone .\", \"labels\": \"[{'aspect': 'customer / warranty service', 'opinion': 'first class', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: return window is just a month , nothing can be done and now i am on mercy of asus .\n->return window is just a month , nothing can be done and now i am on mercy of asus .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: we had the lobster sandwich and it was fantastic .\n->we had the lobster sandwich and it was fantastic .\n[{'aspect': 'lobster sandwich', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: if you are in need of a reliable laptop that is lightweight , fast , and convertible , i highly recommend the asus c302 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are in need of a reliable laptop that is lightweight , fast , and convertible , i highly recommend the asus c302 !\n->", + "output": "{\"text\": \"if you are in need of a reliable laptop that is lightweight , fast , and convertible , i highly recommend the asus c302 !\", \"labels\": \"[{'aspect': 'asus c302', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus c302', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus c302', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus c302', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus c302', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'laptop', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is great , and it ' s backlit !\n->the keyboard is great , and it ' s backlit !\n[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: battery life appears good but will depend on your brightness and how much streaming your doing .\n->battery life appears good but will depend on your brightness and how much streaming your doing .\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\n->", + "output": "{\"text\": \"i loved the simplicity of chrome os while enjoying the flip aspect of being able to turn it into a reading tablet when i wanted to as well .\", \"labels\": \"[{'aspect': 'flip', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: android apps from google play also running well with latest chrome update .\n->android apps from google play also running well with latest chrome update .\n[{'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: My fish was delicious in an incredible curry sauce .\n->My fish was delicious in an incredible curry sauce .\n[{'aspect': 'fish', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'curry sauce', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this time around , asus released an absolutely refined masterpiece .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis time around , asus released an absolutely refined masterpiece .\n->", + "output": "{\"text\": \"this time around , asus released an absolutely refined masterpiece .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'masterpiece', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: wifi radio loses signal too frequently .\n->wifi radio loses signal too frequently .\n[{'aspect': 'wifi radio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: As much as I like the food there , I ca n't bring myself to go back .\n->As much as I like the food there , I ca n't bring myself to go back .\n[{'aspect': 'food', 'opinion': 'like', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the c302 is heavier in size and appearance and also significantly better in all aspects .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe c302 is heavier in size and appearance and also significantly better in all aspects .\n->", + "output": "{\"text\": \"the c302 is heavier in size and appearance and also significantly better in all aspects .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'heavier', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'c302', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i / we will never go back to this place again .\n->i / we will never go back to this place again .\n[{'aspect': 'place', 'opinion': 'never go back', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the owner and staff are all japanese as well and that adds to the entire ambiance .\n->the owner and staff are all japanese as well and that adds to the entire ambiance .\n[{'aspect': 'owner', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}, {'aspect': 'ambiance', 'opinion': 'adds', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\n->", + "output": "{\"text\": \"the keyboard traction is clicky and the layout is spaced out well enough for a terrific typing experience .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'layout', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n->the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: screen is bright , doesn ' t feel heavy .\n->screen is bright , doesn ' t feel heavy .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': \"' t feel heavy\", 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\ntext: the premium feel combined with the charming simplicity of chrome os really gives it a stunning experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe premium feel combined with the charming simplicity of chrome os really gives it a stunning experience .\n->", + "output": "{\"text\": \"the premium feel combined with the charming simplicity of chrome os really gives it a stunning experience .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'premium', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chrome os', 'opinion': 'charming', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Top spot in town for Vietnamese classics , better than places that cost a lot more .\n->Top spot in town for Vietnamese classics , better than places that cost a lot more .\n[{'aspect': 'Vietnamese classics', 'opinion': 'Top', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Been going here since it opened have seen the quality value decrease considerably .\n->Been going here since it opened have seen the quality value decrease considerably .\n[{'aspect': 'quality value', 'opinion': 'decrease', 'polarity': 'negative', 'category': 'NULL'}]\ntext: so i decided to purchase the asus flip , and so far it has met every one of my nitpicky demanding needs .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso i decided to purchase the asus flip , and so far it has met every one of my nitpicky demanding needs .\n->", + "output": "{\"text\": \"so i decided to purchase the asus flip , and so far it has met every one of my nitpicky demanding needs .\", \"labels\": \"[{'aspect': 'asus flip', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everyone must come here at least once .\n->everyone must come here at least once .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i did not try the caviar but i tried their salmon and crab salad ( they are all good )\n->i did not try the caviar but i tried their salmon and crab salad ( they are all good )\n[{'aspect': 'salmon', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crab salad', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbuild quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n->", + "output": "{\"text\": \"build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - battery life is great .\n->- battery life is great .\n[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: sound has been fine , except for a few issues with an android app , and that can easily be software related as android is still in infant stages ( or at least toddler ) on chromebooks , and i expect those quirks will smooth out over time as chromebook software is updated on a very regular basis .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsound has been fine , except for a few issues with an android app , and that can easily be software related as android is still in infant stages ( or at least toddler ) on chromebooks , and i expect those quirks will smooth out over time as chromebook software is updated on a very regular basis .\n->", + "output": "{\"text\": \"sound has been fine , except for a few issues with an android app , and that can easily be software related as android is still in infant stages ( or at least toddler ) on chromebooks , and i expect those quirks will smooth out over time as chromebook software is updated on a very regular basis .\", \"labels\": \"[{'aspect': 'android app', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I recommend their Pad See Ew , Pork Chops or Tofu plates .\n->I recommend their Pad See Ew , Pork Chops or Tofu plates .\n[{'aspect': 'Pad See Ew', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Pork Chops', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Tofu plates', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the display is clear .\n->the display is clear .\n[{'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the keyboard is very nice for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is very nice for me .\n->", + "output": "{\"text\": \"the keyboard is very nice for me .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the overall device is slim and lightweight .\n->the overall device is slim and lightweight .\n[{'aspect': 'device', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: Service was good and food is wonderful .\n->Service was good and food is wonderful .\n[{'aspect': 'Service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nandroid is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\n->", + "output": "{\"text\": \"android is running flawlessly so far , barring one minor incident with sound on clash of clans ( which went away on reboot ) .\", \"labels\": \"[{'aspect': 'android', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i recommend it !\n->i recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n->Service was devine , oysters where a sensual as they come , and the price ca n't be beat ! ! !\n[{'aspect': 'Service', 'opinion': 'devine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'oysters', 'opinion': 'sensual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the m3 processor is fast and fluid .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe m3 processor is fast and fluid .\n->", + "output": "{\"text\": \"the m3 processor is fast and fluid .\", \"labels\": \"[{'aspect': 'm3 processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'm3 processor', 'opinion': 'fluid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n->I had never had Edamame pureed before but I thought it was innovative and tasty ( could 've used a bit more salt ) .\n[{'aspect': 'Edamame pureed', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Edamame pureed', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->The porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'NULL'}]\ntext: and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n->", + "output": "{\"text\": \"and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one special roll and one regular roll is enough to fill you up , but save room for dessert !\n->one special roll and one regular roll is enough to fill you up , but save room for dessert !\n[{'aspect': 'dessert', 'opinion': 'save room', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'special roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'regular roll', 'opinion': 'enough', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: such a disappointment . . .\n->such a disappointment . . .\n[{'aspect': 'NULL', 'opinion': 'disappointment', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: despite the minor case marks , i can heartily recommend this chromebook based on features , design , and operation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndespite the minor case marks , i can heartily recommend this chromebook based on features , design , and operation .\n->", + "output": "{\"text\": \"despite the minor case marks , i can heartily recommend this chromebook based on features , design , and operation .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: on that scale , it ' s a world - beater .\n->on that scale , it ' s a world - beater .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the only thing i ' m bummed about is the lack of google play .\n->the only thing i ' m bummed about is the lack of google play .\n[{'aspect': 'google play', 'opinion': 'lack', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: great product build - the quality of this asus chromebook is impressive at any price , but at sub - $ 500 it ' s astounding .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat product build - the quality of this asus chromebook is impressive at any price , but at sub - $ 500 it ' s astounding .\n->", + "output": "{\"text\": \"great product build - the quality of this asus chromebook is impressive at any price , but at sub - $ 500 it ' s astounding .\", \"labels\": \"[{'aspect': 'product build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'asus chromebook', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'asus chromebook', 'opinion': 'astounding', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\n->Yes , they use fancy ingredients , but even fancy ingredients do n't make for good pizza unless someone knows how to get the crust right .\n[{'aspect': 'ingredients', 'opinion': 'fancy', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'good', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: $ 6 and there is much tasty food , all of it fresh and continually refilled .\n->$ 6 and there is much tasty food , all of it fresh and continually refilled .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'refilled', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the metal case is really well built , and the fit and finish are virtually perfect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe metal case is really well built , and the fit and finish are virtually perfect .\n->", + "output": "{\"text\": \"the metal case is really well built , and the fit and finish are virtually perfect .\", \"labels\": \"[{'aspect': 'metal case', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'fit', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'finish', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good quality all around hardware + software , of course that is what apple is known for .\n->good quality all around hardware + software , of course that is what apple is known for .\n[{'aspect': 'hardware', 'opinion': 'good', 'polarity': 'positive', 'category': 'HARDWARE#QUALITY'}, {'aspect': 'software', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#QUALITY'}, {'aspect': 'apple', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: Too bad the food was n't of the same heritage .\n->Too bad the food was n't of the same heritage .\n[{'aspect': 'food', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}]\ntext: that said just buy it with confidence , it ' s a top quality product all around , and , it looks and feels that way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthat said just buy it with confidence , it ' s a top quality product all around , and , it looks and feels that way .\n->", + "output": "{\"text\": \"that said just buy it with confidence , it ' s a top quality product all around , and , it looks and feels that way .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'top', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is decent .\n->the food is decent .\n[{'aspect': 'food', 'opinion': 'decent', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .\n->Not one of our meals was edible - bland and/or made with weird rosemary or orange flavoring .\n[{'aspect': 'meals', 'opinion': 'edible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'rosemary or orange flavoring', 'opinion': 'weird', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the keyboard and touchpad experience are pivotal for a device like this and what asus delivers is very satisfying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard and touchpad experience are pivotal for a device like this and what asus delivers is very satisfying .\n->", + "output": "{\"text\": \"the keyboard and touchpad experience are pivotal for a device like this and what asus delivers is very satisfying .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n->One of the earlier people commenting on the restaurant did not get the that some experimenting is going on with the menu in a positive way .\n[{'aspect': 'menu', 'opinion': 'positive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: vanison was good but not amazing .\n->vanison was good but not amazing .\n[{'aspect': 'vanison', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'vanison', 'opinion': 'not amazing', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: the back - lit keyboard is a joy to use and the 1920 x 1080 touchscreen is superb .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe back - lit keyboard is a joy to use and the 1920 x 1080 touchscreen is superb .\n->", + "output": "{\"text\": \"the back - lit keyboard is a joy to use and the 1920 x 1080 touchscreen is superb .\", \"labels\": \"[{'aspect': 'back - lit keyboard', 'opinion': 'joy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'touchscreen', 'opinion': 'superb', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was knowledgeable and full of personality .\n->The staff was knowledgeable and full of personality .\n[{'aspect': 'staff', 'opinion': 'knowledgeable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it is terrific , as is the value .\n->it is terrific , as is the value .\n[{'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: this chromebook is light - weight , durable and beautiful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook is light - weight , durable and beautiful .\n->", + "output": "{\"text\": \"this chromebook is light - weight , durable and beautiful .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it holds up to constant use and it ' s really sturdy despite being really slim and lightweight .\n->it holds up to constant use and it ' s really sturdy despite being really slim and lightweight .\n[{'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n->the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: the 360 - degree hinges allow me to make presentations to customers and the battery life is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 360 - degree hinges allow me to make presentations to customers and the battery life is amazing .\n->", + "output": "{\"text\": \"the 360 - degree hinges allow me to make presentations to customers and the battery life is amazing .\", \"labels\": \"[{'aspect': '360 - degree hinges', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it worked great .\n->it worked great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n->the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: this little guy fits the bill perfectly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little guy fits the bill perfectly .\n->", + "output": "{\"text\": \"this little guy fits the bill perfectly .\", \"labels\": \"[{'aspect': 'guy', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Average to good Thai food , but terrible delivery .\n->Average to good Thai food , but terrible delivery .\n[{'aspect': 'Thai food', 'opinion': 'Average to good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'delivery', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n->what we did do was waste 3 hours being trapped in a table waiting and waiting for food and drinks and hooka . . some of which we never received !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the touch screen is nice , and i like to use it for free handing things when i need to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touch screen is nice , and i like to use it for free handing things when i need to .\n->", + "output": "{\"text\": \"the touch screen is nice , and i like to use it for free handing things when i need to .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ravioli was good . . . but i have to say that i found everything a bit overpriced .\n->ravioli was good . . . but i have to say that i found everything a bit overpriced .\n[{'aspect': 'ravioli', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: this chrome book joins the group and is itself excellent and different .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chrome book joins the group and is itself excellent and different .\n->", + "output": "{\"text\": \"this chrome book joins the group and is itself excellent and different .\", \"labels\": \"[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chrome book', 'opinion': 'different', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i really like the fact that when it boots up , it ' s up .\n->i really like the fact that when it boots up , it ' s up .\n[{'aspect': 'boots up', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the keyboard is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is good .\n->", + "output": "{\"text\": \"the keyboard is good .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n->to top it all off . . the main reason we came to your restaurant was for the belly dancers and missed the first show as we were not seated yet and the 2nd belly dancer only danced at two tables in the back of the restaurant and never made it around to the other half of the restaurant .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: the steak tartare is a great bet , they fix it for you at the table .\n->the steak tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'steak tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: the screen is bright and color spread is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is bright and color spread is good .\n->", + "output": "{\"text\": \"the screen is bright and color spread is good .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'color spread', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n->it ' s a real asus hasn ' t been able to improve their customer service and support and won ' t fix this issue .\n[{'aspect': 'customer service and support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n->i will update this review when there is further progress - i believe this is a 5 star product but the updates are reducing my confidence in the software .\n[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'software', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\ntext: fast , beautiful display , comfortable keyboard , and the android apps work well ( not great yet , but well ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfast , beautiful display , comfortable keyboard , and the android apps work well ( not great yet , but well ) .\n->", + "output": "{\"text\": \"fast , beautiful display , comfortable keyboard , and the android apps work well ( not great yet , but well ) .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'display', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'android apps', 'opinion': 'not great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: our server was very helpful and friendly .\n->our server was very helpful and friendly .\n[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this adds to my need for a strong system and willing to sacrifice some weight for strength .\n->this adds to my need for a strong system and willing to sacrifice some weight for strength .\n[{'aspect': 'system', 'opinion': 'strong', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\n->", + "output": "{\"text\": \"sorry for the ` ` downer ` ` start here , because as a chromebook , the c302 is very good .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the crunchy tuna , it is to die for .\n->try the crunchy tuna , it is to die for .\n[{'aspect': 'crunchy tuna', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: - i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n->- i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the c302 m5 ( the best processor i could find so far , is a very good chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe c302 m5 ( the best processor i could find so far , is a very good chromebook .\n->", + "output": "{\"text\": \"the c302 m5 ( the best processor i could find so far , is a very good chromebook .\", \"labels\": \"[{'aspect': 'm5', 'opinion': 'best', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'processor', 'opinion': 'best', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n->Even after getting pushed out by the no-class Famous Ray 's , Sal has risen again to carry on his father 's uncle 's legacies with a smile , true love for his community , and let 's not forget the Outstanding Pizza !\n[{'aspect': 'Pizza', 'opinion': 'Outstanding', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' m saving up for my next visit .\n->i ' m saving up for my next visit .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: still , a very nice compact chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nstill , a very nice compact chromebook .\n->", + "output": "{\"text\": \"still , a very nice compact chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: not a good quality laptop .\n->not a good quality laptop .\n[{'aspect': 'laptop', 'opinion': 'not a good', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: thank you everyone at water ' s edge .\n->thank you everyone at water ' s edge .\n[{'aspect': \"water ' s edge\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n->", + "output": "{\"text\": \"it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'screen', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great place to go for a drink too because they have 100 kinds of beer .\n->great place to go for a drink too because they have 100 kinds of beer .\n[{'aspect': 'drink', 'opinion': 'great', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the keyboard on this thing is my favorite part of the computer .\n->the keyboard on this thing is my favorite part of the computer .\n[{'aspect': 'keyboard', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'computer', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the biggest benefit with this computer for me is the longevity of the battery life with this thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe biggest benefit with this computer for me is the longevity of the battery life with this thing .\n->", + "output": "{\"text\": \"the biggest benefit with this computer for me is the longevity of the battery life with this thing .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'longevity', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n->Fluke sashimi drizzled with jalapeno-lime olive oil , the fruit of the oil nicely highlighting the fish 's sweetness .\n[{'aspect': 'fruit of the oil', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'sweetness', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Ambience is so cute and quaint , good for business although we were there on vacation .\n->Ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'Ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Ambience', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: * very solidly built and it transitions nicely from laptop to tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* very solidly built and it transitions nicely from laptop to tablet mode .\n->", + "output": "{\"text\": \"* very solidly built and it transitions nicely from laptop to tablet mode .\", \"labels\": \"[{'aspect': 'built', 'opinion': 'solidly', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'tablet', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n->i read a lot about people getting doa versions of this product from the reviews and thought i got a pretty good one until now .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n->The outdoor atmosphere of sitting on the sidewalk watching the world go by 50 feet away on 6th avenue on a cool evening was wonderful .\n[{'aspect': 'outdoor atmosphere', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it feels very nice and i also really like the backlit keys in the dark .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit feels very nice and i also really like the backlit keys in the dark .\n->", + "output": "{\"text\": \"it feels very nice and i also really like the backlit keys in the dark .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'backlit keys', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is also one of the nicest most comfortable i have ever used .\n->the keyboard is also one of the nicest most comfortable i have ever used .\n[{'aspect': 'keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\nExample:\ntext: our teenage kids love it , too .\n->our teenage kids love it , too .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: * the screen is more than adequate for me , although i have not used it outside much yet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* the screen is more than adequate for me , although i have not used it outside much yet .\n->", + "output": "{\"text\": \"* the screen is more than adequate for me , although i have not used it outside much yet .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all in all , i ' m really glad i got this machine .\n->all in all , i ' m really glad i got this machine .\n[{'aspect': 'machine', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n->they were such a rip - off ( $ 8 . 95 for four small meat patties in steamed buns ) and not worth trying .\n[{'aspect': 'NULL', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\ntext: * battery - i can not really speak to the battery life yet , but as a power user with lots of tabs and several apps open , it is giving me a days use 5 + hours of use so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n* battery - i can not really speak to the battery life yet , but as a power user with lots of tabs and several apps open , it is giving me a days use 5 + hours of use so far .\n->", + "output": "{\"text\": \"* battery - i can not really speak to the battery life yet , but as a power user with lots of tabs and several apps open , it is giving me a days use 5 + hours of use so far .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the main bad for this particular variant is that google is no longer going to support it with updates .\n->the main bad for this particular variant is that google is no longer going to support it with updates .\n[{'aspect': 'google', 'opinion': 'bad', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n->two complaints - - their appetizer selection stinks , it would be nice to get some mozzarella sticks on the menu .\n[{'aspect': 'appetizer selection', 'opinion': 'stinks', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: it is working really well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is working really well .\n->", + "output": "{\"text\": \"it is working really well .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n->the decor in this place is very diner - ish and the kind of place you expect in the east village - not romantic , just simple , small and sparse .\n[{'aspect': 'decor', 'opinion': 'diner - ish', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n->i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: surprisingly to me , the tablet form has been better than expected for reading .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsurprisingly to me , the tablet form has been better than expected for reading .\n->", + "output": "{\"text\": \"surprisingly to me , the tablet form has been better than expected for reading .\", \"labels\": \"[{'aspect': 'tablet form', 'opinion': 'surprisingly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'tablet form', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n->You can get a completely delish martini in a glass ( that 's about 2 1/2 drinks ) for $ 8.50 ( I recommend the Vanilla Shanty , mmmm ! ) in a great homey setting with great music .\n[{'aspect': 'martini', 'opinion': 'delish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Vanilla Shanty', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'homey', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: All we received was an apology as we left to see our show without dinner .\n->All we received was an apology as we left to see our show without dinner .\n[{'aspect': 'dinner', 'opinion': 'without', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: in laptop mode the trackpad works very well for this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin laptop mode the trackpad works very well for this .\n->", + "output": "{\"text\": \"in laptop mode the trackpad works very well for this .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the inside is great but i feel like it won ' t last long enough before the outside crumbles .\n->the inside is great but i feel like it won ' t last long enough before the outside crumbles .\n[{'aspect': 'inside', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the touch screen is nice , and i like to use it for free handing things when i need to .\n->the touch screen is nice , and i like to use it for free handing things when i need to .\n[{'aspect': 'touch screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: the sound is not real loud from the speakers , but i am reasonably pleased with the quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sound is not real loud from the speakers , but i am reasonably pleased with the quality .\n->", + "output": "{\"text\": \"the sound is not real loud from the speakers , but i am reasonably pleased with the quality .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'speakers', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n->even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'working unit', 'opinion': 'outweighs', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the base model went up in price $ 400 , which excludes any performance benefits .\n->the base model went up in price $ 400 , which excludes any performance benefits .\n[{'aspect': 'base model', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}]\ntext: it ' s one of the best chromebook in the market if not the best .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s one of the best chromebook in the market if not the best .\n->", + "output": "{\"text\": \"it ' s one of the best chromebook in the market if not the best .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great atmoshere and worth every bit .\n->Great atmoshere and worth every bit .\n[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n->The food inludes famous scrumptious bombay style chaat such as bhelpuri , sevpuri and samosa chaats , as well as other great indian appetizers .\n[{'aspect': 'bhelpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sevpuri', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'samosa chaats', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'scrumptious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'indian appetizers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bombay style chaat', 'opinion': 'famous scrumptious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: speed : it ' s very fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeed : it ' s very fast .\n->", + "output": "{\"text\": \"speed : it ' s very fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: long story short , i loved asus and have been buying them for years .\n->long story short , i loved asus and have been buying them for years .\n[{'aspect': 'asus', 'opinion': 'loved', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: there is plenty of room for positives , it looks sharp , runs great and is the perfect size for traveling and working on the go .\n->there is plenty of room for positives , it looks sharp , runs great and is the perfect size for traveling and working on the go .\n[{'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'traveling', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: keybooard : keyboard is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeybooard : keyboard is good .\n->", + "output": "{\"text\": \"keybooard : keyboard is good .\", \"labels\": \"[{'aspect': 'keybooard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service not the friendliest to our ` ` large party ' ' !\n->service not the friendliest to our ` ` large party ' ' !\n[{'aspect': 'service', 'opinion': 'not the friendliest', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Chance is a small cozy restaurant , with a romantic feel to it , the decor is great .\n->Chance is a small cozy restaurant , with a romantic feel to it , the decor is great .\n[{'aspect': 'decor', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: trackbad : it could be better but it ' s not bad 8 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntrackbad : it could be better but it ' s not bad 8 .\n->", + "output": "{\"text\": \"trackbad : it could be better but it ' s not bad 8 .\", \"labels\": \"[{'aspect': 'trackbad', 'opinion': 'better', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackbad', 'opinion': 'not bad', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n->I am relatively new to the area and tried Pick a bgel on 2nd and was disappointed with the service and I thought the food was overated and on the pricey side .\n[{'aspect': 'service', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'overated', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: otherwise i really do love it\n->otherwise i really do love it\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the batrey charges very quickly , you will need 1 hour for a full charge .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe batrey charges very quickly , you will need 1 hour for a full charge .\n->", + "output": "{\"text\": \"the batrey charges very quickly , you will need 1 hour for a full charge .\", \"labels\": \"[{'aspect': 'batrey charges', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections .\n->We were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections .\n[{'aspect': 'staff', 'opinion': 'enthusiastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'manager', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: not happy that the l and the quotation mark key no longer work unless i bang on them .\n->not happy that the l and the quotation mark key no longer work unless i bang on them .\n[{'aspect': 'l and the quotation mark key', 'opinion': 'not happy', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: the coolest thing is the touch screen on something this size .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe coolest thing is the touch screen on something this size .\n->", + "output": "{\"text\": \"the coolest thing is the touch screen on something this size .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'coolest', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i had this for all of a day before it began to have severe issues i set it back to factory settings and that worked for a time but it eventually had an issue with system 32 .\n->i had this for all of a day before it began to have severe issues i set it back to factory settings and that worked for a time but it eventually had an issue with system 32 .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'system 32', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: This is the best sushi in new york city - hands down .\n->This is the best sushi in new york city - hands down .\n[{'aspect': 'sushi', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so far , netflix has been the most useful for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far , netflix has been the most useful for me .\n->", + "output": "{\"text\": \"so far , netflix has been the most useful for me .\", \"labels\": \"[{'aspect': 'netflix', 'opinion': 'useful', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Horrible food and horrible service .\n->Horrible food and horrible service .\n[{'aspect': 'food', 'opinion': 'Horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: whoever the jazz duo was , they were on point .\n->whoever the jazz duo was , they were on point .\n[{'aspect': 'jazz duo', 'opinion': 'on point', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: i love my c302 !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love my c302 !\n->", + "output": "{\"text\": \"i love my c302 !\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was n ' t thrilled to have to wait on line for thirty minutes , but i guess that ' s the price you pay for a popular place .\n->i was n ' t thrilled to have to wait on line for thirty minutes , but i guess that ' s the price you pay for a popular place .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Good luck getting a table .\n->Good luck getting a table .\n[{'aspect': 'getting a table', 'opinion': 'Good luck', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\n->", + "output": "{\"text\": \"it ' s the perfect size and , like you said , the keyboard is one of the best i ' ve ever laid my hands on .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my first chromebook , and so far ( about one month of use ) i like it .\n->my first chromebook , and so far ( about one month of use ) i like it .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i love this computer .\n->i love this computer .\n[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the build quality is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is great .\n->", + "output": "{\"text\": \"the build quality is great .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it was pretty inexpensive too .\n->it was pretty inexpensive too .\n[{'aspect': 'NULL', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: It takes forever to get a drink and they almost always forget to bring something ( although they dont forget to charge you for it .\n->It takes forever to get a drink and they almost always forget to bring something ( although they dont forget to charge you for it .\n[{'aspect': 'drink', 'opinion': 'forever', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: it travels well , great battery life , and is powerful enough for 100 % of the tasks i need to do ( web , streaming , word processing , reports , email ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit travels well , great battery life , and is powerful enough for 100 % of the tasks i need to do ( web , streaming , word processing , reports , email ) .\n->", + "output": "{\"text\": \"it travels well , great battery life , and is powerful enough for 100 % of the tasks i need to do ( web , streaming , word processing , reports , email ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Yakitori ( bbq meats ) is tasty too .\n->Yakitori ( bbq meats ) is tasty too .\n[{'aspect': 'Yakitori ( bbq meats )', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: gpu wasn ' t drawing a lot of power because i was playing world of warcraft on the recommended settings .\n->gpu wasn ' t drawing a lot of power because i was playing world of warcraft on the recommended settings .\n[{'aspect': 'gpu', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'GRAPHICS#OPERATION_PERFORMANCE'}]\ntext: chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\n->", + "output": "{\"text\": \"chrome is also a great os - fast and so convenient especially if you rely on docs , sheets , google drive , etc .\", \"labels\": \"[{'aspect': 'chrome', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'great', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'os', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n->downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n[{'aspect': 'appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: this place , which is only a few months old , is perhaps queens ' biggest secret !\n->this place , which is only a few months old , is perhaps queens ' biggest secret !\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: this chromebook is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook is amazing .\n->", + "output": "{\"text\": \"this chromebook is amazing .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n->my party had the bbe $ 29 fixe prix menu , which was such a wonderful deal since it also came with a flight of sake !\n[{'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'bbe $ 29 fixe prix menu', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: Our waitress was sweet and accomodating , not overbearing .\n->Our waitress was sweet and accomodating , not overbearing .\n[{'aspect': 'waitress', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitress', 'opinion': 'accomodating', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this chromebook features a full size keyboard and is easier type on by a long shot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook features a full size keyboard and is easier type on by a long shot .\n->", + "output": "{\"text\": \"this chromebook features a full size keyboard and is easier type on by a long shot .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'full size', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'easier', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a computer , for typing and using internet in general , this computer is good .\n->as a computer , for typing and using internet in general , this computer is good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Should you happen to be impressed by the cuisine definitely try it .\n->Should you happen to be impressed by the cuisine definitely try it .\n[{'aspect': 'cuisine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it has a very fast 64gb ssd and 4gb on lpddr3 memory .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has a very fast 64gb ssd and 4gb on lpddr3 memory .\n->", + "output": "{\"text\": \"it has a very fast 64gb ssd and 4gb on lpddr3 memory .\", \"labels\": \"[{'aspect': '64gb ssd', 'opinion': 'fast', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'lpddr3 memory', 'opinion': 'fast', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there ' s another girl who i ca n ' t describe , she is about 5 ' 6 ' ' with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you ' re * really * talking about .\n->there ' s another girl who i ca n ' t describe , she is about 5 ' 6 ' ' with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you ' re * really * talking about .\n[{'aspect': 'girl', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: Someone else recommended the dessert - we also left that .\n->Someone else recommended the dessert - we also left that .\n[{'aspect': 'dessert', 'opinion': 'recommended', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it is a fanless processor so there are no vents or openings of any kind which makes this exceptionally quiet as well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is a fanless processor so there are no vents or openings of any kind which makes this exceptionally quiet as well .\n->", + "output": "{\"text\": \"it is a fanless processor so there are no vents or openings of any kind which makes this exceptionally quiet as well .\", \"labels\": \"[{'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: slightly on the pricey side but worth it !\n->slightly on the pricey side but worth it !\n[{'aspect': 'NULL', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it fancies itself a convertible notebook / app consuming functional tablet , but it is not .\n->it fancies itself a convertible notebook / app consuming functional tablet , but it is not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: the device is very light and easily transportable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe device is very light and easily transportable .\n->", + "output": "{\"text\": \"the device is very light and easily transportable .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'transportable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .\n->The Prix Fixe menu is worth every penny and you get more than enough ( both in quantity AND quality ) .\n[{'aspect': 'Prix Fixe menu', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quality', 'opinion': 'more than enough', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: keybooard : keyboard is good .\n->keybooard : keyboard is good .\n[{'aspect': 'keybooard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: speaking of asus flip itself - it is great in every way !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeaking of asus flip itself - it is great in every way !\n->", + "output": "{\"text\": \"speaking of asus flip itself - it is great in every way !\", \"labels\": \"[{'aspect': 'asus flip', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I would definitely go back -- if only for some of those exotic martinis on the blackboard .\n->I would definitely go back -- if only for some of those exotic martinis on the blackboard .\n[{'aspect': 'martinis', 'opinion': 'exotic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Delivery service is great too .\n->Delivery service is great too .\n[{'aspect': 'Delivery service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\n->", + "output": "{\"text\": \"i am not a techie , all i can say as user - this chromebook is fast , it lasts and weighs very little .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it wouldn ' t charge .\n->it wouldn ' t charge .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n->i purchased this laptop on the 11th of june 2018 and today is the 25th , which means i have had it for just about 2 weeks .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: the unit is sturdy and the screen is excellent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe unit is sturdy and the screen is excellent .\n->", + "output": "{\"text\": \"the unit is sturdy and the screen is excellent .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not impressed with the food .\n->Not impressed with the food .\n[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the only thing i don ' t like is that the power button sits beside the delete key .\n->the only thing i don ' t like is that the power button sits beside the delete key .\n[{'aspect': 'power button', 'opinion': \"' t like\", 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: bluetooth to an external speaker is do - able , but not convenient .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbluetooth to an external speaker is do - able , but not convenient .\n->", + "output": "{\"text\": \"bluetooth to an external speaker is do - able , but not convenient .\", \"labels\": \"[{'aspect': 'bluetooth', 'opinion': 'not convenient', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#CONNECTIVITY'}, {'aspect': 'speaker', 'opinion': 'not convenient', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - physically it is appealing looking .\n->- physically it is appealing looking .\n[{'aspect': 'NULL', 'opinion': 'appealing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: the menu looked great , and the waiter was very nice , but when the food came , it was average .\n->the menu looked great , and the waiter was very nice , but when the food came , it was average .\n[{'aspect': 'menu', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'waiter', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: note : my first one arrived with a crushed speaker screen on one side , though the packaging was unmolested .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnote : my first one arrived with a crushed speaker screen on one side , though the packaging was unmolested .\n->", + "output": "{\"text\": \"note : my first one arrived with a crushed speaker screen on one side , though the packaging was unmolested .\", \"labels\": \"[{'aspect': 'speaker screen', 'opinion': 'crushed', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like the laptop for it ' s hardware , and it ' s working properly .\n->i like the laptop for it ' s hardware , and it ' s working properly .\n[{'aspect': 'hardware', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' ve been using this for a couple of weeks and i must say i am very very pleased with the product .\n->i ' ve been using this for a couple of weeks and i must say i am very very pleased with the product .\n[{'aspect': 'product', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this is a wonderful device .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a wonderful device .\n->", + "output": "{\"text\": \"this is a wonderful device .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far i really love this product !\n->so far i really love this product !\n[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: oh , and this charges fast .\n->oh , and this charges fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: i ' m no expert on screens but i personally think the panel looks very nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m no expert on screens but i personally think the panel looks very nice .\n->", + "output": "{\"text\": \"i ' m no expert on screens but i personally think the panel looks very nice .\", \"labels\": \"[{'aspect': 'panel', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is a great place to take out - of - towners , and perfect for watching the sunset .\n->this is a great place to take out - of - towners , and perfect for watching the sunset .\n[{'aspect': 'place', 'opinion': 'great', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\nExample:\ntext: even though the place is not beautiful , the food speaks for itself .\n->even though the place is not beautiful , the food speaks for itself .\n[{'aspect': 'place', 'opinion': 'not beautiful', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'speaks for itself', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: battery life appears good but will depend on your brightness and how much streaming your doing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life appears good but will depend on your brightness and how much streaming your doing .\n->", + "output": "{\"text\": \"battery life appears good but will depend on your brightness and how much streaming your doing .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the crust is thin , the ingredients are fresh and the staff is friendly .\n->the crust is thin , the ingredients are fresh and the staff is friendly .\n[{'aspect': 'crust', 'opinion': 'thin', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'ingredients', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n->the 1tb included drive is pretty slow , but battery life and processing power is pretty great .\n[{'aspect': '1tb included drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'processing power', 'opinion': 'great', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\ntext: i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\n->", + "output": "{\"text\": \"i was able to charge it quite rapidly with the usb - c charger and was about fully charged in an hour .\", \"labels\": \"[{'aspect': 'usb - c charger', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: all around , a very lacking laptop .\n->all around , a very lacking laptop .\n[{'aspect': 'laptop', 'opinion': 'lacking', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: next , is that the track pad is insanely wobbly .\n->next , is that the track pad is insanely wobbly .\n[{'aspect': 'track pad', 'opinion': 'wobbly', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: boot time is basically non - existent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nboot time is basically non - existent .\n->", + "output": "{\"text\": \"boot time is basically non - existent .\", \"labels\": \"[{'aspect': 'boot time', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: highly recommended !\n->highly recommended !\n[{'aspect': 'NULL', 'opinion': 'recommended', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the touch screen works quite well and i have found myself watching videos in tablet mode with ease .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touch screen works quite well and i have found myself watching videos in tablet mode with ease .\n->", + "output": "{\"text\": \"the touch screen works quite well and i have found myself watching videos in tablet mode with ease .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'well', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is may 14 and it ' s not connecting to wifi .\n->it is may 14 and it ' s not connecting to wifi .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n->The service was impeccable and unobtrusive -- the staff knows what they are there to do -- to know their menu , present your meal , and attend to your needs .\n[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it has non existent boot times and updates are easy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has non existent boot times and updates are easy .\n->", + "output": "{\"text\": \"it has non existent boot times and updates are easy .\", \"labels\": \"[{'aspect': 'boot times', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'updates', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n->that said if you ' re not a computer savvy no worries as soon as you turn it on cortana the computers assistant walks you through everything that you need to do .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\nExample:\ntext: build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n->build quality is very good , nearing the ` ` excellent ` ` mark , especially for the price .\n[{'aspect': 'build quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'build quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\n->", + "output": "{\"text\": \"the chromebook is great as a truly mobile device - take it anywhere , open it up , and do whatever you need to do without being hassled and within moments .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service here was great , food was fantastic .\n->Service here was great , food was fantastic .\n[{'aspect': 'Service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the 302 would be just about the perfect chromebook if it had smaller bezels , though .\n->the 302 would be just about the perfect chromebook if it had smaller bezels , though .\n[{'aspect': '302', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: love the keyboard more than its predecessor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the keyboard more than its predecessor .\n->", + "output": "{\"text\": \"love the keyboard more than its predecessor .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did n ' t complain , i liked the atmosphere so much .\n->i did n ' t complain , i liked the atmosphere so much .\n[{'aspect': 'atmosphere', 'opinion': 'liked', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i will be going back and heartily recommend it !\n->i will be going back and heartily recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: great battery ; charges quickly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat battery ; charges quickly .\n->", + "output": "{\"text\": \"great battery ; charges quickly .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->This place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i am truly enjoying my laptop after one month .\n->i am truly enjoying my laptop after one month .\n[{'aspect': 'laptop', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: good looking screen , has been bright enough for daily use , including outdoor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood looking screen , has been bright enough for daily use , including outdoor .\n->", + "output": "{\"text\": \"good looking screen , has been bright enough for daily use , including outdoor .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n->Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n[{'aspect': 'location', 'opinion': 'nice quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: everything about this restaurant was special .\n->everything about this restaurant was special .\n[{'aspect': 'restaurant', 'opinion': 'special', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: the screen ' s color gamut is only 50 % ntsc , but as i don ' t perform image editing with it , this hasn ' t mattered .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen ' s color gamut is only 50 % ntsc , but as i don ' t perform image editing with it , this hasn ' t mattered .\n->", + "output": "{\"text\": \"the screen ' s color gamut is only 50 % ntsc , but as i don ' t perform image editing with it , this hasn ' t mattered .\", \"labels\": \"[{'aspect': \"screen ' s color gamut\", 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the prices were way to high for what you get .\n->And the prices were way to high for what you get .\n[{'aspect': 'prices', 'opinion': 'high', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: 5 pound laptop with its nine hour battery life .\n->5 pound laptop with its nine hour battery life .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: it worked beautifully and smoothly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit worked beautifully and smoothly .\n->", + "output": "{\"text\": \"it worked beautifully and smoothly .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the 16gb of ssd storage is perfect as i use it for school and the biggest file i might be storing would be a picture .\n->the 16gb of ssd storage is perfect as i use it for school and the biggest file i might be storing would be a picture .\n[{'aspect': 'ssd storage', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'HARD_DISC#GENERAL'}]\nExample:\ntext: The service varys from day to day- sometimes they 're very nice , and sometimes not .\n->The service varys from day to day- sometimes they 're very nice , and sometimes not .\n[{'aspect': 'service', 'opinion': 'varys', 'polarity': 'negative', 'category': 'NULL'}]\ntext: - fully charges in about an hour\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- fully charges in about an hour\n->", + "output": "{\"text\": \"- fully charges in about an hour\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n->while the stock aluminum case is darn solid enough , it can take scratches easily from things like keys or cable connectors tossed in a bag with it .\n[{'aspect': 'stock aluminum case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n->we also asked for hooka six times and the waiter kept telling us one minute and never returning with the hooka .\n[{'aspect': 'waiter', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: - backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\n->", + "output": "{\"text\": \"- backlit keyboard that ' s really quiet ( perfect for in - class typing or library use )\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit keyboard', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place does n ' t make any sense\n->this place does n ' t make any sense\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Excellent atmosphere , delicious dishes good and friendly service .\n->Excellent atmosphere , delicious dishes good and friendly service .\n[{'aspect': 'atmosphere', 'opinion': 'Excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - fast boot up ( 3 seconds )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- fast boot up ( 3 seconds )\n->", + "output": "{\"text\": \"- fast boot up ( 3 seconds )\", \"labels\": \"[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I just wonder how you can have such a delicious meal for such little money .\n->I just wonder how you can have such a delicious meal for such little money .\n[{'aspect': 'meal', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'money', 'opinion': 'little', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i will probably be sending it back as it seems too complicated .\n->i will probably be sending it back as it seems too complicated .\n[{'aspect': 'NULL', 'opinion': 'complicated', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: keyboard has a nice feel to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard has a nice feel to it .\n->", + "output": "{\"text\": \"keyboard has a nice feel to it .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: considering you will spend at least $ 60 a head , i expect better service .\n->considering you will spend at least $ 60 a head , i expect better service .\n[{'aspect': 'service', 'opinion': 'expect better', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: it feels sturdy and reliable , both hardware and software - wise .\n->it feels sturdy and reliable , both hardware and software - wise .\n[{'aspect': 'hardware', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'hardware', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'software', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}, {'aspect': 'software', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\ntext: voice to text is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvoice to text is good .\n->", + "output": "{\"text\": \"voice to text is good .\", \"labels\": \"[{'aspect': 'voice to text', 'opinion': 'good', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n->i come from a family of pizzeria owners , and i ' m almost ashamed to say that the pizza in fornino ' s blows my families receipies away .\n[{'aspect': 'pizza', 'opinion': 'ashamed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my main complain involves terrible battery life .\n->my main complain involves terrible battery life .\n[{'aspect': 'battery life', 'opinion': 'terrible', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\ntext: chrome os is like the chrome browser + apps taken up a level .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchrome os is like the chrome browser + apps taken up a level .\n->", + "output": "{\"text\": \"chrome os is like the chrome browser + apps taken up a level .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The lava cake dessert was incredible and I recommend it .\n->The lava cake dessert was incredible and I recommend it .\n[{'aspect': 'lava cake dessert', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lava cake dessert', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i bought the computer on january 2018 and so far i am really enjoying it .\n->i bought the computer on january 2018 and so far i am really enjoying it .\n[{'aspect': 'computer', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i don ' t know that i would want to use for work but it ' s perfect for personal use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t know that i would want to use for work but it ' s perfect for personal use .\n->", + "output": "{\"text\": \"i don ' t know that i would want to use for work but it ' s perfect for personal use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Of course the reason its so packed is because the food is so delicious !\n->Of course the reason its so packed is because the food is so delicious !\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the dancing , white river and millenium rolls are musts .\n->the dancing , white river and millenium rolls are musts .\n[{'aspect': 'dancing , white river and millenium rolls', 'opinion': 'musts', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the screen is great , the computer is fast , and looks great with the aluminum case .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is great , the computer is fast , and looks great with the aluminum case .\n->", + "output": "{\"text\": \"the screen is great , the computer is fast , and looks great with the aluminum case .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'aluminum case', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: It is the type of place to run into old friends and have a late , raucous dinner .\n->It is the type of place to run into old friends and have a late , raucous dinner .\n[{'aspect': 'dinner', 'opinion': 'raucous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n->this device ( and its brother the plus ) has a grave hardware flaw that causes the touchscreen to move on its own .\n[{'aspect': 'hardware', 'opinion': 'flaw', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: openvpn support needs some serious work still .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nopenvpn support needs some serious work still .\n->", + "output": "{\"text\": \"openvpn support needs some serious work still .\", \"labels\": \"[{'aspect': 'openvpn support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s the only place you can get yummy authentic japanese comfort food .\n->it ' s the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'japanese comfort food', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food has been consistant for years and it never lets you down .\n->The food has been consistant for years and it never lets you down .\n[{'aspect': 'food', 'opinion': 'consistant', 'polarity': 'positive', 'category': 'NULL'}]\ntext: don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndon ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n->", + "output": "{\"text\": \"don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'ugly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you like spicy food get the chicken vindaloo .\n->if you like spicy food get the chicken vindaloo .\n[{'aspect': 'chicken vindaloo', 'opinion': 'like', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: If you have a dumpling fetish i suggest you try some here !\n->If you have a dumpling fetish i suggest you try some here !\n[{'aspect': 'dumpling', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the keyboard is really nice - .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is really nice - .\n->", + "output": "{\"text\": \"the keyboard is really nice - .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n->it also refuses to charge past 90 % and instantly jumps to 80 % when i unplug it .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n->battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: this chromebook works like a dream .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook works like a dream .\n->", + "output": "{\"text\": \"this chromebook works like a dream .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'dream', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: He takes real pride in his food and his business .\n->He takes real pride in his food and his business .\n[{'aspect': 'food', 'opinion': 'pride', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n->This little place definitely exceeded my expectations and you sure get a lot of food for your money .\n[{'aspect': 'food', 'opinion': 'lot', 'polarity': 'positive', 'category': 'NULL'}]\ntext: note : i haven ' t had any issues with the touchscreen at all .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnote : i haven ' t had any issues with the touchscreen at all .\n->", + "output": "{\"text\": \"note : i haven ' t had any issues with the touchscreen at all .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: amazon is 2 - day shipping me a replacement .\n->amazon is 2 - day shipping me a replacement .\n[{'aspect': 'amazon', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: wine list selection is good and wine - by - the - glass was generously filled to the top .\n->wine list selection is good and wine - by - the - glass was generously filled to the top .\n[{'aspect': 'wine list selection', 'opinion': 'good', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wine - by - the - glass', 'opinion': 'generously filled', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}]\ntext: i used to use a ridiculously heavy d _ _ _ inspirion , which i love because of the 17 ` ` screen and capabilities , but i quickly found that carrying it anywhere caused my shoulders and back to hurt !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni used to use a ridiculously heavy d _ _ _ inspirion , which i love because of the 17 ` ` screen and capabilities , but i quickly found that carrying it anywhere caused my shoulders and back to hurt !\n->", + "output": "{\"text\": \"i used to use a ridiculously heavy d _ _ _ inspirion , which i love because of the 17 ` ` screen and capabilities , but i quickly found that carrying it anywhere caused my shoulders and back to hurt !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The quail was fantastic and unique and the pastas were full of flavor .\n->The quail was fantastic and unique and the pastas were full of flavor .\n[{'aspect': 'quail', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quail', 'opinion': 'unique', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pastas', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'flavor', 'opinion': 'full', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: good food : my favorite is the seafood spaghetti .\n->good food : my favorite is the seafood spaghetti .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood spaghetti', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i love , love , love this computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love , love , love this computer .\n->", + "output": "{\"text\": \"i love , love , love this computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their pad penang is delicious and everything else is fantastic .\n->Their pad penang is delicious and everything else is fantastic .\n[{'aspect': 'pad penang', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: restaurant with a view\n->restaurant with a view\n[{'aspect': 'view', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LOCATION#GENERAL'}]\ntext: big screen !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbig screen !\n->", + "output": "{\"text\": \"big screen !\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food is good , i ca n ' t lie .\n->the food is good , i ca n ' t lie .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n->i absolutely love this laptop , it looks great , its quality is amazing and can handle so many games without any lag involved .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: sound sort of sucks but i don ' t use it for music .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsound sort of sucks but i don ' t use it for music .\n->", + "output": "{\"text\": \"sound sort of sucks but i don ' t use it for music .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'sucks', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The location and ambience is Ok but the food is what makes up for it .\n->The location and ambience is Ok but the food is what makes up for it .\n[{'aspect': 'location', 'opinion': 'Ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'Ok', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'makes up', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: liked it when it was working , but it ' s a paperweight now .\n->liked it when it was working , but it ' s a paperweight now .\n[{'aspect': 'it', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: android aspect is no longer ` ` beta , ` ` and gotten / still getting better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nandroid aspect is no longer ` ` beta , ` ` and gotten / still getting better .\n->", + "output": "{\"text\": \"android aspect is no longer ` ` beta , ` ` and gotten / still getting better .\", \"labels\": \"[{'aspect': 'android', 'opinion': 'better', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff is excellent , special : that girl behind the bar , european chic .\n->The staff is excellent , special : that girl behind the bar , european chic .\n[{'aspect': 'staff', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bar', 'opinion': 'special', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: i ' m glad i got this .\n->i ' m glad i got this .\n[{'aspect': 'NULL', 'opinion': 'glad', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the keyboard and display quality have always been asus strengths in my experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard and display quality have always been asus strengths in my experience .\n->", + "output": "{\"text\": \"the keyboard and display quality have always been asus strengths in my experience .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'strengths', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you do n ' t need a full blown laptop this is a good choice .\n->if you do n ' t need a full blown laptop this is a good choice .\n[{'aspect': 'laptop', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: our server was very helpful and friendly .\n->our server was very helpful and friendly .\n[{'aspect': 'server', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'server', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the touchpad is not the best , but it is not that terrible either .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchpad is not the best , but it is not that terrible either .\n->", + "output": "{\"text\": \"the touchpad is not the best , but it is not that terrible either .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'not the best', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'not that terrible', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food is great .\n->food is great .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: so rushing us out was absolutely unnecessary !\n->so rushing us out was absolutely unnecessary !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i am not fond of touchpads anyway , so probably not the best one to judge them .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am not fond of touchpads anyway , so probably not the best one to judge them .\n->", + "output": "{\"text\": \"i am not fond of touchpads anyway , so probably not the best one to judge them .\", \"labels\": \"[{'aspect': 'touchpads', 'opinion': 'not fond of', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->the svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'must', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i love my laptop but the battery life is the worst i ' ve ever had on a laptop .\n->i love my laptop but the battery life is the worst i ' ve ever had on a laptop .\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'worst', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}]\ntext: but as it stands still makes a great mobile device with excellent battery life to boot .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut as it stands still makes a great mobile device with excellent battery life to boot .\n->", + "output": "{\"text\": \"but as it stands still makes a great mobile device with excellent battery life to boot .\", \"labels\": \"[{'aspect': 'mobile device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n->I have been coming here for years and have nothing but good things to say about the service and the great staff at La Lanterna .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i love my c302 !\n->i love my c302 !\n[{'aspect': 'c302', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this chromebook is great system that is lightweight , has excellent battery life , and offers a fantastic keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook is great system that is lightweight , has excellent battery life , and offers a fantastic keyboard .\n->", + "output": "{\"text\": \"this chromebook is great system that is lightweight , has excellent battery life , and offers a fantastic keyboard .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'system', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'system', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sadly , apple is moving into fashion - - your laptop is no longer a performing athlete .\n->sadly , apple is moving into fashion - - your laptop is no longer a performing athlete .\n[{'aspect': 'apple', 'opinion': 'sadly', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: it can not .\n->it can not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i have not fiddled around much with the touchscreen yet , but it seems very responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have not fiddled around much with the touchscreen yet , but it seems very responsive .\n->", + "output": "{\"text\": \"i have not fiddled around much with the touchscreen yet , but it seems very responsive .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: again , no apology , no is there anything else i can get you , no can i get you a drink to make up for it , nothing ! ! ! !\n->again , no apology , no is there anything else i can get you , no can i get you a drink to make up for it , nothing ! ! ! !\n[{'aspect': 'NULL', 'opinion': 'nothing', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n->ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\n->", + "output": "{\"text\": \"sleek , great keyboard , convertible laptop with great battery life , nice display and android app support after running a system update .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'display', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'android app support', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great bagels made the old-fashioned way .\n->Great bagels made the old-fashioned way .\n[{'aspect': 'bagels', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The ambience was nice , but service was n't so great .\n->The ambience was nice , but service was n't so great .\n[{'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': \"was n't so great\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: oh , and this charges fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noh , and this charges fast .\n->", + "output": "{\"text\": \"oh , and this charges fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cheese plate is a varied delight and great bargain at $ 10 .\n->Cheese plate is a varied delight and great bargain at $ 10 .\n[{'aspect': 'Cheese plate', 'opinion': 'varied delight', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cheese plate', 'opinion': 'great bargain', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n->the dessert ( we had a pear torte ) was good - but , once again , the staff was unable to provide appropriate drink suggestions .\n[{'aspect': 'pear torte', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'staff', 'opinion': 'unable to provide', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: one of the things that drew me to the c302 was the convertible form - factor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \none of the things that drew me to the c302 was the convertible form - factor .\n->", + "output": "{\"text\": \"one of the things that drew me to the c302 was the convertible form - factor .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: taxan delicious !\n->taxan delicious !\n[{'aspect': 'taxan', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: and yes Dal Bukhara is so dam good and so are all the kababs .\n->and yes Dal Bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Dal Bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this puppy starts up quickly and has done everything i ' ve asked it to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis puppy starts up quickly and has done everything i ' ve asked it to .\n->", + "output": "{\"text\": \"this puppy starts up quickly and has done everything i ' ve asked it to .\", \"labels\": \"[{'aspect': 'puppy', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'starts up', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Not impressed with the food .\n->Not impressed with the food .\n[{'aspect': 'food', 'opinion': 'Not impressed', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n->However , in the summer of 2003 , it seems the management has changed and the great big door has been replaced for a glass front ridding itself of the dark romantic getup .\n[{'aspect': 'management', 'opinion': 'changed', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'door', 'opinion': 'great big', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it ' s very crisp and responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s very crisp and responsive .\n->", + "output": "{\"text\": \"it ' s very crisp and responsive .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n->i liked the look , the speed , and the quality of the device but two had problems and not giving it a 3rd try .\n[{'aspect': 'look', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'speed', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'liked', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: chrome has come a long way to be sure and in its optimized avatar on this system its very snappy .\n->chrome has come a long way to be sure and in its optimized avatar on this system its very snappy .\n[{'aspect': 'chrome', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: the speakers are surprisingly decent .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speakers are surprisingly decent .\n->", + "output": "{\"text\": \"the speakers are surprisingly decent .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'decent', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m partial to the gnocchi .\n->i ' m partial to the gnocchi .\n[{'aspect': 'gnocchi', 'opinion': 'partial', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it has way more than i will ever need because all i do is check my email and facebook , but it is crazy fast .\n->it has way more than i will ever need because all i do is check my email and facebook , but it is crazy fast .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the 100gb google drive offer is great , too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 100gb google drive offer is great , too .\n->", + "output": "{\"text\": \"the 100gb google drive offer is great , too .\", \"labels\": \"[{'aspect': '100gb google drive offer', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: cpu and gpu are good , ram is good and i like the keyboard .\n->cpu and gpu are good , ram is good and i like the keyboard .\n[{'aspect': 'cpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'gpu', 'opinion': 'good', 'polarity': 'positive', 'category': 'GRAPHICS#GENERAL'}, {'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: it ' s not real fast and it doesn ' t have a lot of storage .\n->it ' s not real fast and it doesn ' t have a lot of storage .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#GENERAL'}]\ntext: i ' m more about personality than looks , but this little thing is a looker , too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m more about personality than looks , but this little thing is a looker , too .\n->", + "output": "{\"text\": \"i ' m more about personality than looks , but this little thing is a looker , too .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was very good , but not what I would consider out of this world .\n->Food was very good , but not what I would consider out of this world .\n[{'aspect': 'Food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n->Its a nice quiet location to go eat a good meal , relax , be able to talk and have a very good time .\n[{'aspect': 'location', 'opinion': 'nice quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'meal', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: its fast , battery lasts for many hours , all the apps work well , the wifi on this is great , and the screen resolution is really great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits fast , battery lasts for many hours , all the apps work well , the wifi on this is great , and the screen resolution is really great .\n->", + "output": "{\"text\": \"its fast , battery lasts for many hours , all the apps work well , the wifi on this is great , and the screen resolution is really great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'wifi', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#CONNECTIVITY'}, {'aspect': 'screen resolution', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Anyway , the food is good , the price is right and they have a decent wine list .\n->Anyway , the food is good , the price is right and they have a decent wine list .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: at first i was totally stoked on this chromebook .\n->at first i was totally stoked on this chromebook .\n[{'aspect': 'chromebook', 'opinion': 'stoked', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\n->", + "output": "{\"text\": \"for the battery , i ' ve used it to watch espn for over four hours and the battery indicator it still had 5 hours of battery left .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now , the headphone jack produces low volume at 10 percent capacity .\n->now , the headphone jack produces low volume at 10 percent capacity .\n[{'aspect': 'headphone jack', 'opinion': 'low', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: when it works it ' s a great device .\n->when it works it ' s a great device .\n[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: nice back lit keyboard , i like the screen , but in the end , it is the software that counts .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice back lit keyboard , i like the screen , but in the end , it is the software that counts .\n->", + "output": "{\"text\": \"nice back lit keyboard , i like the screen , but in the end , it is the software that counts .\", \"labels\": \"[{'aspect': 'back lit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'screen', 'opinion': 'like', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'software', 'opinion': 'counts', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the pizza was great .\n->the pizza was great .\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\n->", + "output": "{\"text\": \"i ' m a decent size ( 5 ' 11 ` ` 230lb ) with stubby fingers and proportionate hands .\", \"labels\": \"[{'aspect': 'size', 'opinion': 'decent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overall , excellent restaurant !\n->overall , excellent restaurant !\n[{'aspect': 'restaurant', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: this does exactly what i need , writing on google docs .\n->this does exactly what i need , writing on google docs .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: the 302 would be just about the perfect chromebook if it had smaller bezels , though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 302 would be just about the perfect chromebook if it had smaller bezels , though .\n->", + "output": "{\"text\": \"the 302 would be just about the perfect chromebook if it had smaller bezels , though .\", \"labels\": \"[{'aspect': '302', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'bezels', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the touchscreen is great though and feels very intuitive .\n->the touchscreen is great though and feels very intuitive .\n[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: everything is plastic .\n->everything is plastic .\n[{'aspect': 'NULL', 'opinion': 'plastic', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: it ' s an easy one for me : the 302 offers a better experience overall and it ' s not close .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s an easy one for me : the 302 offers a better experience overall and it ' s not close .\n->", + "output": "{\"text\": \"it ' s an easy one for me : the 302 offers a better experience overall and it ' s not close .\", \"labels\": \"[{'aspect': '302', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': '302', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is exactly what i need and nothing more .\n->this is exactly what i need and nothing more .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i love how much you can customize this and also it is pretty speedy !\n->i love how much you can customize this and also it is pretty speedy !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: love the feel and quality of this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the feel and quality of this chromebook .\n->", + "output": "{\"text\": \"love the feel and quality of this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n->i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n[{'aspect': 'machine', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: Good for casual dinner with jeans and sneakers .\n->Good for casual dinner with jeans and sneakers .\n[{'aspect': 'casual dinner', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it runs android apps with power and quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit runs android apps with power and quality .\n->", + "output": "{\"text\": \"it runs android apps with power and quality .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'with power and quality', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n->My friends and I experienced amazing cheese and a delicious , new summer menu at Artisanal last night .\n[{'aspect': 'cheese', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'new', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n->the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n[{'aspect': '1080p screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': '1080p screen', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'hinge', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: touch screen is really responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntouch screen is really responsive .\n->", + "output": "{\"text\": \"touch screen is really responsive .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - physically it is appealing looking .\n->- physically it is appealing looking .\n[{'aspect': 'NULL', 'opinion': 'appealing', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n->i contacted asus support twice so far , however , they are not able to provide a solution to choppy video .\n[{'aspect': 'asus support', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: screen looks great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen looks great .\n->", + "output": "{\"text\": \"screen looks great .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the c302 m5 ( the best processor i could find so far , is a very good chromebook .\n->the c302 m5 ( the best processor i could find so far , is a very good chromebook .\n[{'aspect': 'm5', 'opinion': 'best', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'processor', 'opinion': 'best', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'c302', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n->i am very much in the causal gamer category , sims 4 being probably the most taxing ( on the system ) game i will play , and it seems to be running it just fine .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: keyboard feels good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard feels good .\n->", + "output": "{\"text\": \"keyboard feels good .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and yes dal bukhara is so dam good and so are all the kababs .\n->and yes dal bukhara is so dam good and so are all the kababs .\n[{'aspect': 'kababs', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'dal bukhara', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n->the responses in the faq are wrong , this does not have 16 gb of ram , almost gave it a 1 star bexause of this reason .\n[{'aspect': 'responses', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: usb - c connectors on both sides can both charge , making the power cord location an option .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nusb - c connectors on both sides can both charge , making the power cord location an option .\n->", + "output": "{\"text\": \"usb - c connectors on both sides can both charge , making the power cord location an option .\", \"labels\": \"[{'aspect': 'usb - c connectors', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mmmmmmmmmmmmmmm so delicious\n->mmmmmmmmmmmmmmm so delicious\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The Steak Tartare is a great bet , they fix it for you at the table .\n->The Steak Tartare is a great bet , they fix it for you at the table .\n[{'aspect': 'Steak Tartare', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: edges of sides can be a little sharp .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nedges of sides can be a little sharp .\n->", + "output": "{\"text\": \"edges of sides can be a little sharp .\", \"labels\": \"[{'aspect': 'edges', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n->for the backlighting , he sent me some zip files for atk and chipset which i don ' t know how to use and which i shouldn ' t have to use .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the service is good and the resturant is clean .\n->the service is good and the resturant is clean .\n[{'aspect': 'service', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'resturant', 'opinion': 'clean', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\n->", + "output": "{\"text\": \"the keypad is really nice and i like the back light on it although i didn ' t think it was anything i really wanted .\", \"labels\": \"[{'aspect': 'keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'back light', 'opinion': 'like', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s complementary , not revolutionary , which is much more intuitive and useful .\n->it ' s complementary , not revolutionary , which is much more intuitive and useful .\n[{'aspect': 'NULL', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'useful', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n->Pizza - the only pizza in NYC that should not have additional toppings - the crust tastes like the best , freshly baked bread !\n[{'aspect': 'crust', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'bread', 'opinion': 'freshly baked', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the color of everything is so very much brighter and clearer it makes the extra cost is worth more for just that .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe color of everything is so very much brighter and clearer it makes the extra cost is worth more for just that .\n->", + "output": "{\"text\": \"the color of everything is so very much brighter and clearer it makes the extra cost is worth more for just that .\", \"labels\": \"[{'aspect': 'color', 'opinion': 'brighter', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'color', 'opinion': 'clearer', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'color', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: im thrilled with my mac .\n->im thrilled with my mac .\n[{'aspect': 'mac', 'opinion': 'thrilled', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: first off the chromebook itself is excellent , one of the few budget models with 1080p and a long battery life .\n->first off the chromebook itself is excellent , one of the few budget models with 1080p and a long battery life .\n[{'aspect': 'chromebook', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the whole thing seems very sturdy but not stiff .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe whole thing seems very sturdy but not stiff .\n->", + "output": "{\"text\": \"the whole thing seems very sturdy but not stiff .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'not stiff', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google is very concerned about arc to chromeos connections for security , etc etc .\n->google is very concerned about arc to chromeos connections for security , etc etc .\n[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n->pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n[{'aspect': 'flip functions', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: movies look good and the sound is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmovies look good and the sound is great .\n->", + "output": "{\"text\": \"movies look good and the sound is great .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'great', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is excellent and our students love them !\n->battery life is excellent and our students love them !\n[{'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: the second one arrived , i had it from end of may june , to september started to notice the a key was a bit unresponsive at times .\n->the second one arrived , i had it from end of may june , to september started to notice the a key was a bit unresponsive at times .\n[{'aspect': 'a key', 'opinion': 'unresponsive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: everything works fast and smooth .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything works fast and smooth .\n->", + "output": "{\"text\": \"everything works fast and smooth .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is excellent and our students love them !\n->battery life is excellent and our students love them !\n[{'aspect': 'battery life', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\nExample:\ntext: Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n->Planet Thailand has always been a hit with me , I go there usually for the sushi , which is great , the thai food is excellent too .\n[{'aspect': 'sushi', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'thai food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the asus is powerful enough to stream video , i watch tv on it , do all my google trainer work with it , and all my daily work too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe asus is powerful enough to stream video , i watch tv on it , do all my google trainer work with it , and all my daily work too .\n->", + "output": "{\"text\": \"the asus is powerful enough to stream video , i watch tv on it , do all my google trainer work with it , and all my daily work too .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The characters really make for an enjoyable experience .\n->The characters really make for an enjoyable experience .\n[{'aspect': 'characters', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen is crisp and clear , easy to set up .\n->the screen is crisp and clear , easy to set up .\n[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\ntext: the fact that i can flip it into tablet is cool , the audio is decent , i love the feel of the keyboard , i didn ' t think touch screen was a big deal , but i use it all the time now with joy , ya .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fact that i can flip it into tablet is cool , the audio is decent , i love the feel of the keyboard , i didn ' t think touch screen was a big deal , but i use it all the time now with joy , ya .\n->", + "output": "{\"text\": \"the fact that i can flip it into tablet is cool , the audio is decent , i love the feel of the keyboard , i didn ' t think touch screen was a big deal , but i use it all the time now with joy , ya .\", \"labels\": \"[{'aspect': 'tablet', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touch screen', 'opinion': 'joy', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now the hole thing crashed out of nowhere and i ' m going to lose everything i had on it .\n->now the hole thing crashed out of nowhere and i ' m going to lose everything i had on it .\n[{'aspect': 'NULL', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: i ' m satisfied with the product .\n->i ' m satisfied with the product .\n[{'aspect': 'product', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\n->", + "output": "{\"text\": \"i did my research and found a combination of features i wanted : quality screen , faster processor , google play store compatibility , quality keyboard with a solid feel and the option to use as a tablet .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'quality', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'processor', 'opinion': 'faster', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'google play store', 'opinion': 'compatibility', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food is great and inexpensive .\n->Food is great and inexpensive .\n[{'aspect': 'Food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Food', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - timeout on keyboard backlight not adjustable .\n->- timeout on keyboard backlight not adjustable .\n[{'aspect': 'keyboard', 'opinion': 'not adjustable', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}]\ntext: i will admit that i needed to get used to a combination of keystrokes and screen touches , but the touch screen is both sensitive and accurate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni will admit that i needed to get used to a combination of keystrokes and screen touches , but the touch screen is both sensitive and accurate .\n->", + "output": "{\"text\": \"i will admit that i needed to get used to a combination of keystrokes and screen touches , but the touch screen is both sensitive and accurate .\", \"labels\": \"[{'aspect': 'keystrokes', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'screen touches', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'touch screen', 'opinion': 'sensitive', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'touch screen', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: your mileage may vary but it ' s been a headache for me since i bought it .\n->your mileage may vary but it ' s been a headache for me since i bought it .\n[{'aspect': 'NULL', 'opinion': 'headache', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: which of course is not real kobe but wagyu beef .\n->which of course is not real kobe but wagyu beef .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: asus has definitely broadened the flexibility and usefulness of this niche .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nasus has definitely broadened the flexibility and usefulness of this niche .\n->", + "output": "{\"text\": \"asus has definitely broadened the flexibility and usefulness of this niche .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: But the main hit was the whole grilled fish .\n->But the main hit was the whole grilled fish .\n[{'aspect': 'whole grilled fish', 'opinion': 'hit', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: first of all , the battery life on it is insane .\n->first of all , the battery life on it is insane .\n[{'aspect': 'battery life', 'opinion': 'insane', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: great product !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat product !\n->", + "output": "{\"text\": \"great product !\", \"labels\": \"[{'aspect': 'product', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\n->Interesting other dishes for a change include chicken in curry sauce and salmon caserole .\n[{'aspect': 'dishes', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon caserole', 'opinion': 'Interesting', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Everything was wonderful ; food , drinks , staff , mileau .\n->Everything was wonderful ; food , drinks , staff , mileau .\n[{'aspect': 'food', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'drinks', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'mileau', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the asus chromebook flip 302 fit the bill .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe asus chromebook flip 302 fit the bill .\n->", + "output": "{\"text\": \"the asus chromebook flip 302 fit the bill .\", \"labels\": \"[{'aspect': 'asus chromebook flip 302', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n->The place is a little tight and on a cold day , the seating by the entranceway can be pretty drafty .\n[{'aspect': 'seating', 'opinion': 'drafty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'tight', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: As always we had a great glass of wine while we waited .\n->As always we had a great glass of wine while we waited .\n[{'aspect': 'glass of wine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' ve been using it for almost three weeks now and it has not let me down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been using it for almost three weeks now and it has not let me down .\n->", + "output": "{\"text\": \"i ' ve been using it for almost three weeks now and it has not let me down .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n->The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes .\n[{'aspect': 'food', 'opinion': 'diamond', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'balance of herbs and tomatoes', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the sad part is i truly do like acer products , but this made me rethink this purchase .\n->the sad part is i truly do like acer products , but this made me rethink this purchase .\n[{'aspect': 'acer products', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'acer products', 'opinion': 'sad', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: this has been an upgrade from me as the 4 gb of memory has made a difference in many ways , but most of all with long documents .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis has been an upgrade from me as the 4 gb of memory has made a difference in many ways , but most of all with long documents .\n->", + "output": "{\"text\": \"this has been an upgrade from me as the 4 gb of memory has made a difference in many ways , but most of all with long documents .\", \"labels\": \"[{'aspect': '4 gb of memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we could have made a meal of the yummy dumplings from the dumpling menu .\n->we could have made a meal of the yummy dumplings from the dumpling menu .\n[{'aspect': 'dumplings', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: i had hoped that doubling the ram would increase performance a bit , but it seems to run exactly the same .\n->i had hoped that doubling the ram would increase performance a bit , but it seems to run exactly the same .\n[{'aspect': 'ram', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: it ' s performance is quite zippy and the screen is very sharp and bright .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s performance is quite zippy and the screen is very sharp and bright .\n->", + "output": "{\"text\": \"it ' s performance is quite zippy and the screen is very sharp and bright .\", \"labels\": \"[{'aspect': 'performance', 'opinion': 'zippy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: If we were to move from the upper east side , we would genuinely miss this restaurant .\n->If we were to move from the upper east side , we would genuinely miss this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n->The plain slice is great and if you get toppings , the whole slice is topped with them , not sparsely sprinkled on like some places .\n[{'aspect': 'plain slice', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: workmanship is top notch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nworkmanship is top notch .\n->", + "output": "{\"text\": \"workmanship is top notch .\", \"labels\": \"[{'aspect': 'workmanship', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like cafe noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n->i like cafe noir dont get me wrong , it is jsut that the people who work there are evil and incompetent ! !\n[{'aspect': 'people', 'opinion': 'evil', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'people', 'opinion': 'incompetent', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'cafe noir', 'opinion': 'like', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Food was OK - fish was cooked well .\n->Food was OK - fish was cooked well .\n[{'aspect': 'Food', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'NULL'}]\ntext: bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\n->", + "output": "{\"text\": \"bought this because of the touch screen and specs capable of running android ( and it ' s a supported model ) .\", \"labels\": \"[{'aspect': 'specs', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as to my comment about the food , no apology or acknowledgment was made .\n->as to my comment about the food , no apology or acknowledgment was made .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\n->The dosas are skimpy , unattractive and drip with grease , and personally I 'd drink popcorn topping before I 'd eat another one of these .\n[{'aspect': 'dosas', 'opinion': 'skimpy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dosas', 'opinion': 'unattractive', 'polarity': 'negative', 'category': 'NULL'}]\ntext: a little disappointed with the android experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na little disappointed with the android experience .\n->", + "output": "{\"text\": \"a little disappointed with the android experience .\", \"labels\": \"[{'aspect': 'android experience', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i heard the lobster roll was excellent .\n->i heard the lobster roll was excellent .\n[{'aspect': 'lobster roll', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: my macbook pro 15 \u201d can overheating when some do it !\n->my macbook pro 15 \u201d can overheating when some do it !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: as a chrome book it is excellent , but android support is unsatisfying .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas a chrome book it is excellent , but android support is unsatisfying .\n->", + "output": "{\"text\": \"as a chrome book it is excellent , but android support is unsatisfying .\", \"labels\": \"[{'aspect': 'chrome book', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'android support', 'opinion': 'unsatisfying', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so a for the computer , f for the charger , and a d for the customer support .\n->so a for the computer , f for the charger , and a d for the customer support .\n[{'aspect': 'computer', 'opinion': 'a', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'charger', 'opinion': 'f', 'polarity': 'negative', 'category': 'POWER_SUPPLY#GENERAL'}, {'aspect': 'customer support', 'opinion': 'd', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: i am returning immediately , no patience for this .\n->i am returning immediately , no patience for this .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this chromebook is awesome .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook is awesome .\n->", + "output": "{\"text\": \"this chromebook is awesome .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: google updates are super fast .\n->google updates are super fast .\n[{'aspect': 'google updates', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: on a hot day it was fabulous to stop in and enjoy lunch .\n->on a hot day it was fabulous to stop in and enjoy lunch .\n[{'aspect': 'NULL', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have a lot of asus products and am happy with their service and quality of product , and this is no exception .\n->", + "output": "{\"text\": \"i have a lot of asus products and am happy with their service and quality of product , and this is no exception .\", \"labels\": \"[{'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'asus products', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my husbands birthday and my sons was not as it was intended . . . and we drove two hours to spend too much money to be treated terribly !\n->my husbands birthday and my sons was not as it was intended . . . and we drove two hours to spend too much money to be treated terribly !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'terribly', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n->thanks to its ssd , it boots windows 10 pretty fast , bloatware is kept to a minimum ( please uninstall norton before doing anything else .\n[{'aspect': 'windows 10', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the tablet here works well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe tablet here works well .\n->", + "output": "{\"text\": \"the tablet here works well .\", \"labels\": \"[{'aspect': 'tablet', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: will never buy an asus product again .\n->will never buy an asus product again .\n[{'aspect': 'asus product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it just stopped working in the middle of my paper i was writing .\n->it just stopped working in the middle of my paper i was writing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the touchscreen works well , the apps generally work , the performance is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchscreen works well , the apps generally work , the performance is good .\n->", + "output": "{\"text\": \"the touchscreen works well , the apps generally work , the performance is good .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'well', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: mmmmmmmmmmmmmmm so delicious\n->mmmmmmmmmmmmmmm so delicious\n[{'aspect': 'NULL', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: otherwise , it has everything i can / might / could love .\n->otherwise , it has everything i can / might / could love .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: screen resolution is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen resolution is good .\n->", + "output": "{\"text\": \"screen resolution is good .\", \"labels\": \"[{'aspect': 'screen resolution', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Good , dark atmosphere and the music is a nice touch .\n->Good , dark atmosphere and the music is a nice touch .\n[{'aspect': 'atmosphere', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'music', 'opinion': 'nice touch', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food here does a great service to the name ( Cantonese that is ... ) .\n->The food here does a great service to the name ( Cantonese that is ... ) .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Cantonese', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the mousepad is functional but really doesnt get in the way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe mousepad is functional but really doesnt get in the way .\n->", + "output": "{\"text\": \"the mousepad is functional but really doesnt get in the way .\", \"labels\": \"[{'aspect': 'mousepad', 'opinion': 'functional', 'polarity': 'neutral', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n->the reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: but the service was a bit slow .\n->but the service was a bit slow .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: basically , it is great for work and media as long as you dont need other proprietary programs to do your work .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbasically , it is great for work and media as long as you dont need other proprietary programs to do your work .\n->", + "output": "{\"text\": \"basically , it is great for work and media as long as you dont need other proprietary programs to do your work .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This tiny restaurant is as cozy as it gets , with that certain Parisian flair .\n->This tiny restaurant is as cozy as it gets , with that certain Parisian flair .\n[{'aspect': 'restaurant', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: 2 ) the battery was pretty limited .\n->2 ) the battery was pretty limited .\n[{'aspect': 'battery', 'opinion': 'limited', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i absolutely love this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely love this chromebook .\n->", + "output": "{\"text\": \"i absolutely love this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n->we were offered water for the table but were not told the voss bottles of water were $ 8 a piece .\n[{'aspect': 'voss bottles of water', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: The fish was really , really fresh .\n->The fish was really , really fresh .\n[{'aspect': 'fish', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: looks great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlooks great .\n->", + "output": "{\"text\": \"looks great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: now the start up is failing .\n->now the start up is failing .\n[{'aspect': 'start up', 'opinion': 'failing', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: google is very concerned about arc to chromeos connections for security , etc etc .\n->google is very concerned about arc to chromeos connections for security , etc etc .\n[{'aspect': 'google', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'COMPANY#GENERAL'}]\ntext: very portable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery portable .\n->", + "output": "{\"text\": \"very portable .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it 's the only place you can get yummy authentic japanese comfort food .\n->it 's the only place you can get yummy authentic japanese comfort food .\n[{'aspect': 'japanese comfort food', 'opinion': 'yummy authentic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the service was excellent - friendly and attentive .\n->the service was excellent - friendly and attentive .\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: only problem i have had , which was from the moment i started using it is the audio .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nonly problem i have had , which was from the moment i started using it is the audio .\n->", + "output": "{\"text\": \"only problem i have had , which was from the moment i started using it is the audio .\", \"labels\": \"[{'aspect': 'audio', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was using it and the screen started to flicker and then went completely dim .\n->i was using it and the screen started to flicker and then went completely dim .\n[{'aspect': 'screen', 'opinion': 'flicker', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: The food is usually good but it certainly is n't a relaxing place to go .\n->The food is usually good but it certainly is n't a relaxing place to go .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': \"is n't a relaxing\", 'polarity': 'negative', 'category': 'NULL'}]\ntext: first the volume does not get very loud .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfirst the volume does not get very loud .\n->", + "output": "{\"text\": \"first the volume does not get very loud .\", \"labels\": \"[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: gates and this machine allows me to do this .\n->gates and this machine allows me to do this .\n[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: The entree was bland and small , dessert was not inspired .\n->The entree was bland and small , dessert was not inspired .\n[{'aspect': 'entree', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'entree', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'not inspired', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i ' ve had this for about 3 weeks , and i ' m loving it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had this for about 3 weeks , and i ' m loving it .\n->", + "output": "{\"text\": \"i ' ve had this for about 3 weeks , and i ' m loving it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n->i do like it , just now that i had time to use it in preparing for my exams , the following was observed : -\n[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Toons has recently been redone , so it 's now a very attractive space .\n->Toons has recently been redone , so it 's now a very attractive space .\n[{'aspect': 'Toons', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen is a little smaller , but it ' s touch and even higher resolution .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is a little smaller , but it ' s touch and even higher resolution .\n->", + "output": "{\"text\": \"the screen is a little smaller , but it ' s touch and even higher resolution .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'smaller', 'polarity': 'neutral', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'touch', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'higher', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Their calzones are horrific , bad , vomit-inducing , YUCK .\n->Their calzones are horrific , bad , vomit-inducing , YUCK .\n[{'aspect': 'calzones', 'opinion': 'horrific', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'bad', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'vomit-inducing', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'calzones', 'opinion': 'YUCK', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the asus chromebook flip is the best one i have owned .\n->the asus chromebook flip is the best one i have owned .\n[{'aspect': 'asus chromebook flip', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the keyboard is great , and it ' s backlit !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is great , and it ' s backlit !\n->", + "output": "{\"text\": \"the keyboard is great , and it ' s backlit !\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They did not have mayonnaise , forgot our toast , left out ingredients ( ie cheese in an omelet ) , below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it .\n->They did not have mayonnaise , forgot our toast , left out ingredients ( ie cheese in an omelet ) , below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it .\n[{'aspect': 'toast', 'opinion': 'forgot', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'bacon', 'opinion': 'over cooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'cheese', 'opinion': 'left out', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'left out', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'plate', 'opinion': 'over cooked', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'omelet', 'opinion': 'left out', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: The place 's decor and hidden bathrooms made for a good laugh .\n->The place 's decor and hidden bathrooms made for a good laugh .\n[{'aspect': 'decor', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hidden bathrooms', 'opinion': 'good laugh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the build quality is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is fantastic .\n->", + "output": "{\"text\": \"the build quality is fantastic .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We 've been to Grocery three times and not once has an item on the menu disappointed .\n->We 've been to Grocery three times and not once has an item on the menu disappointed .\n[{'aspect': 'menu', 'opinion': 'disappointed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Solid wine list , knowledgeable staff , friendly owners and an adventurous , ever-changing menu keep us coming back .\n->Solid wine list , knowledgeable staff , friendly owners and an adventurous , ever-changing menu keep us coming back .\n[{'aspect': 'wine list', 'opinion': 'Solid', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'knowledgeable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owners', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'adventurous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'ever-changing', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it starts up fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit starts up fast .\n->", + "output": "{\"text\": \"it starts up fast .\", \"labels\": \"[{'aspect': 'starts up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i am beyond impressed with this little machine , i would absolutly buy this again !\n->i am beyond impressed with this little machine , i would absolutly buy this again !\n[{'aspect': 'machine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the m3 processor is fast and fluid .\n->the m3 processor is fast and fluid .\n[{'aspect': 'm3 processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'm3 processor', 'opinion': 'fluid', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: the second issue i have with this machine so far is that if you have several gmail accounts , it gets really confusing to find your files , where they go , where they were saved .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe second issue i have with this machine so far is that if you have several gmail accounts , it gets really confusing to find your files , where they go , where they were saved .\n->", + "output": "{\"text\": \"the second issue i have with this machine so far is that if you have several gmail accounts , it gets really confusing to find your files , where they go , where they were saved .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'gmail accounts', 'opinion': 'confusing', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: an unexpected benefit for me has been the iphone / mbp integration .\n->an unexpected benefit for me has been the iphone / mbp integration .\n[{'aspect': 'iphone / mbp integration', 'opinion': 'benefit', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: by the time we finished our dinner we still had not received one beverage nor hooka !\n->by the time we finished our dinner we still had not received one beverage nor hooka !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n->", + "output": "{\"text\": \"and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\", \"labels\": \"[{'aspect': 'google environment', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n->admittedly some nights inside the restaurant were rather warm , but the open kitchen is part of the charm .\n[{'aspect': 'open kitchen', 'opinion': 'charm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'restaurant', 'opinion': 'warm', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: i had the duck breast special on my last visit and it was incredible .\n->i had the duck breast special on my last visit and it was incredible .\n[{'aspect': 'duck breast special', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: for the price , 5 stars .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor the price , 5 stars .\n->", + "output": "{\"text\": \"for the price , 5 stars .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the shrimp scampi was excellent and the antipasti were plentiful .\n->the shrimp scampi was excellent and the antipasti were plentiful .\n[{'aspect': 'shrimp scampi', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'antipasti', 'opinion': 'plentiful', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: however , it ' s the service that leaves a bad taste in my mouth .\n->however , it ' s the service that leaves a bad taste in my mouth .\n[{'aspect': 'service', 'opinion': 'bad taste', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: very impressive design .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery impressive design .\n->", + "output": "{\"text\": \"very impressive design .\", \"labels\": \"[{'aspect': 'design', 'opinion': 'impressive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We took advanatage of the half price sushi deal on saturday so it was well worth it .\n->We took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if you are looking for a good quality , cheap eats - this is the place .\n->if you are looking for a good quality , cheap eats - this is the place .\n[{'aspect': 'eats', 'opinion': 'good quality', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'eats', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: nice build quality , very fast and beautiful display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice build quality , very fast and beautiful display .\n->", + "output": "{\"text\": \"nice build quality , very fast and beautiful display .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'display', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is my first chromebook , and i ' m absolutely loving it .\n->this is my first chromebook , and i ' m absolutely loving it .\n[{'aspect': 'this', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: and it was quick which is very important .\n->and it was quick which is very important .\n[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'important', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i work with an it company and we ' re testing an all android environment and it ' s working out pretty well so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni work with an it company and we ' re testing an all android environment and it ' s working out pretty well so far .\n->", + "output": "{\"text\": \"i work with an it company and we ' re testing an all android environment and it ' s working out pretty well so far .\", \"labels\": \"[{'aspect': 'android environment', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n->i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: I was in love with Pongsri on 48th , but compared to Suan it is slow in service and overpriced .\n->I was in love with Pongsri on 48th , but compared to Suan it is slow in service and overpriced .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it stays very cool to the touch and the performance has really been amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit stays very cool to the touch and the performance has really been amazing .\n->", + "output": "{\"text\": \"it stays very cool to the touch and the performance has really been amazing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: overpriced low quality product .\n->overpriced low quality product .\n[{'aspect': 'product', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'low', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: i hope it is fixed this time .\n->i hope it is fixed this time .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' m very very happy with this fast lightweight convertible chromebook and my search has concluded .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m very very happy with this fast lightweight convertible chromebook and my search has concluded .\n->", + "output": "{\"text\": \"i ' m very very happy with this fast lightweight convertible chromebook and my search has concluded .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good quality .\n->good quality .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\nExample:\ntext: told us to sit anywhere , and when we sat he said the table was reserved .\n->told us to sit anywhere , and when we sat he said the table was reserved .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: has everything i was looking for , such as touch screen capability , tablet convertible , small bezel , lightweight and small footprint , * backlit * keyboard , not one but two * * usb - c * * ports , octane 2 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhas everything i was looking for , such as touch screen capability , tablet convertible , small bezel , lightweight and small footprint , * backlit * keyboard , not one but two * * usb - c * * ports , octane 2 .\n->", + "output": "{\"text\": \"has everything i was looking for , such as touch screen capability , tablet convertible , small bezel , lightweight and small footprint , * backlit * keyboard , not one but two * * usb - c * * ports , octane 2 .\", \"labels\": \"[{'aspect': 'touch screen capability', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'tablet', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'bezel', 'opinion': 'small', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'footprint', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'footprint', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '* backlit * keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': '* * usb - c * * ports', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n->the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screen', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\nExample:\ntext: Pizza is terrific , as is homemade pasta .\n->Pizza is terrific , as is homemade pasta .\n[{'aspect': 'Pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'homemade pasta', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i love chromebooks and have been using them before they were available to the public .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love chromebooks and have been using them before they were available to the public .\n->", + "output": "{\"text\": \"i love chromebooks and have been using them before they were available to the public .\", \"labels\": \"[{'aspect': 'chromebooks', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: screen is bright , doesn ' t feel heavy .\n->screen is bright , doesn ' t feel heavy .\n[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': \"' t feel heavy\", 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: it ' s also very lightweight , so it ' s easy to carry around .\n->it ' s also very lightweight , so it ' s easy to carry around .\n[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: the asus chromebook flip is the best one i have owned .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe asus chromebook flip is the best one i have owned .\n->", + "output": "{\"text\": \"the asus chromebook flip is the best one i have owned .\", \"labels\": \"[{'aspect': 'asus chromebook flip', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we took advanatage of the half price sushi deal on saturday so it was well worth it .\n->we took advanatage of the half price sushi deal on saturday so it was well worth it .\n[{'aspect': 'half price sushi deal', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this is a dreadful little piece of machinery .\n->this is a dreadful little piece of machinery .\n[{'aspect': 'machinery', 'opinion': 'dreadful', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it has a quality construction making it feel like a much more expensive laptop and the performance is perfect for those who use a chromebook for everyday computing and entertainment .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit has a quality construction making it feel like a much more expensive laptop and the performance is perfect for those who use a chromebook for everyday computing and entertainment .\n->", + "output": "{\"text\": \"it has a quality construction making it feel like a much more expensive laptop and the performance is perfect for those who use a chromebook for everyday computing and entertainment .\", \"labels\": \"[{'aspect': 'performance', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: battery life is horrible though .\n->battery life is horrible though .\n[{'aspect': 'battery life', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: This is some really good , inexpensive sushi .\n->This is some really good , inexpensive sushi .\n[{'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'inexpensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it doubles as an android tablet and so far the experience with running android apps has been good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit doubles as an android tablet and so far the experience with running android apps has been good .\n->", + "output": "{\"text\": \"it doubles as an android tablet and so far the experience with running android apps has been good .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very pleased\n->very pleased\n[{'aspect': 'NULL', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i bought this originally a few months back , died within a week .\n->i bought this originally a few months back , died within a week .\n[{'aspect': 'NULL', 'opinion': 'died', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\n->", + "output": "{\"text\": \"battery life is right at 10 hours as advertised and the keyboard feels great when typing out emails .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: yay amazon customer service they replaced it immediately for me no questions asked .\n->yay amazon customer service they replaced it immediately for me no questions asked .\n[{'aspect': 'amazon customer service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: it is perfect for his college courses , work and fun .\n->it is perfect for his college courses , work and fun .\n[{'aspect': 'NULL', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: if you are looking for a quality chromebook with the most important features for everyday use , the asus flip is a fantastic choice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are looking for a quality chromebook with the most important features for everyday use , the asus flip is a fantastic choice .\n->", + "output": "{\"text\": \"if you are looking for a quality chromebook with the most important features for everyday use , the asus flip is a fantastic choice .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus flip', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n->I have reservations about the all you can eat deal , however -- the choices are fairly limited and you can probably order more food than you can eat for less than $ 18 by just going off the menu .\n[{'aspect': 'all you can eat deal', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Fresh , authentic , french cuisine in substantial portions .\n->Fresh , authentic , french cuisine in substantial portions .\n[{'aspect': 'french cuisine', 'opinion': 'Fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'french cuisine', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'substantial', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the only thing i think that could be better is the volume of the speakers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only thing i think that could be better is the volume of the speakers .\n->", + "output": "{\"text\": \"the only thing i think that could be better is the volume of the speakers .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'could be better', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n->this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n[{'aspect': 'silver bullet', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'silver bullet', 'opinion': 'functional', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it stays very cool to the touch and the performance has really been amazing .\n->it stays very cool to the touch and the performance has really been amazing .\n[{'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i just love my asus chromebook , i take it everywhere .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just love my asus chromebook , i take it everywhere .\n->", + "output": "{\"text\": \"i just love my asus chromebook , i take it everywhere .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was there on sat . for my birthday and we had an excellent time .\n->i was there on sat . for my birthday and we had an excellent time .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: called acer support , they were completely useless .\n->called acer support , they were completely useless .\n[{'aspect': 'acer support', 'opinion': 'useless', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: if you ' re looking for a good chromebook , this is the one for you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re looking for a good chromebook , this is the one for you .\n->", + "output": "{\"text\": \"if you ' re looking for a good chromebook , this is the one for you .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n->The lobster sandwich is good and the spaghetti with Scallops and Shrimp is great .\n[{'aspect': 'lobster sandwich', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'spaghetti with Scallops and Shrimp', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: lobster was good , nothing spectacular .\n->lobster was good , nothing spectacular .\n[{'aspect': 'lobster', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'nothing spectacular', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: a fantastic product , with an aluminum frame , touchscreen , and high definition ; high resolution screen ; you can ' t beat this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na fantastic product , with an aluminum frame , touchscreen , and high definition ; high resolution screen ; you can ' t beat this .\n->", + "output": "{\"text\": \"a fantastic product , with an aluminum frame , touchscreen , and high definition ; high resolution screen ; you can ' t beat this .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'definition', 'opinion': 'high', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and it was a very good price .\n->and it was a very good price .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: I love when restaurants think using fancy expensive ingrediants makes the food fine cuisine , even with no idea how to use them .\n->I love when restaurants think using fancy expensive ingrediants makes the food fine cuisine , even with no idea how to use them .\n[{'aspect': 'ingrediants', 'opinion': 'expensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'fine', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the m3 is great , never slow or laggy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe m3 is great , never slow or laggy .\n->", + "output": "{\"text\": \"the m3 is great , never slow or laggy .\", \"labels\": \"[{'aspect': 'm3', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'm3', 'opinion': 'never slow or laggy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food was undercooked -the sauce watery , and the vegetables raw .\n->the food was undercooked -the sauce watery , and the vegetables raw .\n[{'aspect': 'food', 'opinion': 'undercooked', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'raw', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the dancing , white river and millenium rolls are musts .\n->the dancing , white river and millenium rolls are musts .\n[{'aspect': 'dancing , white river and millenium rolls', 'opinion': 'musts', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i mainly download movies over the netflix app and watch movies on the airplane and internet browsing in hotel rooms .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni mainly download movies over the netflix app and watch movies on the airplane and internet browsing in hotel rooms .\n->", + "output": "{\"text\": \"i mainly download movies over the netflix app and watch movies on the airplane and internet browsing in hotel rooms .\", \"labels\": \"[{'aspect': 'netflix app', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the max screen brightness isn ' t very bright\n->the max screen brightness isn ' t very bright\n[{'aspect': 'screen', 'opinion': \"' t very bright\", 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: the case is solid and attractive , the keyboard responsive and comfortable to use ( nice touch with the dedicated number keypad too ! )\n->the case is solid and attractive , the keyboard responsive and comfortable to use ( nice touch with the dedicated number keypad too ! )\n[{'aspect': 'case', 'opinion': 'solid', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'case', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'number keypad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}]\ntext: the chromebook loads everything lightning fast and provides a simple user experience that gets you where you need to go quickly and efficiently .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook loads everything lightning fast and provides a simple user experience that gets you where you need to go quickly and efficiently .\n->", + "output": "{\"text\": \"the chromebook loads everything lightning fast and provides a simple user experience that gets you where you need to go quickly and efficiently .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'chromebook', 'opinion': 'efficiently', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .\n->ive asked a cart attendant for a lotus leaf wrapped rice and she replied back rice and just walked away .\n[{'aspect': 'cart attendant', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: nice keyboard\n->nice keyboard\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\n->", + "output": "{\"text\": \"the computer itself is sturdy and feels well - made , though the keyboard is a bit smaller than i ' m used to .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'well - made', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'smaller', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is awesome - definitely try the striped bass .\n->The food is awesome - definitely try the striped bass .\n[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'striped bass', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: you just have to deal with a low battery and that ' s all\n->you just have to deal with a low battery and that ' s all\n[{'aspect': 'battery', 'opinion': 'low', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: this keyboard was perfect - not cramped and highly responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis keyboard was perfect - not cramped and highly responsive .\n->", + "output": "{\"text\": \"this keyboard was perfect - not cramped and highly responsive .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'not cramped', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the system is quick , and used for browsing , and basic notes .\n->the system is quick , and used for browsing , and basic notes .\n[{'aspect': 'system', 'opinion': 'quick', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The entertainment was great they have shows that go on through out the dinner .\n->The entertainment was great they have shows that go on through out the dinner .\n[{'aspect': 'entertainment', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the pricing was too good to pass up and i ' ve really been pleased with the product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pricing was too good to pass up and i ' ve really been pleased with the product .\n->", + "output": "{\"text\": \"the pricing was too good to pass up and i ' ve really been pleased with the product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->the staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n[{'aspect': 'staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'stressed', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'unisex bathroom', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n->The mussels were fantastic and so was the dessert ... definitely going to be back very soon .\n[{'aspect': 'mussels', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dessert', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i plan to take it with me when working as i have gotten approximately 8 - 9 hrs of battery time usage so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni plan to take it with me when working as i have gotten approximately 8 - 9 hrs of battery time usage so far .\n->", + "output": "{\"text\": \"i plan to take it with me when working as i have gotten approximately 8 - 9 hrs of battery time usage so far .\", \"labels\": \"[{'aspect': 'battery time usage', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: spreads and toppings are great - though a bit pricey .\n->spreads and toppings are great - though a bit pricey .\n[{'aspect': 'spreads', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'spreads', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'toppings', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'toppings', 'opinion': 'pricey', 'polarity': 'negative', 'category': 'FOOD#PRICES'}]\nExample:\ntext: i love how quick this thing is .\n->i love how quick this thing is .\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\ntext: this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\n->", + "output": "{\"text\": \"this is my first chromebook so i am still learning the ins and outside but am very impressed with the whole package .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Try sushimi cucumber roll .\n->Try sushimi cucumber roll .\n[{'aspect': 'sushimi cucumber roll', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Waitstaff are very friendly .\n->Waitstaff are very friendly .\n[{'aspect': 'Waitstaff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: there is nothing i can ' t do on this amazing thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is nothing i can ' t do on this amazing thing .\n->", + "output": "{\"text\": \"there is nothing i can ' t do on this amazing thing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this asus worked right out of the box and was very responsive .\n->this asus worked right out of the box and was very responsive .\n[{'aspect': 'asus', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: - backlit and solid keyboard ( not flimsy or cheap )\n->- backlit and solid keyboard ( not flimsy or cheap )\n[{'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'not flimsy or cheap', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: battery life is good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is good .\n->", + "output": "{\"text\": \"battery life is good .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: A touch more jalapeno heat for contrast and it would have been very good indeed .\n->A touch more jalapeno heat for contrast and it would have been very good indeed .\n[{'aspect': 'jalapeno', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n->the screen looks great ( though maybe not if all you do is look at 4k resolution ) and the keyboard response feels pretty good .\n[{'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard response', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: touch pad is a + + .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntouch pad is a + + .\n->", + "output": "{\"text\": \"touch pad is a + + .\", \"labels\": \"[{'aspect': 'touch pad', 'opinion': 'a +', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n->we went here for lunch a couple of weeks ago on a saturday , and i was thoroughly impressed with the food .\n[{'aspect': 'food', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n->Guacamole+shrimp appetizer was really great , we both had the filet , very good , did n't much like the frites that came with , but the filet was so good , neither of us cared .\n[{'aspect': 'Guacamole+shrimp appetizer', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'filet', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: sound is .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsound is .\n->", + "output": "{\"text\": \"sound is .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Made my dining experience uncomfortable .\n->Made my dining experience uncomfortable .\n[{'aspect': 'dining experience', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n->Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it ' s lightweight , sleek , and sexy as hell .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s lightweight , sleek , and sexy as hell .\n->", + "output": "{\"text\": \"it ' s lightweight , sleek , and sexy as hell .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'sexy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n->but the battery was giving erroneous feedback and getting too hot while charging , the keyboard was way too sensitive for me , and the cover / lid was a really cheaply made .\n[{'aspect': 'battery', 'opinion': 'erroneous', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'way too sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'cover / lid', 'opinion': 'cheaply', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: Despite the fact that the space is large , they 've overcrowded the floor with tables .\n->Despite the fact that the space is large , they 've overcrowded the floor with tables .\n[{'aspect': 'space', 'opinion': 'large', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'overcrowded', 'polarity': 'negative', 'category': 'NULL'}]\ntext: keyboard is the best laptop keyboard i have ever used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard is the best laptop keyboard i have ever used .\n->", + "output": "{\"text\": \"keyboard is the best laptop keyboard i have ever used .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'laptop keyboard', 'opinion': 'best', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the microsd slot leaves an inserted card perfectly flush .\n->the microsd slot leaves an inserted card perfectly flush .\n[{'aspect': 'microsd slot', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n->The wine list is n't great , and the desserts are shipped in from Bruno 's down the street , which is not as good as it used to be .\n[{'aspect': 'wine list', 'opinion': \"is n't great\", 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'desserts', 'opinion': 'not as good', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\n->", + "output": "{\"text\": \"i did a massive amount of research and found that the asus chromebook flip c302ca with 64 gigs was exactly what i wanted .\", \"labels\": \"[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was due to upgrade and this product seemed perfect for me .\n->i was due to upgrade and this product seemed perfect for me .\n[{'aspect': 'product', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food was great and tasty , but the sitting space was too small , i do n ' t like being cramp in a corner .\n->the food was great and tasty , but the sitting space was too small , i do n ' t like being cramp in a corner .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sitting space', 'opinion': 'too small', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: this chromebook not only meets those requirements it has exceeded my expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook not only meets those requirements it has exceeded my expectations .\n->", + "output": "{\"text\": \"this chromebook not only meets those requirements it has exceeded my expectations .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: lobster was good , nothing spectacular .\n->lobster was good , nothing spectacular .\n[{'aspect': 'lobster', 'opinion': 'good', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'lobster', 'opinion': 'nothing spectacular', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the key board and mouse pad are not very sensitive .\n->the key board and mouse pad are not very sensitive .\n[{'aspect': 'key board', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'mouse pad', 'opinion': 'not very sensitive', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: the intel i3 processor simply flys .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe intel i3 processor simply flys .\n->", + "output": "{\"text\": \"the intel i3 processor simply flys .\", \"labels\": \"[{'aspect': 'intel i3 processor', 'opinion': 'flys', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 6 inch laptop it appears fragile the keyboard itself feels like the keys will pop out .\n->6 inch laptop it appears fragile the keyboard itself feels like the keys will pop out .\n[{'aspect': '6 inch laptop', 'opinion': 'fragile', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'keys', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\nExample:\ntext: it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n->it has a light design making it easy for travel , reasonably small screen that makes it easy to use on your lap or in class , and a quality chassis to give it that nice premium feeling almost like a shiny apple mac .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'screen', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chassis', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the machine is incredibly responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe machine is incredibly responsive .\n->", + "output": "{\"text\": \"the machine is incredibly responsive .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'incredibly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'machine', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food is reliable and the price is moderate .\n->The food is reliable and the price is moderate .\n[{'aspect': 'food', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: if i could give 0 stars i would do so for this place .\n->if i could give 0 stars i would do so for this place .\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: i see absolutely no lag on videos or streaming content .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni see absolutely no lag on videos or streaming content .\n->", + "output": "{\"text\": \"i see absolutely no lag on videos or streaming content .\", \"labels\": \"[{'aspect': 'videos', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'streaming content', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n->you are not eating haut cuisine with subtle hints of whatever but : cassuolet , steake fritte , tripe stew , etc ; simple stuff .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: their designs are made for phones and on this huge screen , they are palpitated .\n->their designs are made for phones and on this huge screen , they are palpitated .\n[{'aspect': 'designs', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\ntext: the track pad , which i don ' t use , is also highly responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe track pad , which i don ' t use , is also highly responsive .\n->", + "output": "{\"text\": \"the track pad , which i don ' t use , is also highly responsive .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n->my girlfriend , being slightly more aggressive , and having been equally disgusted causing her to throw out the remainder of her barely eaten meal , called back only to be informed that i was probably wrong and that it was most likely an oyster , and that we were also blacklisted from their restaurant .\n[{'aspect': 'meal', 'opinion': 'disgusted', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: great machine out of the box .\n->great machine out of the box .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\n->", + "output": "{\"text\": \"the fact that it flips to become a tablet style unit , the great build quality ( all aluminum chassis , top and sides ) , etc .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: then it would not boot up .\n->then it would not boot up .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the food is great and they have a good selection of wines at reasonable prices .\n->the food is great and they have a good selection of wines at reasonable prices .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'DRINKS#STYLE_OPTIONS'}, {'aspect': 'wines', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'DRINKS#PRICES'}]\ntext: i ' ve used this daily for nearly eight months and have been very happy with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve used this daily for nearly eight months and have been very happy with .\n->", + "output": "{\"text\": \"i ' ve used this daily for nearly eight months and have been very happy with .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n->ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: Decent wine at reasonable prices .\n->Decent wine at reasonable prices .\n[{'aspect': 'wine', 'opinion': 'Decent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen is bright and beautiful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is bright and beautiful .\n->", + "output": "{\"text\": \"the screen is bright and beautiful .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it also has pretty decent i / o with two usb 3 .\n->it also has pretty decent i / o with two usb 3 .\n[{'aspect': 'i / o', 'opinion': 'decent', 'polarity': 'positive', 'category': 'PORTS#CONNECTIVITY'}]\nExample:\ntext: disappointing battery life , even with light use i have to recharge every 4 - 5 hours ( at best ) .\n->disappointing battery life , even with light use i have to recharge every 4 - 5 hours ( at best ) .\n[{'aspect': 'battery life', 'opinion': 'disappointing', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: the overall device is slim and lightweight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe overall device is slim and lightweight .\n->", + "output": "{\"text\": \"the overall device is slim and lightweight .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this by far has been the easiest to set up and use .\n->this by far has been the easiest to set up and use .\n[{'aspect': 'set up', 'opinion': 'easiest', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\nExample:\ntext: - long battery life\n->- long battery life\n[{'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\ntext: in practice , the device is heavier than is comfortable for this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin practice , the device is heavier than is comfortable for this .\n->", + "output": "{\"text\": \"in practice , the device is heavier than is comfortable for this .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'device', 'opinion': 'than is comfortable', 'polarity': 'negative', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n->downtown dinner 2002 - prixe fix : appetizers were ok , waiter gave me poor suggestion . . . try the potato stuff kanish best one .\n[{'aspect': 'appetizers', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiter', 'opinion': 'poor', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n->as of the time of writing , the audio drivers are still not working , but i typically don ' t care for that anyways .\n[{'aspect': 'audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: battery life is fantastic giving me over 8 hours easily with moderate usage .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is fantastic giving me over 8 hours easily with moderate usage .\n->", + "output": "{\"text\": \"battery life is fantastic giving me over 8 hours easily with moderate usage .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my chow fun and chow see was really bland and oily .\n->my chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: With the great variety on the menu , I eat here often and never get bored .\n->With the great variety on the menu , I eat here often and never get bored .\n[{'aspect': 'menu', 'opinion': 'great variety', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i knocked off a star for build quality control .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni knocked off a star for build quality control .\n->", + "output": "{\"text\": \"i knocked off a star for build quality control .\", \"labels\": \"[{'aspect': 'build quality control', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice build quality , very fast and beautiful display .\n->nice build quality , very fast and beautiful display .\n[{'aspect': 'build quality', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'display', 'opinion': 'fast', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'display', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: the rest of the dim sum , though pricey by chinatown standards , is worth it .\n->the rest of the dim sum , though pricey by chinatown standards , is worth it .\n[{'aspect': 'dim sum', 'opinion': 'pricey', 'polarity': 'positive', 'category': 'FOOD#PRICES'}, {'aspect': 'dim sum', 'opinion': 'worth', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\ntext: the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\n->", + "output": "{\"text\": \"the keyboard is great and the mouse hasn ' t given me the issues my old laptop gave me .\", \"labels\": \"[{'aspect': 'mouse', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MOUSE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer runs great .\n->the computer runs great .\n[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: have had mine for about 3 - 4 wks and have had no trouble .\n->have had mine for about 3 - 4 wks and have had no trouble .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the screen is plenty big and the visual very nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is plenty big and the visual very nice .\n->", + "output": "{\"text\": \"the screen is plenty big and the visual very nice .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'big', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i think that the bezels are a smidge thick , but that actually makes perfect sense .\n->i think that the bezels are a smidge thick , but that actually makes perfect sense .\n[{'aspect': 'bezels', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: if only they delivered , they ' d make a mint !\n->if only they delivered , they ' d make a mint !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: the backlit keyboard is very nice and only comes on in low light .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe backlit keyboard is very nice and only comes on in low light .\n->", + "output": "{\"text\": \"the backlit keyboard is very nice and only comes on in low light .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is a consistently great place to dine for lunch or dinner .\n->This is a consistently great place to dine for lunch or dinner .\n[{'aspect': 'dine', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: sadly , apple is moving into fashion - - your laptop is no longer a performing athlete .\n->sadly , apple is moving into fashion - - your laptop is no longer a performing athlete .\n[{'aspect': 'apple', 'opinion': 'sadly', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: this chromebook is fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook is fast .\n->", + "output": "{\"text\": \"this chromebook is fast .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the overall unit has a slim profile , and is light weight .\n->the overall unit has a slim profile , and is light weight .\n[{'aspect': 'unit', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: ambience is so cute and quaint , good for business although we were there on vacation .\n->ambience is so cute and quaint , good for business although we were there on vacation .\n[{'aspect': 'ambience', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'ambience', 'opinion': 'quaint', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: the display is clear .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe display is clear .\n->", + "output": "{\"text\": \"the display is clear .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Aside from the Sea Urchin , the chef recommended an assortment of fish including Fatty Yellow Tail , Boton Shrimp , Blue Fin Torro ( Fatty Tuna ) , Sea Eel , etc .\n->Aside from the Sea Urchin , the chef recommended an assortment of fish including Fatty Yellow Tail , Boton Shrimp , Blue Fin Torro ( Fatty Tuna ) , Sea Eel , etc .\n[{'aspect': 'assortment of fish', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Fatty Yellow Tail', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Boton Shrimp', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Sea Eel', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Sea Urchin', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'Blue Fin Torro ( Fatty Tuna )', 'opinion': 'recommended', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n->the servers at flatbush farm appear to have perfected that ghastly technique of making you feel guilty and ashamed for deigning to attract their attention .\n[{'aspect': 'servers', 'opinion': 'perfected', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: if this guy is in your price range - just buy it and get it over with .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif this guy is in your price range - just buy it and get it over with .\n->", + "output": "{\"text\": \"if this guy is in your price range - just buy it and get it over with .\", \"labels\": \"[{'aspect': 'guy', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the keyboard is really nice - .\n->the keyboard is really nice - .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: While this is a pretty place in that overly cute French way , the food was insultingly horrible .\n->While this is a pretty place in that overly cute French way , the food was insultingly horrible .\n[{'aspect': 'place', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'insultingly horrible', 'polarity': 'negative', 'category': 'NULL'}]\ntext: it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\n->", + "output": "{\"text\": \"it is remarkably fast on chrome os and the screen is very clear ( but glossy ) .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'clear', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'glossy', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n->We had great desserts ( including the best cannoli I 've ever had ) and then they offered an after dinner drink , on the house .\n[{'aspect': 'desserts', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cannoli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: what was even worse was the customer service .\n->what was even worse was the customer service .\n[{'aspect': 'customer service', 'opinion': 'worse', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: battery life appears to be pretty good too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life appears to be pretty good too .\n->", + "output": "{\"text\": \"battery life appears to be pretty good too .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice keyboard .\n->nice keyboard .\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: My suggestion is to eat family style because you 'll want to try the other dishes .\n->My suggestion is to eat family style because you 'll want to try the other dishes .\n[{'aspect': 'eat family style', 'opinion': 'suggestion', 'polarity': 'positive', 'category': 'NULL'}]\ntext: chrome has come a long way to be sure and in its optimized avatar on this system its very snappy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchrome has come a long way to be sure and in its optimized avatar on this system its very snappy .\n->", + "output": "{\"text\": \"chrome has come a long way to be sure and in its optimized avatar on this system its very snappy .\", \"labels\": \"[{'aspect': 'chrome', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - power button next to delete button ?\n->- power button next to delete button ?\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n->i ' ve had this laptop for 4 months and it ' s run very well considering it ' s price .\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the keyboard is also very comfortable to type on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is also very comfortable to type on .\n->", + "output": "{\"text\": \"the keyboard is also very comfortable to type on .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the fans did not turn on loudly if at all .\n->the fans did not turn on loudly if at all .\n[{'aspect': 'fans', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FANS&COOLING#OPERATION_PERFORMANCE'}]\nExample:\ntext: Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n->Oh yes , and they lie on the phone , claiming they have seating in the garden , then of course the seats are not available .\n[{'aspect': 'seating in the garden', 'opinion': 'lie', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'seats', 'opinion': 'not available', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: the touchscreen functions very well , both in laptop and tablet mode , and the trackpad and keyboard are enjoyable to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchscreen functions very well , both in laptop and tablet mode , and the trackpad and keyboard are enjoyable to use .\n->", + "output": "{\"text\": \"the touchscreen functions very well , both in laptop and tablet mode , and the trackpad and keyboard are enjoyable to use .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'well', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'enjoyable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n->the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n[{'aspect': 'ambience', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: The prices were CHEAP compared to the quality of service and food .\n->The prices were CHEAP compared to the quality of service and food .\n[{'aspect': 'prices', 'opinion': 'CHEAP', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i really like this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really like this chromebook .\n->", + "output": "{\"text\": \"i really like this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: After all that , they complained to me about the small tip .\n->After all that , they complained to me about the small tip .\n[{'aspect': 'tip', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the food is tasty and portion sizes are appropriate .\n->the food is tasty and portion sizes are appropriate .\n[{'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'portion sizes', 'opinion': 'appropriate', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: i have had my asus chromebook for several months and feel liberated from electronic hell .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have had my asus chromebook for several months and feel liberated from electronic hell .\n->", + "output": "{\"text\": \"i have had my asus chromebook for several months and feel liberated from electronic hell .\", \"labels\": \"[{'aspect': 'asus chromebook', 'opinion': 'liberated', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in preparation for becoming a chromebook user i had purchased a 256 gig memory card and moved all my laptop files to it .\n->in preparation for becoming a chromebook user i had purchased a 256 gig memory card and moved all my laptop files to it .\n[{'aspect': 'memory card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: this is the first macbook i have ever purchased , i wish i had purchased sooner .\n->this is the first macbook i have ever purchased , i wish i had purchased sooner .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: it is great quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is great quality .\n->", + "output": "{\"text\": \"it is great quality .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We had the pot-stickers which were great and a tempura dish that was great .\n->We had the pot-stickers which were great and a tempura dish that was great .\n[{'aspect': 'pot-stickers', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'tempura dish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n->it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n[{'aspect': 'tablet', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\n->", + "output": "{\"text\": \"pro ' s : flip functions are great ; especially for watching movies or flipping through powerpoint or photos .\", \"labels\": \"[{'aspect': 'flip functions', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n->i have had nothing but problems with my macbook connecting to my wifi from the beginning .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#CONNECTIVITY'}]\nExample:\ntext: If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n->If the omakase is to showcase technique and variety , serving almost 40 % of items BBQ-ed and a spicy tuna roll wrapped with not-so-fresh nori seems to be a rather limp performance .\n[{'aspect': 'nori', 'opinion': 'not-so-fresh', 'polarity': 'negative', 'category': 'NULL'}]\ntext: touch screen and zoom is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntouch screen and zoom is great .\n->", + "output": "{\"text\": \"touch screen and zoom is great .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'zoom', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: so far , this chromebook is fantastic .\n->so far , this chromebook is fantastic .\n[{'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the only thing that my friend left out is that when we sat down at the bar the bartender disappeared .\n->the only thing that my friend left out is that when we sat down at the bar the bartender disappeared .\n[{'aspect': 'bartender', 'opinion': 'disappeared', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: my biggest gripe with this that makes me incredibly frustrated is that google hangouts is unreliable on this .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy biggest gripe with this that makes me incredibly frustrated is that google hangouts is unreliable on this .\n->", + "output": "{\"text\": \"my biggest gripe with this that makes me incredibly frustrated is that google hangouts is unreliable on this .\", \"labels\": \"[{'aspect': 'google hangouts', 'opinion': 'unreliable', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'google hangouts', 'opinion': 'frustrated', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the mbp 13 ` ` is plenty mobile .\n->the mbp 13 ` ` is plenty mobile .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n->Traditional French decour was pleasant though the hall was rather noisy - the restaurant was full and we had to raise our voices to be able to maintain a conversation .\n[{'aspect': 'Traditional French decour', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'hall', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: this is a great device if your main goal is to check email , surf the internet listen to music or watch videos .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great device if your main goal is to check email , surf the internet listen to music or watch videos .\n->", + "output": "{\"text\": \"this is a great device if your main goal is to check email , surf the internet listen to music or watch videos .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sushi is amazing ! ! !\n->the sushi is amazing ! ! !\n[{'aspect': 'sushi', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Though you will undoubtedly be seated at a table with what seems like barely enough room ( no matter what the size of your party ) , the warm atomosphere is worth the cramped quarters- you 'll have fun and forgot about the tight spot you 're in .\n->Though you will undoubtedly be seated at a table with what seems like barely enough room ( no matter what the size of your party ) , the warm atomosphere is worth the cramped quarters- you 'll have fun and forgot about the tight spot you 're in .\n[{'aspect': 'table', 'opinion': 'enough', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'atomosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'room', 'opinion': 'enough', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'spot', 'opinion': 'tight', 'polarity': 'negative', 'category': 'NULL'}]\ntext: tapping it on either end is hit or miss .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntapping it on either end is hit or miss .\n->", + "output": "{\"text\": \"tapping it on either end is hit or miss .\", \"labels\": \"[{'aspect': 'tapping', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food , good size menu , great service and an unpretentious setting .\n->Great food , good size menu , great service and an unpretentious setting .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'menu', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'setting', 'opinion': 'unpretentious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Service was very prompt but slightly rushed .\n->Service was very prompt but slightly rushed .\n[{'aspect': 'Service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Service', 'opinion': 'rushed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: easy to setup and use\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neasy to setup and use\n->", + "output": "{\"text\": \"easy to setup and use\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n->There is a downside if you 're ordering in -- the delivery guys have MAJOR attitude .\n[{'aspect': 'delivery guys', 'opinion': 'downside', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n->i ' ve owned several acer monitors that i ' ve always been pleased with , but this is the first computer .\n[{'aspect': 'acer monitors', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'computer', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i ' ve been happy with all of the asus devices i ' ve purchased ( several computers and tablets ) but this is a real gem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been happy with all of the asus devices i ' ve purchased ( several computers and tablets ) but this is a real gem .\n->", + "output": "{\"text\": \"i ' ve been happy with all of the asus devices i ' ve purchased ( several computers and tablets ) but this is a real gem .\", \"labels\": \"[{'aspect': 'asus devices', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'asus devices', 'opinion': 'gem', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is always very crowded and popular .\n->This place is always very crowded and popular .\n[{'aspect': 'place', 'opinion': 'crowded', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'popular', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Frites were delicious if a bit on the thick side .\n->Frites were delicious if a bit on the thick side .\n[{'aspect': 'Frites', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the keyboard is basically perfect for a laptop , the touchscreen is amazing , and battery life is usually over 10 hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is basically perfect for a laptop , the touchscreen is amazing , and battery life is usually over 10 hours .\n->", + "output": "{\"text\": \"the keyboard is basically perfect for a laptop , the touchscreen is amazing , and battery life is usually over 10 hours .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n->So all I 'm trying to say is this restaurant is by far the best thai food restaurant I 've ever been to .\n[{'aspect': 'thai food', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n->- i swear my screen periodically changes brightness but i can ' t tell if this is my imagination or not .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: if you are looking to ditch apple i recommend it and touch screen works great and easy to read movie scripts to take notes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you are looking to ditch apple i recommend it and touch screen works great and easy to read movie scripts to take notes .\n->", + "output": "{\"text\": \"if you are looking to ditch apple i recommend it and touch screen works great and easy to read movie scripts to take notes .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'touch screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the decor is very simple but comfortable .\n->the decor is very simple but comfortable .\n[{'aspect': 'decor', 'opinion': 'simple', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'decor', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it just freezes up on you .\n->it just freezes up on you .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: everything looked great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything looked great .\n->", + "output": "{\"text\": \"everything looked great .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: transferring files from a non - iphone phone , like android is extremely annoying .\n->transferring files from a non - iphone phone , like android is extremely annoying .\n[{'aspect': 'android', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'SOFTWARE#PORTABILITY'}]\nExample:\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' ve found the large trackpad to be responsive and accurate .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve found the large trackpad to be responsive and accurate .\n->", + "output": "{\"text\": \"i ' ve found the large trackpad to be responsive and accurate .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: 5 pound laptop with its nine hour battery life .\n->5 pound laptop with its nine hour battery life .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: backlit keyboard is extremely viewable and comfortable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbacklit keyboard is extremely viewable and comfortable .\n->", + "output": "{\"text\": \"backlit keyboard is extremely viewable and comfortable .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'viewable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n->there nwere complaints regarding the lousy screen resolution , as well as the mouse pad not working well .\n[{'aspect': 'screen resolution', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'mouse pad', 'opinion': 'not working well', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n->This is the only Thai place I go too in NYC , it 's wonderful , and live relaxed Jazz on certain nights .\n[{'aspect': 'Jazz', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: she loves this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nshe loves this laptop .\n->", + "output": "{\"text\": \"she loves this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'loves', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: how can a brand new computer not charge properly ?\n->how can a brand new computer not charge properly ?\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n->We had the scallops as an appetizer and they were delicious and the sauce was wonderful .\n[{'aspect': 'scallops', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'appetizer', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sauce', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: very solid build quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery solid build quality .\n->", + "output": "{\"text\": \"very solid build quality .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: edit : reinstalled the driver now the audio is better on earphones .\n->edit : reinstalled the driver now the audio is better on earphones .\n[{'aspect': 'audio', 'opinion': 'better', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\nExample:\ntext: sauce was watery and the food did n ' t have much flavor .\n->sauce was watery and the food did n ' t have much flavor .\n[{'aspect': 'sauce', 'opinion': 'watery', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: charging is crazy fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncharging is crazy fast .\n->", + "output": "{\"text\": \"charging is crazy fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'crazy', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: They treated us well and the food was extremely fresh and well-prepared .\n->They treated us well and the food was extremely fresh and well-prepared .\n[{'aspect': 'food', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'well-prepared', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n->i ' ve only had it for one day and the set up was easy , and all items delivered were present , will update over time !\n[{'aspect': 'set up', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\ntext: the good news is that the android features ( google play store apps ) work nearly across the board .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe good news is that the android features ( google play store apps ) work nearly across the board .\n->", + "output": "{\"text\": \"the good news is that the android features ( google play store apps ) work nearly across the board .\", \"labels\": \"[{'aspect': 'android features', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: rao is a good restaurant , but it ' s nothing special .\n->rao is a good restaurant , but it ' s nothing special .\n[{'aspect': 'rao', 'opinion': 'good', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'rao', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: ingredients are organic which is a real plus for me .\n->ingredients are organic which is a real plus for me .\n[{'aspect': 'ingredients', 'opinion': 'organic', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ingredients', 'opinion': 'plus', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: chrome os is pretty simplistic and easy to learn .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchrome os is pretty simplistic and easy to learn .\n->", + "output": "{\"text\": \"chrome os is pretty simplistic and easy to learn .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'simplistic', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'chrome os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + good battery life\n->+ good battery life\n[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n->don ' t get me wrong though , i love the battery life and 95 % i ' m happy chugging along , but every once in a while this rears it ' s ugly head .\n[{'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'ugly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: touch - screen features are responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntouch - screen features are responsive .\n->", + "output": "{\"text\": \"touch - screen features are responsive .\", \"labels\": \"[{'aspect': 'touch - screen', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n->if celebrities make you sweat , then your in for a ride , but if your like most around these parts then you ' ll just yawn and wonder whats with all the hype .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n->My husband said he could 've eaten several more , the portion was fine for me he even exclaimed that the french fries were the best he has had .\n[{'aspect': 'french fries', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so far , this chromebook is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far , this chromebook is fantastic .\n->", + "output": "{\"text\": \"so far , this chromebook is fantastic .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: asus support is responsive but ineffective .\n->asus support is responsive but ineffective .\n[{'aspect': 'asus support', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}, {'aspect': 'asus support', 'opinion': 'ineffective', 'polarity': 'positive', 'category': 'SUPPORT#QUALITY'}]\nExample:\ntext: The price was extremely reasonable for the appetizers and food we ate .\n->The price was extremely reasonable for the appetizers and food we ate .\n[{'aspect': 'price', 'opinion': 'reasonable', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it served my needs of web browsing and word processing for a number of years , but its battery life had dwindled and neither the screen nor the processor can match this asus .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit served my needs of web browsing and word processing for a number of years , but its battery life had dwindled and neither the screen nor the processor can match this asus .\n->", + "output": "{\"text\": \"it served my needs of web browsing and word processing for a number of years , but its battery life had dwindled and neither the screen nor the processor can match this asus .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'processor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it fit nicely into my backpack without taking up space or weighing me down .\n->it fit nicely into my backpack without taking up space or weighing me down .\n[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: cons : i wished it had a backlight on the keyboard .\n->cons : i wished it had a backlight on the keyboard .\n[{'aspect': 'NULL', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: it boots in seconds , and i get ~ 10 hours out of the battery .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit boots in seconds , and i get ~ 10 hours out of the battery .\n->", + "output": "{\"text\": \"it boots in seconds , and i get ~ 10 hours out of the battery .\", \"labels\": \"[{'aspect': 'boots', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food there are sastifying .\n->The food there are sastifying .\n[{'aspect': 'food', 'opinion': 'sastifying', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: when i recieved the item i was amazed at the quality of it .\n->when i recieved the item i was amazed at the quality of it .\n[{'aspect': 'item', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\ntext: i ' ve had this device for 5 days so i ' ll keep my review simple .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve had this device for 5 days so i ' ll keep my review simple .\n->", + "output": "{\"text\": \"i ' ve had this device for 5 days so i ' ll keep my review simple .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: most of the servers are very attentive , friendly and quite attractive .\n->most of the servers are very attentive , friendly and quite attractive .\n[{'aspect': 'servers', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'servers', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: horrible keyboard flex\n->horrible keyboard flex\n[{'aspect': 'keyboard flex', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: i absolutely love everything about this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely love everything about this chromebook .\n->", + "output": "{\"text\": \"i absolutely love everything about this chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also like the display .\n->i also like the display .\n[{'aspect': 'display', 'opinion': 'like', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: pros - slim , lightweight laptop due to 8th gen core - i5 .\n->pros - slim , lightweight laptop due to 8th gen core - i5 .\n[{'aspect': 'laptop', 'opinion': 'pros', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': '8th gen core - i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\ntext: the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\n->", + "output": "{\"text\": \"the build quality is top notch , screen looks wonderful , the keyboard feels comfortable and the back lit keys are a nice touch .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'back lit keys', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this time around , asus released an absolutely refined masterpiece .\n->this time around , asus released an absolutely refined masterpiece .\n[{'aspect': 'asus', 'opinion': 'masterpiece', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: it can not .\n->it can not .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: a + device and i would highly recommend it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na + device and i would highly recommend it .\n->", + "output": "{\"text\": \"a + device and i would highly recommend it .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'a +', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'device', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great selection of wine , and seafood .\n->Great selection of wine , and seafood .\n[{'aspect': 'selection of wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'seafood', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i added an sd card which has expanded on the 16gb of storage .\n->i added an sd card which has expanded on the 16gb of storage .\n[{'aspect': 'sd card', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'MEMORY#GENERAL'}]\ntext: i am happy to say that this is a really well built , capable machine and i am very very happy with this chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am happy to say that this is a really well built , capable machine and i am very very happy with this chromebook .\n->", + "output": "{\"text\": \"i am happy to say that this is a really well built , capable machine and i am very very happy with this chromebook .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'machine', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'chromebook', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the food looked very appetizing and delicious since it came on a variety of fancy plates .\n->the food looked very appetizing and delicious since it came on a variety of fancy plates .\n[{'aspect': 'food', 'opinion': 'appetizing', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: not only is the food\n->not only is the food\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin all , this is really great machine and i am very very pleased with hardware and chrome os so far .\n->", + "output": "{\"text\": \"in all , this is really great machine and i am very very pleased with hardware and chrome os so far .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'hardware', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'chrome os', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'OS#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: pizza is terrific , as is homemade pasta .\n->pizza is terrific , as is homemade pasta .\n[{'aspect': 'pizza', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'homemade', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it is not up to my expectations , it produces some kind of sound when you play something on youtube ( from its keyboard ) perhaps !\n->it is not up to my expectations , it produces some kind of sound when you play something on youtube ( from its keyboard ) perhaps !\n[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: it ' s super portable and sleek .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s super portable and sleek .\n->", + "output": "{\"text\": \"it ' s super portable and sleek .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'sleek', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cute place , nice wait staff but would never go there again .\n->Cute place , nice wait staff but would never go there again .\n[{'aspect': 'wait staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'Cute', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the acer is similar but bigger and heavier .\n->the acer is similar but bigger and heavier .\n[{'aspect': 'acer', 'opinion': 'bigger', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'acer', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i have to say that the keyboard is my favorite feature .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have to say that the keyboard is my favorite feature .\n->", + "output": "{\"text\": \"i have to say that the keyboard is my favorite feature .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: after a couple of months of using this product , i finally could say that it ' s the best thing that i have ever bought up to this moment .\n->after a couple of months of using this product , i finally could say that it ' s the best thing that i have ever bought up to this moment .\n[{'aspect': 'product', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: even with the fan and heat if you use a cool mat on your lap you will be find especially with the screen being amazing for the price .\n->even with the fan and heat if you use a cool mat on your lap you will be find especially with the screen being amazing for the price .\n[{'aspect': 'fan', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FANS&COOLING#GENERAL'}, {'aspect': 'screen', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\ntext: please note that the track pad is way better than most .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nplease note that the track pad is way better than most .\n->", + "output": "{\"text\": \"please note that the track pad is way better than most .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: please note that the track pad is way better than most .\n->please note that the track pad is way better than most .\n[{'aspect': 'track pad', 'opinion': 'better', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: it was easy to set up .\n->it was easy to set up .\n[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: i just got this yesterday and i am very satisfied with the speed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni just got this yesterday and i am very satisfied with the speed .\n->", + "output": "{\"text\": \"i just got this yesterday and i am very satisfied with the speed .\", \"labels\": \"[{'aspect': 'speed', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was also horrible and the ambience is not that great .\n->Service was also horrible and the ambience is not that great .\n[{'aspect': 'Service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'not that great', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: people are rude bit again it 's new york !\n->people are rude bit again it 's new york !\n[{'aspect': 'people', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i use this for leisure activities and the ability to flip this around to watch movies is awesome !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use this for leisure activities and the ability to flip this around to watch movies is awesome !\n->", + "output": "{\"text\": \"i use this for leisure activities and the ability to flip this around to watch movies is awesome !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - performance can be stuttering when under heavy load .\n->- performance can be stuttering when under heavy load .\n[{'aspect': 'performance', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: We parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas .\n->We parked on the block of Nina 's the place looked nice , with people obviously enjoying their pizzas .\n[{'aspect': 'place', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizzas', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\ntext: good screen quality for reading , fairly fast i3 processor , and decent battery life .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood screen quality for reading , fairly fast i3 processor , and decent battery life .\n->", + "output": "{\"text\": \"good screen quality for reading , fairly fast i3 processor , and decent battery life .\", \"labels\": \"[{'aspect': 'screen quality', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'i3 processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'decent', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Acceptable prices .\n->Acceptable prices .\n[{'aspect': 'prices', 'opinion': 'Acceptable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i can plug it in and put it back to sleep and it will charge , but if i haven ' t woken it up it won ' t begin to charge .\n->i can plug it in and put it back to sleep and it will charge , but if i haven ' t woken it up it won ' t begin to charge .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i absolutely love this laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni absolutely love this laptop .\n->", + "output": "{\"text\": \"i absolutely love this laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n->The menu is very limited - i think we counted 4 or 5 entrees .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n->short and sweet \u2013 seating is great : it ' s romantic , cozy and private .\n[{'aspect': 'seating', 'opinion': 'short', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'seating', 'opinion': 'private', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: lightweight , gorgeous , great screen / picture quaity , sound could be louder but still good , fast processor ( i do a lot of research , photo processing , netflix watching and reading on the laptop ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlightweight , gorgeous , great screen / picture quaity , sound could be louder but still good , fast processor ( i do a lot of research , photo processing , netflix watching and reading on the laptop ) .\n->", + "output": "{\"text\": \"lightweight , gorgeous , great screen / picture quaity , sound could be louder but still good , fast processor ( i do a lot of research , photo processing , netflix watching and reading on the laptop ) .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'sound', 'opinion': 'good', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'processor', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n->as a japanese native , i ' ve lived in the tristate area for over 8 years , but i was just so amazed at this place .\n[{'aspect': 'place', 'opinion': 'amazed', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it just stopped working in the middle of my paper i was writing .\n->it just stopped working in the middle of my paper i was writing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\n->", + "output": "{\"text\": \"pros : light - sensor back - lit keyboard , great battery , fast startup , average - student friendly\", \"labels\": \"[{'aspect': 'back - lit keyboard', 'opinion': 'pros', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'battery', 'opinion': 'pros', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'battery', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'startup', 'opinion': 'pros', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'startup', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n->i wanted something i could work on my writing projects on the go , so i paid the $ 250 for this thing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'must', 'polarity': 'positive', 'category': 'NULL'}]\ntext: cons : no caps lock key ( still haven ' t found it , help ! )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ncons : no caps lock key ( still haven ' t found it , help ! )\n->", + "output": "{\"text\": \"cons : no caps lock key ( still haven ' t found it , help ! )\", \"labels\": \"[{'aspect': 'caps lock key', 'opinion': 'cons', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Largest and freshest pieces of sushi , and delicious !\n->Largest and freshest pieces of sushi , and delicious !\n[{'aspect': 'pieces of sushi', 'opinion': 'Largest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'freshest', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pieces of sushi', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i would highly recommand requesting a table by the window .\n->i would highly recommand requesting a table by the window .\n[{'aspect': 'table by the window', 'opinion': 'recommand', 'polarity': 'positive', 'category': 'LOCATION#GENERAL'}]\ntext: i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\n->", + "output": "{\"text\": \"i purchased this machine on cyber monday for $ 375 ( $ 75 off the listing price at the time of this review ) and i think at that price point , it ' s well worth it .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Total hipster-wannabe attitude in an otherwise sweet spot .\n->Total hipster-wannabe attitude in an otherwise sweet spot .\n[{'aspect': 'spot', 'opinion': 'sweet', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: This place is incredibly tiny .\n->This place is incredibly tiny .\n[{'aspect': 'place', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\n->", + "output": "{\"text\": \"the startup time is ridiculously fast , and just opening the laptop will turn it on and get you going on chrome within 10 seconds - i ' ve had as many as 12 tabs open , and it still ran smoothly .\", \"labels\": \"[{'aspect': 'startup time', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'chrome', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portion sizes here are huge , and the sushi is good .\n->The portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n->The staff has been nice , but they seemed really stressed and the unisex bathroom needs to be cleaned more often .\n[{'aspect': 'staff', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'stressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'unisex bathroom', 'opinion': 'stressed', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the battery life is a huge selling point in my opinion : even after multiple shut - downs / start - ups throughout the day , i get about 10 hours total run - time , and charging only takes about an hour .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery life is a huge selling point in my opinion : even after multiple shut - downs / start - ups throughout the day , i get about 10 hours total run - time , and charging only takes about an hour .\n->", + "output": "{\"text\": \"the battery life is a huge selling point in my opinion : even after multiple shut - downs / start - ups throughout the day , i get about 10 hours total run - time , and charging only takes about an hour .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keyboard feels firm and no flex , screen is nice for the price range .\n->keyboard feels firm and no flex , screen is nice for the price range .\n[{'aspect': 'keyboard', 'opinion': 'firm', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#PRICE'}]\nExample:\ntext: the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n->the porcini mushroom pasta special was tasteless , so was the seafood tagliatelle .\n[{'aspect': 'porcini mushroom pasta special', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'seafood tagliatelle', 'opinion': 'tasteless', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: all in all , this is a great laptop for the casual user , especially at this price point .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nall in all , this is a great laptop for the casual user , especially at this price point .\n->", + "output": "{\"text\": \"all in all , this is a great laptop for the casual user , especially at this price point .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#MISCELLANEOUS'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n->I have n't eat a lamb chop as delicious as that , the salads are really nice dressed with lemon and extra virgnin olive oil .\n[{'aspect': 'lamb chop', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salads', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: thin , light , cool are what i feel when holding it and carry around .\n->thin , light , cool are what i feel when holding it and carry around .\n[{'aspect': 'NULL', 'opinion': 'thin', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'cool', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: if you want to watch movies or listen to music , this might not be the machine for you .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you want to watch movies or listen to music , this might not be the machine for you .\n->", + "output": "{\"text\": \"if you want to watch movies or listen to music , this might not be the machine for you .\", \"labels\": \"[{'aspect': 'machine', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: faan is sooo good .\n->faan is sooo good .\n[{'aspect': 'faan', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: amma is nothing special .\n->amma is nothing special .\n[{'aspect': 'amma', 'opinion': 'nothing special', 'polarity': 'neutral', 'category': 'RESTAURANT#GENERAL'}]\ntext: the key features that drew me to this chromebook was design , a quality keyboard that had backlit keys , and a good processor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe key features that drew me to this chromebook was design , a quality keyboard that had backlit keys , and a good processor .\n->", + "output": "{\"text\": \"the key features that drew me to this chromebook was design , a quality keyboard that had backlit keys , and a good processor .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'quality', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'backlit keys', 'opinion': 'quality', 'polarity': 'positive', 'category': 'KEYBOARD#QUALITY'}, {'aspect': 'processor', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the all-u-can-eat sushi is definitely in very poor quality .\n->the all-u-can-eat sushi is definitely in very poor quality .\n[{'aspect': 'all-u-can-eat sushi', 'opinion': 'poor quality', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Great wine , great food .\n->Great wine , great food .\n[{'aspect': 'wine', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i did not find the battery to last a full ten hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did not find the battery to last a full ten hours .\n->", + "output": "{\"text\": \"i did not find the battery to last a full ten hours .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the seller ' s customer service was great very fast response time and the computer came packaged really well , in original box and it great shape .\n->the seller ' s customer service was great very fast response time and the computer came packaged really well , in original box and it great shape .\n[{'aspect': 'customer service', 'opinion': 'great', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}, {'aspect': 'customer service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n->i would like to have more storage capacity but the ssd boots this laptop up in a matter of seconds .\n[{'aspect': 'ssd', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: however , it charges insanely quickly : you can get a full charge in under an hour .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , it charges insanely quickly : you can get a full charge in under an hour .\n->", + "output": "{\"text\": \"however , it charges insanely quickly : you can get a full charge in under an hour .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: very poor battery life .\n->very poor battery life .\n[{'aspect': 'battery life', 'opinion': 'poor', 'polarity': 'negative', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n->The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n[{'aspect': 'decor', 'opinion': 'diner-ish', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'sparse', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the touchscreen is great though and feels very intuitive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchscreen is great though and feels very intuitive .\n->", + "output": "{\"text\": \"the touchscreen is great though and feels very intuitive .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service was slow had to wait to order and get food although not crowded .\n->Service was slow had to wait to order and get food although not crowded .\n[{'aspect': 'Service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n->the ambience was so fun , and the prices were great , on top of the fact that the food was really tasty .\n[{'aspect': 'ambience', 'opinion': 'fun', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'food', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: keyboard is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nkeyboard is fantastic .\n->", + "output": "{\"text\": \"keyboard is fantastic .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , I think this place is a good hang out spot .\n->However , I think this place is a good hang out spot .\n[{'aspect': 'spot', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: clearly , this is a company that can not handle repairs in a timely matter .\n->clearly , this is a company that can not handle repairs in a timely matter .\n[{'aspect': 'company', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: outstanding laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noutstanding laptop .\n->", + "output": "{\"text\": \"outstanding laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: first of all , this is a physically beautiful machine .\n->first of all , this is a physically beautiful machine .\n[{'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n->The decor in this place is very diner-ish and the kind of place you expect in the East Village - not romantic , just simple , small and sparse .\n[{'aspect': 'decor', 'opinion': 'diner-ish', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: super easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuper easy to use .\n->", + "output": "{\"text\": \"super easy to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i purchased this item 7 months ago and i love it .\n->i purchased this item 7 months ago and i love it .\n[{'aspect': 'item', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i got this laptop 2 days ago and it says plugged in , not charged .\n->i got this laptop 2 days ago and it says plugged in , not charged .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: it starts up fast , goes seamlessly from computer mode to ` ` tent ` ` mode to tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit starts up fast , goes seamlessly from computer mode to ` ` tent ` ` mode to tablet mode .\n->", + "output": "{\"text\": \"it starts up fast , goes seamlessly from computer mode to ` ` tent ` ` mode to tablet mode .\", \"labels\": \"[{'aspect': 'starts up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer mode', 'opinion': 'seamlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'tablet mode', 'opinion': 'seamlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: their tuna tartar appetizer is to die for .\n->their tuna tartar appetizer is to die for .\n[{'aspect': 'tuna tartar appetizer', 'opinion': 'die for', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the body of the chromebook feels solid due to the aluminium body .\n->the body of the chromebook feels solid due to the aluminium body .\n[{'aspect': 'body of the chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: it had everything i wanted and more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit had everything i wanted and more .\n->", + "output": "{\"text\": \"it had everything i wanted and more .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: less than 90 days and the screen stopped working .\n->less than 90 days and the screen stopped working .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n->also it 's great to have dinner in a very romantic and comfortable place , the service it 's just perfect ... they 're so frendly that we never want to live the place !\n[{'aspect': 'place', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\ntext: battery lasts me all day , it ' s big screen is easy on the eyes .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery lasts me all day , it ' s big screen is easy on the eyes .\n->", + "output": "{\"text\": \"battery lasts me all day , it ' s big screen is easy on the eyes .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'easy', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n->this is not an inexpensive product , especially for a chrome book but , i figured you get what you pay for .\n[{'aspect': 'product', 'opinion': 'not an inexpensive', 'polarity': 'negative', 'category': 'LAPTOP#PRICE'}, {'aspect': 'product', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: for starters they delivered us someone else ' s order .\n->for starters they delivered us someone else ' s order .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: this is a great chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a great chromebook !\n->", + "output": "{\"text\": \"this is a great chromebook !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it works well for internet browsing and e - mail but i was hoping for much more .\n->it works well for internet browsing and e - mail but i was hoping for much more .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\nExample:\ntext: very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n->very good in all respects except a very important one which is the pathetic sound volume especially during movie playback .\n[{'aspect': 'sound volume', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: for me the extra storage , back light keyboard ( you ' ll love it ) , 2 in 1 factor , and great build quality made it a no - brainer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor me the extra storage , back light keyboard ( you ' ll love it ) , 2 in 1 factor , and great build quality made it a no - brainer .\n->", + "output": "{\"text\": \"for me the extra storage , back light keyboard ( you ' ll love it ) , 2 in 1 factor , and great build quality made it a no - brainer .\", \"labels\": \"[{'aspect': 'storage', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'HARD_DISC#DESIGN_FEATURES'}, {'aspect': 'back light keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'build quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Service is not what one would expect from a joint in this price category .\n->Service is not what one would expect from a joint in this price category .\n[{'aspect': 'Service', 'opinion': 'not what one would expect', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'price category', 'opinion': 'expect', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: very cozy and warm inside . . . . .\n->very cozy and warm inside . . . . .\n[{'aspect': 'NULL', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: and the price is excellent for what you get .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nand the price is excellent for what you get .\n->", + "output": "{\"text\": \"and the price is excellent for what you get .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Faan 's got a great concept but a little rough on the delivery .\n->Faan 's got a great concept but a little rough on the delivery .\n[{'aspect': 'delivery', 'opinion': 'rough', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: nice keyboard\n->nice keyboard\n[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: i ' ve been using this for a couple of weeks and i must say i am very very pleased with the product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve been using this for a couple of weeks and i must say i am very very pleased with the product .\n->", + "output": "{\"text\": \"i ' ve been using this for a couple of weeks and i must say i am very very pleased with the product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything we had was good or ok . . . . but definitely nothing great .\n->everything we had was good or ok . . . . but definitely nothing great .\n[{'aspect': 'NULL', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The food there are sastifying .\n->The food there are sastifying .\n[{'aspect': 'food', 'opinion': 'sastifying', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - great quality build .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- great quality build .\n->", + "output": "{\"text\": \"- great quality build .\", \"labels\": \"[{'aspect': 'quality build', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food there is so good that even to order out the wait is incredible .\n->The food there is so good that even to order out the wait is incredible .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wait', 'opinion': 'incredible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: the charging issue i can live with as well , even though it is annoying .\n->the charging issue i can live with as well , even though it is annoying .\n[{'aspect': 'charging', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: - 360 degrees flipping is actually pretty practical\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- 360 degrees flipping is actually pretty practical\n->", + "output": "{\"text\": \"- 360 degrees flipping is actually pretty practical\", \"labels\": \"[{'aspect': '360 degrees flipping', 'opinion': 'practical', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great Indian food and the service is incredible .\n->Great Indian food and the service is incredible .\n[{'aspect': 'Indian food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: latterly the best laptop i ' ve ever had , fast , powerful , stunning display , little problems with pairing bluetooth\n->latterly the best laptop i ' ve ever had , fast , powerful , stunning display , little problems with pairing bluetooth\n[{'aspect': 'laptop', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'laptop', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'display', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'pairing bluetooth', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\ntext: - backlit and solid keyboard ( not flimsy or cheap )\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- backlit and solid keyboard ( not flimsy or cheap )\n->", + "output": "{\"text\": \"- backlit and solid keyboard ( not flimsy or cheap )\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'not flimsy or cheap', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n->it won ' t hold a charge for longer than an hour , my son has to relay on this piece of cheap equipment primarily for taking notes and doing schoolwork .\n[{'aspect': 'equipment', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'equipment', 'opinion': 'cheap', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: black white shakes came out good also .\n->black white shakes came out good also .\n[{'aspect': 'black white shakes', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - touch screen is very accurate\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- touch screen is very accurate\n->", + "output": "{\"text\": \"- touch screen is very accurate\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'accurate', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wish the power cord were longer , but that ' s minor .\n->i wish the power cord were longer , but that ' s minor .\n[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}]\nExample:\ntext: The quantity is also very good , you will come out satisfied .\n->The quantity is also very good , you will come out satisfied .\n[{'aspect': 'quantity', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'quantity', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'NULL'}]\ntext: - side buttons are responsive and well - made\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- side buttons are responsive and well - made\n->", + "output": "{\"text\": \"- side buttons are responsive and well - made\", \"labels\": \"[{'aspect': 'side buttons', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'side buttons', 'opinion': 'well - made', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n->sometimes tables do n ' t understand his sense of humor but it ' s refreshing to have a server who has personality , professionalism , and respects the privacy of your dinner .\n[{'aspect': 'server', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: food - awesome .\n->food - awesome .\n[{'aspect': 'food', 'opinion': 'awesome', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\ntext: - battery life is a bit short after some gaming\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- battery life is a bit short after some gaming\n->", + "output": "{\"text\": \"- battery life is a bit short after some gaming\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'short', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: issue summary : frequent crashing\n->issue summary : frequent crashing\n[{'aspect': 'NULL', 'opinion': 'crashing', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: and really large portions .\n->and really large portions .\n[{'aspect': 'portions', 'opinion': 'large', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: - touch pad seems a little off to me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n- touch pad seems a little off to me .\n->", + "output": "{\"text\": \"- touch pad seems a little off to me .\", \"labels\": \"[{'aspect': 'touch pad', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: still learning but it ' s a good computer and a great deal\n->still learning but it ' s a good computer and a great deal\n[{'aspect': 'computer', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n->i updated the realtek audio drivers and tested it using razer surround ' s audio drivers ; it still did not fix it .\n[{'aspect': 'realtek audio drivers', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#GENERAL'}]\ntext: other than that it ' s everything i imagined and more .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother than that it ' s everything i imagined and more .\n->", + "output": "{\"text\": \"other than that it ' s everything i imagined and more .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Much more reasonably priced too !\n->Much more reasonably priced too !\n[{'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n->BUt their best dish is thh Thai spiced curry noodles with shrimp - a dish that would cost $ 23.95 is most places , but it is $ 16 here .\n[{'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Thai spiced curry noodles with shrimp', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the m3 processor is pretty good ( decent speedometer score ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe m3 processor is pretty good ( decent speedometer score ) .\n->", + "output": "{\"text\": \"the m3 processor is pretty good ( decent speedometer score ) .\", \"labels\": \"[{'aspect': 'm3 processor', 'opinion': 'good', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'm3 processor', 'opinion': 'decent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was very impressed with its sleek design and the speed of the processor itself .\n->i was very impressed with its sleek design and the speed of the processor itself .\n[{'aspect': 'design', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'processor', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\nExample:\ntext: The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n->The svc can be a bit rude at times , esp if you have big group , but overall the restaurant is a must !\n[{'aspect': 'svc', 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the body of the chromebook feels solid due to the aluminium body .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe body of the chromebook feels solid due to the aluminium body .\n->", + "output": "{\"text\": \"the body of the chromebook feels solid due to the aluminium body .\", \"labels\": \"[{'aspect': 'body of the chromebook', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n->The menu is very limited - i think we counted 4 or 5 entrees .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: This tiny restaurant is as cozy as it gets , with that certain Parisian flair .\n->This tiny restaurant is as cozy as it gets , with that certain Parisian flair .\n[{'aspect': 'restaurant', 'opinion': 'tiny', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'cozy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: trackpad is nice and quiet and responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntrackpad is nice and quiet and responsive .\n->", + "output": "{\"text\": \"trackpad is nice and quiet and responsive .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i see absolutely no lag on videos or streaming content .\n->i see absolutely no lag on videos or streaming content .\n[{'aspect': 'videos', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'streaming content', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: i love how slim the design is - will fit easily into my backpack : ) highly recommend for everyday or school use !\n->i love how slim the design is - will fit easily into my backpack : ) highly recommend for everyday or school use !\n[{'aspect': 'design', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: as soon as i learned about c302 , i decided top bought it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas soon as i learned about c302 , i decided top bought it .\n->", + "output": "{\"text\": \"as soon as i learned about c302 , i decided top bought it .\", \"labels\": \"[{'aspect': 'c302', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Delicious food at a great price but do not go here on a cold day and sit by the front door .\n->Delicious food at a great price but do not go here on a cold day and sit by the front door .\n[{'aspect': 'food', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'front door', 'opinion': 'cold', 'polarity': 'neutral', 'category': 'NULL'}]\nExample:\ntext: the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n->the service was impeccable and unobtrusive - - the staff knows what they are there to do - - to know their menu , present your meal , and attend to your needs .\n[{'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'unobtrusive', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'staff', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: this little chromebook is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little chromebook is amazing .\n->", + "output": "{\"text\": \"this little chromebook is amazing .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n->Quality of food is excellent and price is cheap , stick to pork , fish , chicken , lamb and vegetables .\n[{'aspect': 'Quality of food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pork', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'chicken', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'vegetables', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food is great and they have a good selection of wines at reasonable prices .\n->The food is great and they have a good selection of wines at reasonable prices .\n[{'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wines', 'opinion': 'good selection', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the battery lasts a solid 8 + hours unless you ' re playing games its more like 4 - 5 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe battery lasts a solid 8 + hours unless you ' re playing games its more like 4 - 5 .\n->", + "output": "{\"text\": \"the battery lasts a solid 8 + hours unless you ' re playing games its more like 4 - 5 .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'solid', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: priced at upper intermediate range .\n->priced at upper intermediate range .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: the sashimi is always fresh and the rolls are innovative and delicious .\n->the sashimi is always fresh and the rolls are innovative and delicious .\n[{'aspect': 'sashimi', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'innovative', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'rolls', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: overall a good chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall a good chromebook .\n->", + "output": "{\"text\": \"overall a good chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: And the food was fantastic .\n->And the food was fantastic .\n[{'aspect': 'food', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n->and the backlit keyboard has a very good feel , and great key ` ` travel . ` `\n[{'aspect': 'backlit keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\ntext: outside of that , the keyboard is solid , the back lighting was not a selling point to me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noutside of that , the keyboard is solid , the back lighting was not a selling point to me .\n->", + "output": "{\"text\": \"outside of that , the keyboard is solid , the back lighting was not a selling point to me .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'solid', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'back lighting', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n->if you want something that would last longer than a month , look somewhere else , as this is an absolute rip - off .\n[{'aspect': 'NULL', 'opinion': 'rip - off', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Anyway , the food is good , the price is right and they have a decent wine list .\n->Anyway , the food is good , the price is right and they have a decent wine list .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'price', 'opinion': 'right', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine list', 'opinion': 'decent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the speakers overall are not very good .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speakers overall are not very good .\n->", + "output": "{\"text\": \"the speakers overall are not very good .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'not very good', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is such a lovely , peaceful place to eat outside .\n->This is such a lovely , peaceful place to eat outside .\n[{'aspect': 'place', 'opinion': 'lovely', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'peaceful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i would however spend $ 300 on this device .\n->i would however spend $ 300 on this device .\n[{'aspect': 'device', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\ntext: track pad is passable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntrack pad is passable .\n->", + "output": "{\"text\": \"track pad is passable .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'passable', 'polarity': 'neutral', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ll end the review simply saying i ' m very happy overall with my purchase !\n->i ' ll end the review simply saying i ' m very happy overall with my purchase !\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i have had my chromebook for 4 years still works great for internet , netflix , adult education classes .\n->i have had my chromebook for 4 years still works great for internet , netflix , adult education classes .\n[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: screen is nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen is nice .\n->", + "output": "{\"text\": \"screen is nice .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: oh and the customer service is garbage .\n->oh and the customer service is garbage .\n[{'aspect': 'customer service', 'opinion': 'garbage', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n->While the food was excellent , it was n't cheap ( though not extremely expensive either ) .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i find it to be a little large when used in tablet mode .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni find it to be a little large when used in tablet mode .\n->", + "output": "{\"text\": \"i find it to be a little large when used in tablet mode .\", \"labels\": \"[{'aspect': 'tablet mode', 'opinion': 'large', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n->However , looking at the table next to ours , we both sort of wished we had ordered pizza , which looked perfect\n[{'aspect': 'pizza', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The ambience is authentic and relaxing and we have always received attentive and prompt service .\n->The ambience is authentic and relaxing and we have always received attentive and prompt service .\n[{'aspect': 'ambience', 'opinion': 'authentic', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'relaxing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'attentive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'prompt', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overall , i am happy with it and would purchase it again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , i am happy with it and would purchase it again .\n->", + "output": "{\"text\": \"overall , i am happy with it and would purchase it again .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n->once you step into cosette , you ' re miraculously in a small , off - the - beaten path parisian bistro .\n[{'aspect': 'cosette', 'opinion': 'off - the - beaten', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: it runs most of those apps and games beautifully and when combined with a ` ` logitech gamepad f310 ` ` you can play the games with a game controller !\n->it runs most of those apps and games beautifully and when combined with a ` ` logitech gamepad f310 ` ` you can play the games with a game controller !\n[{'aspect': 'apps', 'opinion': 'beautifully', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\ntext: i tried the unit at best buy and to be honest , it felt a bit flimsy as a laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni tried the unit at best buy and to be honest , it felt a bit flimsy as a laptop .\n->", + "output": "{\"text\": \"i tried the unit at best buy and to be honest , it felt a bit flimsy as a laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'unit', 'opinion': 'flimsy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i would not recommend this as a primary chromebook or even to buy it at all .\n->i would not recommend this as a primary chromebook or even to buy it at all .\n[{'aspect': 'chromebook', 'opinion': 'not recommend', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the food is excellent !\n->the food is excellent !\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is awkward to the touch and the internals only has 32gb of hard drive .\n->", + "output": "{\"text\": \"the keyboard is awkward to the touch and the internals only has 32gb of hard drive .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'awkward', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'hard drive', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARD_DISC#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n->Both the fresh mozzerella slices and the Plain Cheese slice are phenomenal .\n[{'aspect': 'fresh mozzerella slices', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fresh mozzerella slices', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'Plain Cheese slice', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this is now my fastest - charging device .\n->this is now my fastest - charging device .\n[{'aspect': 'device', 'opinion': 'fastest', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the sound isn ' t great on both devices but they ' ll suffice ; at least the asus speakers are side - firing and not coming from the bottom like the pro .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe sound isn ' t great on both devices but they ' ll suffice ; at least the asus speakers are side - firing and not coming from the bottom like the pro .\n->", + "output": "{\"text\": \"the sound isn ' t great on both devices but they ' ll suffice ; at least the asus speakers are side - firing and not coming from the bottom like the pro .\", \"labels\": \"[{'aspect': 'asus speakers', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Make sure you try this place as often as you can .\n->Make sure you try this place as often as you can .\n[{'aspect': 'place', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: definitely a great spot for a nice occasion or date .\n->definitely a great spot for a nice occasion or date .\n[{'aspect': 'spot', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: so far i absolutely love it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far i absolutely love it .\n->", + "output": "{\"text\": \"so far i absolutely love it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I also ordered the Change Mojito , which was out of this world .\n->I also ordered the Change Mojito , which was out of this world .\n[{'aspect': 'Change Mojito', 'opinion': 'out of this world', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: plus the screen is bland .\n->plus the screen is bland .\n[{'aspect': 'screen', 'opinion': 'bland', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: love how fast it is , love that it can do everything i ' ve asked it to do so far in the two weeks i ' ve owned it , and i love how compact and easy it is to carry around .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove how fast it is , love that it can do everything i ' ve asked it to do so far in the two weeks i ' ve owned it , and i love how compact and easy it is to carry around .\n->", + "output": "{\"text\": \"love how fast it is , love that it can do everything i ' ve asked it to do so far in the two weeks i ' ve owned it , and i love how compact and easy it is to carry around .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this is the ultimate tablet .\n->this is the ultimate tablet .\n[{'aspect': 'tablet', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n->The ambience is pretty and nice for conversation , so a casual lunch here would probably be best .\n[{'aspect': 'ambience', 'opinion': 'pretty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lunch', 'opinion': 'casual', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it greatly exceeded my expectations .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit greatly exceeded my expectations .\n->", + "output": "{\"text\": \"it greatly exceeded my expectations .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'greatly', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: excellent choice in processor .\n->excellent choice in processor .\n[{'aspect': 'processor', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\nExample:\ntext: Everything is excellent , the menu is quite extensive , and you eat with a view on both sides of the city .\n->Everything is excellent , the menu is quite extensive , and you eat with a view on both sides of the city .\n[{'aspect': 'menu', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}]\ntext: pro ' s : this chromebook is very light .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \npro ' s : this chromebook is very light .\n->", + "output": "{\"text\": \"pro ' s : this chromebook is very light .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: feel like at max brightness it just isn ' t enough .\n->feel like at max brightness it just isn ' t enough .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: excellent food , although the interior could use some help .\n->excellent food , although the interior could use some help .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'interior', 'opinion': 'help', 'polarity': 'negative', 'category': 'AMBIENCE#GENERAL'}]\ntext: i don ' t know why google print is so touchy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni don ' t know why google print is so touchy .\n->", + "output": "{\"text\": \"i don ' t know why google print is so touchy .\", \"labels\": \"[{'aspect': 'google print', 'opinion': 'touchy', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great food , great prices , great service .\n->Great food , great prices , great service .\n[{'aspect': 'food', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service was a bit slow , but they were very friendly .\n->The service was a bit slow , but they were very friendly .\n[{'aspect': 'service', 'opinion': 'slow', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the second is also a known trouble spot with android , and that is the microsd card .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe second is also a known trouble spot with android , and that is the microsd card .\n->", + "output": "{\"text\": \"the second is also a known trouble spot with android , and that is the microsd card .\", \"labels\": \"[{'aspect': 'microsd card', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'Out_Of_Scope#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: biggest gripe , no backlights on the keyboard .\n->biggest gripe , no backlights on the keyboard .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: mermaid inn is an overall good restaurant with really good seafood .\n->mermaid inn is an overall good restaurant with really good seafood .\n[{'aspect': 'seafood', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mermaid inn', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: overall excellent machine , great keyboard & trackpad , very fast , battery lasts for hours , but know what you ' re getting into with chrome os .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall excellent machine , great keyboard & trackpad , very fast , battery lasts for hours , but know what you ' re getting into with chrome os .\n->", + "output": "{\"text\": \"overall excellent machine , great keyboard & trackpad , very fast , battery lasts for hours , but know what you ' re getting into with chrome os .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'trackpad', 'opinion': 'great', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the chromebook is simply dead from what i can tell .\n->the chromebook is simply dead from what i can tell .\n[{'aspect': 'chromebook', 'opinion': 'dead', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n->i have to say that i am pleasantly suprised and i will most likely stop in again if i am in the neighborhood .\n[{'aspect': 'NULL', 'opinion': 'pleasantly suprised', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: this is a capable chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a capable chromebook !\n->", + "output": "{\"text\": \"this is a capable chromebook !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'capable', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i ' ll be back for sure .\n->i ' ll be back for sure .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: this chromebook is awesome .\n->this chromebook is awesome .\n[{'aspect': 'chromebook', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: this asus chromebook c302ca does not skip a beat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis asus chromebook c302ca does not skip a beat .\n->", + "output": "{\"text\": \"this asus chromebook c302ca does not skip a beat .\", \"labels\": \"[{'aspect': 'asus chromebook c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it doubles as an android tablet and so far the experience with running android apps has been good .\n->it doubles as an android tablet and so far the experience with running android apps has been good .\n[{'aspect': 'android apps', 'opinion': 'good', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n->when the computer is working hard , it doesn ' t burn my lap anymore ( although i do feel some hot air flowing through my fingertips , when i am typing , but i am okay with that ) .\n[{'aspect': 'NULL', 'opinion': 'okay', 'polarity': 'positive', 'category': 'FANS&COOLING#GENERAL'}]\ntext: this is a good product based on my experience - i have used this for almost a whole month .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is a good product based on my experience - i have used this for almost a whole month .\n->", + "output": "{\"text\": \"this is a good product based on my experience - i have used this for almost a whole month .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i returned it twice .\n->i returned it twice .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: return window is just a month , nothing can be done and now i am on mercy of asus .\n->return window is just a month , nothing can be done and now i am on mercy of asus .\n[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\ntext: the asus flip is perfect for me .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe asus flip is perfect for me .\n->", + "output": "{\"text\": \"the asus flip is perfect for me .\", \"labels\": \"[{'aspect': 'asus flip', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n->i could n ' t even enjoy the mashed potatoes because it was hidden completely under the chicken and spinach .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: As I made the title , it 's an affordable restaurant for great taste .\n->As I made the title , it 's an affordable restaurant for great taste .\n[{'aspect': 'taste', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: so i went ahead and ordered the c302ca , and after a week of use there are no issues to report .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso i went ahead and ordered the c302ca , and after a week of use there are no issues to report .\n->", + "output": "{\"text\": \"so i went ahead and ordered the c302ca , and after a week of use there are no issues to report .\", \"labels\": \"[{'aspect': 'c302ca', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the entrees were served with miso soup and rice .\n->the entrees were served with miso soup and rice .\n[{'aspect': 'entrees', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'FOOD#STYLE_OPTIONS'}]\nExample:\ntext: i ' m giving this five stars considering the price .\n->i ' m giving this five stars considering the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n->", + "output": "{\"text\": \"i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The food was mediocre at best but it was the horrible service that made me vow never to go back .\n->The food was mediocre at best but it was the horrible service that made me vow never to go back .\n[{'aspect': 'food', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: do n ' t get me started on the margaritas , either .\n->do n ' t get me started on the margaritas , either .\n[{'aspect': 'margaritas', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}]\ntext: for anyone wanting a small footprint 1080p chromebook , this one is hard to beat .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nfor anyone wanting a small footprint 1080p chromebook , this one is hard to beat .\n->", + "output": "{\"text\": \"for anyone wanting a small footprint 1080p chromebook , this one is hard to beat .\", \"labels\": \"[{'aspect': 'footprint 1080p chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the screen is surprisingly poor .\n->the screen is surprisingly poor .\n[{'aspect': 'screen', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n->the food is yummy , especially their cooked - to - perfection mussels in spicy tomato sauce and their shoestring crispy fries .\n[{'aspect': 'food', 'opinion': 'yummy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mussels in spicy tomato sauce', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'fries', 'opinion': 'cooked - to - perfection', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: i ' m loving this thing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m loving this thing .\n->", + "output": "{\"text\": \"i ' m loving this thing .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loving', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * excellent form factor , extremely portable while remaining a serious pro computer\n->* excellent form factor , extremely portable while remaining a serious pro computer\n[{'aspect': 'pro computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'pro computer', 'opinion': 'portable', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n->but if you ' re prepared to spend some $ and remember to ask if something they offer is complimentary , then this is the place to go for indian food\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'indian food', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: superb quality , looks and feels like apple , keyboard is great , the touchpad is flawless and the screen is brilliant , battery life is great too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nsuperb quality , looks and feels like apple , keyboard is great , the touchpad is flawless and the screen is brilliant , battery life is great too .\n->", + "output": "{\"text\": \"superb quality , looks and feels like apple , keyboard is great , the touchpad is flawless and the screen is brilliant , battery life is great too .\", \"labels\": \"[{'aspect': 'quality', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'flawless', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'brilliant', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'battery life', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n->Other guests enjoyed pizza , santa fe chopped salad and fish and chips .\n[{'aspect': 'pizza', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'santa fe chopped salad', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish and chips', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: to me it exemplifies soho , cute , artsy , interesting .\n->to me it exemplifies soho , cute , artsy , interesting .\n[{'aspect': 'NULL', 'opinion': 'cute', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'artsy', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'NULL', 'opinion': 'interesting', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: yet , compare with other flip - style of chromebooks , the screen is sufficient for me to do my code .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nyet , compare with other flip - style of chromebooks , the screen is sufficient for me to do my code .\n->", + "output": "{\"text\": \"yet , compare with other flip - style of chromebooks , the screen is sufficient for me to do my code .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'sufficient', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n->Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n[{'aspect': 'food', 'opinion': 'loving', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dining experiences', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: overpriced and not tasty\n->overpriced and not tasty\n[{'aspect': 'NULL', 'opinion': 'overpriced', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'not tasty', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\n->", + "output": "{\"text\": \"the biggest reason for me to pick this laptop over others is the light - weight design and its overall spec .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'spec', 'opinion': 'light - weight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i really do like this computer , however the description is wrong .\n->i really do like this computer , however the description is wrong .\n[{'aspect': 'computer', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'description', 'opinion': 'wrong', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: this staff should be fired .\n->this staff should be fired .\n[{'aspect': 'staff', 'opinion': 'fired', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i like it , good construction , can load android apps .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni like it , good construction , can load android apps .\n->", + "output": "{\"text\": \"i like it , good construction , can load android apps .\", \"labels\": \"[{'aspect': 'construction', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n->the last two times i ordered from here my food was soo spicy that i could barely eat it , and the spice took away from the flavor of the dish .\n[{'aspect': 'food', 'opinion': 'spicy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'spice', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: screen is ips .\n->screen is ips .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#GENERAL'}]\ntext: the main complaints are the touch screen .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe main complaints are the touch screen .\n->", + "output": "{\"text\": \"the main complaints are the touch screen .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'complaints', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the stock video player misses audio on a lot of movies , and using the vlc app ( android ) is super buggy ( frequently freezes and shuts down ) .\n->the stock video player misses audio on a lot of movies , and using the vlc app ( android ) is super buggy ( frequently freezes and shuts down ) .\n[{'aspect': 'stock video player', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}, {'aspect': 'vlc app', 'opinion': 'buggy', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n->the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'travel / feedback', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\ntext: i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am a big chromebook enthusiast , and this is by far the best chromebook built to date .\n->", + "output": "{\"text\": \"i am a big chromebook enthusiast , and this is by far the best chromebook built to date .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'enthusiast', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n->On a recent Sunday afternoon , a friend and I accidently found this great restaurant on our way to see the pulitzer prize winning play DOUBT .\n[{'aspect': 'restaurant', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The service is ok , some of the people did n't get what they asked for .\n->The service is ok , some of the people did n't get what they asked for .\n[{'aspect': 'service', 'opinion': 'ok', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: asus hit all the right notes on this one .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nasus hit all the right notes on this one .\n->", + "output": "{\"text\": \"asus hit all the right notes on this one .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: service was efficient courteous .\n->service was efficient courteous .\n[{'aspect': 'service', 'opinion': 'efficient courteous', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i take all my nyc guests to vt ' s .\n->i take all my nyc guests to vt ' s .\n[{'aspect': \"vt ' s\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: great keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat keyboard .\n->", + "output": "{\"text\": \"great keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the computer is very beautiful and very light .\n->the computer is very beautiful and very light .\n[{'aspect': 'computer', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: ` ` this thing is neat . ` `\n->` ` this thing is neat . ` `\n[{'aspect': 'NULL', 'opinion': 'neat', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\ntext: great display .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat display .\n->", + "output": "{\"text\": \"great display .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sake menu should not be overlooked !\n->The sake menu should not be overlooked !\n[{'aspect': 'sake menu', 'opinion': 'overlooked', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: great little machine while it was functioning 100 % .\n->great little machine while it was functioning 100 % .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: great build materials and quality .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngreat build materials and quality .\n->", + "output": "{\"text\": \"great build materials and quality .\", \"labels\": \"[{'aspect': 'build materials', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'quality', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: For the quality of food , a little too expensive .\n->For the quality of food , a little too expensive .\n[{'aspect': 'quality of food', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: they are clearly working with more than one person at a time , and not effective multi - taskers .\n->they are clearly working with more than one person at a time , and not effective multi - taskers .\n[{'aspect': 'NULL', 'opinion': 'not effective', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\ntext: excellent choice in processor .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nexcellent choice in processor .\n->", + "output": "{\"text\": \"excellent choice in processor .\", \"labels\": \"[{'aspect': 'processor', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'CPU#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we paid a fixed pricce but got nothing ! !\n->we paid a fixed pricce but got nothing ! !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: The food was lousy - too sweet or too salty and the portions tiny .\n->The food was lousy - too sweet or too salty and the portions tiny .\n[{'aspect': 'food', 'opinion': 'lousy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too sweet', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'too salty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: i ' m very happy i chose this unit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m very happy i chose this unit .\n->", + "output": "{\"text\": \"i ' m very happy i chose this unit .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the portions are small but being that the food was so good makes up for that .\n->the portions are small but being that the food was so good makes up for that .\n[{'aspect': 'portions', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: The place was quiet and delightful .\n->The place was quiet and delightful .\n[{'aspect': 'place', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'place', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}]\ntext: battery life is great if you use half of the brightness .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is great if you use half of the brightness .\n->", + "output": "{\"text\": \"battery life is great if you use half of the brightness .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'great', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Food was OK - fish was cooked well .\n->Food was OK - fish was cooked well .\n[{'aspect': 'Food', 'opinion': 'OK', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'cooked well', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: Ballato 's is consistently delicious authentic italian food .\n->Ballato 's is consistently delicious authentic italian food .\n[{'aspect': 'italian food', 'opinion': 'delicious authentic', 'polarity': 'positive', 'category': 'NULL'}]\ntext: after purchasing , this chromebook came with me on a 2 week trip to china .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nafter purchasing , this chromebook came with me on a 2 week trip to china .\n->", + "output": "{\"text\": \"after purchasing , this chromebook came with me on a 2 week trip to china .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: * * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n->* * * update * * * this laptop has already bit the dust ; it crashed with a blue screen of death , and then refused to power back on .\n[{'aspect': 'laptop', 'opinion': 'crashed', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: You should pass on the calamari .\n->You should pass on the calamari .\n[{'aspect': 'calamari', 'opinion': 'pass', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the keyboard has a nice quiet touch .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard has a nice quiet touch .\n->", + "output": "{\"text\": \"the keyboard has a nice quiet touch .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The seats are uncomfortable if you are sitting against the wall on wooden benches .\n->The seats are uncomfortable if you are sitting against the wall on wooden benches .\n[{'aspect': 'seats', 'opinion': 'uncomfortable', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: Try sushimi cucumber roll .\n->Try sushimi cucumber roll .\n[{'aspect': 'sushimi cucumber roll', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it fit nicely into my backpack without taking up space or weighing me down .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit fit nicely into my backpack without taking up space or weighing me down .\n->", + "output": "{\"text\": \"it fit nicely into my backpack without taking up space or weighing me down .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'nicely', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The drinks are a saving grace , but service staff , please , get over yourselves .\n->The drinks are a saving grace , but service staff , please , get over yourselves .\n[{'aspect': 'drinks', 'opinion': 'saving grace', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i take all my nyc guests to vt ' s .\n->i take all my nyc guests to vt ' s .\n[{'aspect': \"vt ' s\", 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: chrome os is intuitive and easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchrome os is intuitive and easy to use .\n->", + "output": "{\"text\": \"chrome os is intuitive and easy to use .\", \"labels\": \"[{'aspect': 'chrome os', 'opinion': 'intuitive', 'polarity': 'positive', 'category': 'OS#USABILITY'}, {'aspect': 'chrome os', 'opinion': 'easy', 'polarity': 'positive', 'category': 'OS#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n->i wanted a large screen laptop with backlit keyboard and a lot of memory and this computer does not disappoint .\n[{'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#GENERAL'}]\nExample:\ntext: Good food .\n->Good food .\n[{'aspect': 'food', 'opinion': 'Good', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\n->", + "output": "{\"text\": \"this little silver bullet is compact , functional , and has held up to everything i ' ve thrown at it .\", \"labels\": \"[{'aspect': 'silver bullet', 'opinion': 'compact', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'silver bullet', 'opinion': 'functional', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speakers on at the front on bottom so sound quality isn ' t the best .\n->the speakers on at the front on bottom so sound quality isn ' t the best .\n[{'aspect': 'speakers', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}, {'aspect': 'sound quality', 'opinion': \"' t the best\", 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#QUALITY'}]\nExample:\ntext: I had the best ravioli ever .\n->I had the best ravioli ever .\n[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: chrome extensions are great productivity tools to boot - they help me squeeze the most out of everyday .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nchrome extensions are great productivity tools to boot - they help me squeeze the most out of everyday .\n->", + "output": "{\"text\": \"chrome extensions are great productivity tools to boot - they help me squeeze the most out of everyday .\", \"labels\": \"[{'aspect': 'chrome extensions', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n->the service is amazing , i ' ve had different waiters and they were all nice , which is a rare thing in nyc .\n[{'aspect': 'service', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'waiters', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: This place is worth an one-hour drive .\n->This place is worth an one-hour drive .\n[{'aspect': 'place', 'opinion': 'worth', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the reason for a 4 star rating is due to the provided power cord failing to charge within two months .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe reason for a 4 star rating is due to the provided power cord failing to charge within two months .\n->", + "output": "{\"text\": \"the reason for a 4 star rating is due to the provided power cord failing to charge within two months .\", \"labels\": \"[{'aspect': 'power cord', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Ambience is delightful , service impeccable .\n->Ambience is delightful , service impeccable .\n[{'aspect': 'Ambience', 'opinion': 'delightful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'impeccable', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: as other reviews have mentioned - its a bit heavier than expected for such a slim frame .\n->as other reviews have mentioned - its a bit heavier than expected for such a slim frame .\n[{'aspect': 'NULL', 'opinion': 'heavier', 'polarity': 'negative', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: the speakers are not great , but bluetooth connection to an external speaker is standard these days and it ' s how we watch movies .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speakers are not great , but bluetooth connection to an external speaker is standard these days and it ' s how we watch movies .\n->", + "output": "{\"text\": \"the speakers are not great , but bluetooth connection to an external speaker is standard these days and it ' s how we watch movies .\", \"labels\": \"[{'aspect': 'bluetooth connection', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'PORTS#CONNECTIVITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend the restaurant based on our experience last night .\n->I highly recommend the restaurant based on our experience last night .\n[{'aspect': 'restaurant', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i can not imagine better indian food in all of the city .\n->i can not imagine better indian food in all of the city .\n[{'aspect': 'indian food', 'opinion': 'better', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nextremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\n->", + "output": "{\"text\": \"extremely disappointed that you can not run android apps on this chromebook , despite the description stating clearly that you can , and the seller responding to my question regarding this question .\", \"labels\": \"[{'aspect': 'seller', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: buyer beware - computer is complete trash .\n->buyer beware - computer is complete trash .\n[{'aspect': 'computer', 'opinion': 'trash', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: i just got this yesterday and i am very satisfied with the speed .\n->i just got this yesterday and i am very satisfied with the speed .\n[{'aspect': 'speed', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: other than that i ' m pleased with the performance .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nother than that i ' m pleased with the performance .\n->", + "output": "{\"text\": \"other than that i ' m pleased with the performance .\", \"labels\": \"[{'aspect': 'performance', 'opinion': 'pleased', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: speaker is only good for watching a movie in a quiet room .\n->speaker is only good for watching a movie in a quiet room .\n[{'aspect': 'speaker', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\nExample:\ntext: its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n->its an ok laptop , it makes crackling annoying noises sometimes , im worried that it will become worse with time so i will be returning it , also the speakers are really bad , keyboard is good enough .\n[{'aspect': 'laptop', 'opinion': 'annoying', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'bad', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'keyboard', 'opinion': 'good', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: this chromebook is amazing , i have had zero issues with it thus far and i ' ve used it quite extensively during the semester .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis chromebook is amazing , i have had zero issues with it thus far and i ' ve used it quite extensively during the semester .\n->", + "output": "{\"text\": \"this chromebook is amazing , i have had zero issues with it thus far and i ' ve used it quite extensively during the semester .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n->I highly recommend Caviar Russe to anyone who wants delicious top grade caviar and fantastic service .\n[{'aspect': 'caviar', 'opinion': 'delicious top grade', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the keyboard is excellent ( and backlit ) .\n->the keyboard is excellent ( and backlit ) .\n[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'backlit', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the keyboard on this thing is my favorite part of the computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard on this thing is my favorite part of the computer .\n->", + "output": "{\"text\": \"the keyboard on this thing is my favorite part of the computer .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'computer', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n->Took my mom for Mother 's Day , and the maitre d ' was pretty rude .\n[{'aspect': \"maitre d '\", 'opinion': 'rude', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: If we were to move from the upper east side , we would genuinely miss this restaurant .\n->If we were to move from the upper east side , we would genuinely miss this restaurant .\n[{'aspect': 'restaurant', 'opinion': 'miss', 'polarity': 'positive', 'category': 'NULL'}]\ntext: it feels amazing and the travel is perfect .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit feels amazing and the travel is perfect .\n->", + "output": "{\"text\": \"it feels amazing and the travel is perfect .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'travel', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'KEYBOARD#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n->The sicilian is my favorite it is moist not dry like most places but all their pizza is great !\n[{'aspect': 'pizza', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sicilian', 'opinion': 'moist not dry', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: at taj , vegetarians can rejoice-all the dishes are manna from heaven .\n->at taj , vegetarians can rejoice-all the dishes are manna from heaven .\n[{'aspect': 'dishes', 'opinion': 'heaven', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this biggest weak point and my only major complaint is that the speakers on it just suck .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis biggest weak point and my only major complaint is that the speakers on it just suck .\n->", + "output": "{\"text\": \"this biggest weak point and my only major complaint is that the speakers on it just suck .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'weak', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'speakers', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}, {'aspect': 'speakers', 'opinion': 'suck', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was very excited at the prospect of buying this laptop .\n->i was very excited at the prospect of buying this laptop .\n[{'aspect': 'laptop', 'opinion': 'excited', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: for me , it ' s been fantastic .\n->for me , it ' s been fantastic .\n[{'aspect': 'NULL', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: they are loud enough to fill a small , quiet room , but that is about it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthey are loud enough to fill a small , quiet room , but that is about it .\n->", + "output": "{\"text\": \"they are loud enough to fill a small , quiet room , but that is about it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'loud', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This is a wonderful place on all stand points especially value ofr money .\n->This is a wonderful place on all stand points especially value ofr money .\n[{'aspect': 'value ofr money', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n->The food , served in small tasting portions ( as an option ) is very good with each dish being better than the next .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'portions', 'opinion': 'small', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dish', 'opinion': 'better', 'polarity': 'positive', 'category': 'NULL'}]\ntext: overall , i am very happy with this purchase , and i am in love with the simplicity of the google ecosystem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall , i am very happy with this purchase , and i am in love with the simplicity of the google ecosystem .\n->", + "output": "{\"text\": \"overall , i am very happy with this purchase , and i am in love with the simplicity of the google ecosystem .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'google ecosystem', 'opinion': 'love', 'polarity': 'positive', 'category': 'Out_Of_Scope#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: highly recommend it !\n->highly recommend it !\n[{'aspect': 'NULL', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n->Despite a slightly limited menu , everything prepared is done to perfection , ultra fresh and a work of food art .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food art', 'opinion': 'ultra fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: this battery lasts for 5 + hours on some of the most taxing apps my phone can run .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis battery lasts for 5 + hours on some of the most taxing apps my phone can run .\n->", + "output": "{\"text\": \"this battery lasts for 5 + hours on some of the most taxing apps my phone can run .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: great device until battery won ' t charge .\n->great device until battery won ' t charge .\n[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'BATTERY#QUALITY'}]\nExample:\ntext: we had pam ' s special fried fish and it was amazing .\n->we had pam ' s special fried fish and it was amazing .\n[{'aspect': \"pam ' s special fried fish\", 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the pic quality is pretty good , though not 4k , the sound is pretty good for a laptop that is less than a half inch thick and weighs less than 3 pounds .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe pic quality is pretty good , though not 4k , the sound is pretty good for a laptop that is less than a half inch thick and weighs less than 3 pounds .\n->", + "output": "{\"text\": \"the pic quality is pretty good , though not 4k , the sound is pretty good for a laptop that is less than a half inch thick and weighs less than 3 pounds .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i like the laptop for it ' s hardware , and it ' s working properly .\n->i like the laptop for it ' s hardware , and it ' s working properly .\n[{'aspect': 'hardware', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n->it was $ 14 not really bad for a pound of pastrami - but it did n ' t have much taste - i ' ve had better for less elsewhere !\n[{'aspect': 'NULL', 'opinion': 'not really bad', 'polarity': 'neutral', 'category': 'FOOD#PRICES'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the touchscreen is great and the backlit keyboard is fantastic .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe touchscreen is great and the backlit keyboard is fantastic .\n->", + "output": "{\"text\": \"the touchscreen is great and the backlit keyboard is fantastic .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'backlit keyboard', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n->The decor is dark , cool and soothing , while the food 's presentation is spectacular , considering the low prices .\n[{'aspect': 'decor', 'opinion': 'dark', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'cool', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'decor', 'opinion': 'soothing', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'low', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': \"food 's presentation\", 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i wish i could be refunded !\n->i wish i could be refunded !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: asus is a great computer company .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nasus is a great computer company .\n->", + "output": "{\"text\": \"asus is a great computer company .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}, {'aspect': 'computer company', 'opinion': 'great', 'polarity': 'positive', 'category': 'COMPANY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: try the pad thai , it ' s fabulous and their prices are so cheap !\n->try the pad thai , it ' s fabulous and their prices are so cheap !\n[{'aspect': 'pad thai', 'opinion': 'try', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'fabulous', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pad thai', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'so cheap', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: And the Tom Kha soup was pathetic .\n->And the Tom Kha soup was pathetic .\n[{'aspect': 'Tom Kha soup', 'opinion': 'pathetic', 'polarity': 'negative', 'category': 'NULL'}]\ntext: while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhile i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\n->", + "output": "{\"text\": \"while i hate that it doesn ' t have a caps key , so i have to keep using the shift key to capitalize my letters , the device itself is lightweight , has a very nice backlit keyboard , and works well for the purposes of taking it on the road and using it with wifi at your local coffeeshop .\", \"labels\": \"[{'aspect': 'device', 'opinion': 'lightweight', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'backlit keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Delicious crab cakes too .\n->Delicious crab cakes too .\n[{'aspect': 'crab cakes', 'opinion': 'Delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: my only issue is the wifi likes to randomly turn off then back on .\n->my only issue is the wifi likes to randomly turn off then back on .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#CONNECTIVITY'}]\ntext: the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\n->", + "output": "{\"text\": \"the aluminum construction is every bit as nice as my 12 ` ` macbook , the keyboard is excellent and has better travel / feedback and the trackpad is the best i ' ve used on any asus portable .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'travel / feedback', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'trackpad', 'opinion': 'best', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n->i can not explain why , except to think that there is something wrong with the voltage wiring and the left channel is exceeding design capacity .\n[{'aspect': 'voltage', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: gross food \u2013 wow -\n->gross food \u2013 wow -\n[{'aspect': 'food', 'opinion': 'gross', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\n->", + "output": "{\"text\": \"the 1080p screen is bright and has decent viewing angles , the hinge is smooth and solid enough to not shake when typing on your lap .\", \"labels\": \"[{'aspect': '1080p screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': '1080p screen', 'opinion': 'decent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'hinge', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'hinge', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - side buttons are responsive and well - made\n->- side buttons are responsive and well - made\n[{'aspect': 'side buttons', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'side buttons', 'opinion': 'well - made', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: Who has room for Cheesesticks with the best pizza in NYC !\n->Who has room for Cheesesticks with the best pizza in NYC !\n[{'aspect': 'pizza', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i ' ve found the touch screen is pretty handy .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve found the touch screen is pretty handy .\n->", + "output": "{\"text\": \"i ' ve found the touch screen is pretty handy .\", \"labels\": \"[{'aspect': 'touch screen', 'opinion': 'handy', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: a must try !\n->a must try !\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: when it works it ' s a great device .\n->when it works it ' s a great device .\n[{'aspect': 'device', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it ' s very quick , despite the dsl internet connection we have .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s very quick , despite the dsl internet connection we have .\n->", + "output": "{\"text\": \"it ' s very quick , despite the dsl internet connection we have .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: An excellent service\n->An excellent service\n[{'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: not impressed with the food .\n->not impressed with the food .\n[{'aspect': 'food', 'opinion': 'not impressed', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: i ' ve only utilized the 360 degree opening once and so far i like it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' ve only utilized the 360 degree opening once and so far i like it .\n->", + "output": "{\"text\": \"i ' ve only utilized the 360 degree opening once and so far i like it .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this little computer is very fast and does a great job .\n->this little computer is very fast and does a great job .\n[{'aspect': 'computer', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the anti - pasta was excellent , especially the calamari , as were the filling pasta mains .\n->the anti - pasta was excellent , especially the calamari , as were the filling pasta mains .\n[{'aspect': 'anti - pasta', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'calamari', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'pasta mains', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'FOOD#STYLE_OPTIONS'}]\ntext: this is my first chromebook , and i ' m absolutely loving it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is my first chromebook , and i ' m absolutely loving it .\n->", + "output": "{\"text\": \"this is my first chromebook , and i ' m absolutely loving it .\", \"labels\": \"[{'aspect': 'this', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n->it ' ll be interesting to see what the revisions are in 2016 , but i was worried about the shallow keys of the new macbook making their way to the mbp and the loss of port options with a new revision , so i am very happy to have this now .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the screen quality is excellent , and i am fussy due to my interest in digital imagery .\n->the screen quality is excellent , and i am fussy due to my interest in digital imagery .\n[{'aspect': 'screen quality', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\ntext: i ' m beyond satisfied with this chromebook , it is stunning in every way .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m beyond satisfied with this chromebook , it is stunning in every way .\n->", + "output": "{\"text\": \"i ' m beyond satisfied with this chromebook , it is stunning in every way .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'chromebook', 'opinion': 'stunning', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i love this laptop !\n->i love this laptop !\n[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it just stopped working in the middle of my paper i was writing .\n->it just stopped working in the middle of my paper i was writing .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: no problems with play store / android apps .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nno problems with play store / android apps .\n->", + "output": "{\"text\": \"no problems with play store / android apps .\", \"labels\": \"[{'aspect': 'play store / android apps', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n->this place is really trendi but they have forgotten about the most important part of a restaurant , the food .\n[{'aspect': 'food', 'opinion': 'forgotten', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'place', 'opinion': 'trendi', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\nExample:\ntext: this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n->this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n[{'aspect': 'computer', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: light weight , very convenient to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlight weight , very convenient to use .\n->", + "output": "{\"text\": \"light weight , very convenient to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'convenient', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this was a great surprise .\n->this was a great surprise .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: great price for a brand new product .\n->great price for a brand new product .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: nice keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnice keyboard .\n->", + "output": "{\"text\": \"nice keyboard .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my first chromebook , and so far ( about one month of use ) i like it .\n->my first chromebook , and so far ( about one month of use ) i like it .\n[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: check this place out !\n->check this place out !\n[{'aspect': 'place', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: i would never have thought that a chromebook would be so fun to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni would never have thought that a chromebook would be so fun to use .\n->", + "output": "{\"text\": \"i would never have thought that a chromebook would be so fun to use .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fun', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Another plus is most of the entrees are approx .\n->Another plus is most of the entrees are approx .\n[{'aspect': 'entrees', 'opinion': 'plus', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: fish was overdone .\n->fish was overdone .\n[{'aspect': 'fish', 'opinion': 'overdone', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: its really quick at loading web pages .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nits really quick at loading web pages .\n->", + "output": "{\"text\": \"its really quick at loading web pages .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'quick', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: delicious simple food in nice outdoor atmosphere .\n->delicious simple food in nice outdoor atmosphere .\n[{'aspect': 'food', 'opinion': 'delicious simple', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'nice', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: bought this for my daughter ' s senior year of college and she ' s very happy .\n->bought this for my daughter ' s senior year of college and she ' s very happy .\n[{'aspect': 'NULL', 'opinion': 'happy', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i love how much you can customize this and also it is pretty speedy !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love how much you can customize this and also it is pretty speedy !\n->", + "output": "{\"text\": \"i love how much you can customize this and also it is pretty speedy !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'speedy', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it ' s lightning fast and handles games like skyrim and the witcher 3 surprisingly smoothly for the price i paid .\n->it ' s lightning fast and handles games like skyrim and the witcher 3 surprisingly smoothly for the price i paid .\n[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'smoothly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: the internal flash memory is like greased lightning .\n->the internal flash memory is like greased lightning .\n[{'aspect': 'flash memory', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'MEMORY#OPERATION_PERFORMANCE'}]\ntext: definitely the best chromebook out there .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely the best chromebook out there .\n->", + "output": "{\"text\": \"definitely the best chromebook out there .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the veal and the mushrooms were cooked perfectly .\n->the veal and the mushrooms were cooked perfectly .\n[{'aspect': 'veal', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mushrooms', 'opinion': 'perfectly', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: the waitress , seems to be more concerned of looking good than actually waitressing .\n->the waitress , seems to be more concerned of looking good than actually waitressing .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: i ' d say the only drawback might be the speakers .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' d say the only drawback might be the speakers .\n->", + "output": "{\"text\": \"i ' d say the only drawback might be the speakers .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'drawback', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: All in all the food was above average and I would return to see how they operate with four or less dinners .\n->All in all the food was above average and I would return to see how they operate with four or less dinners .\n[{'aspect': 'food', 'opinion': 'above average', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: still we keep going back : )\n->still we keep going back : )\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: love the backlit keyboard not a lot of screen wobble - was a bit worried about the screen ratio in tablet mode but it ' s just fine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlove the backlit keyboard not a lot of screen wobble - was a bit worried about the screen ratio in tablet mode but it ' s just fine .\n->", + "output": "{\"text\": \"love the backlit keyboard not a lot of screen wobble - was a bit worried about the screen ratio in tablet mode but it ' s just fine .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'screen ratio', 'opinion': 'worried', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'screen ratio', 'opinion': 'fine', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as a refurbished item it was indistinguishable from a new item .\n->as a refurbished item it was indistinguishable from a new item .\n[{'aspect': 'item', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: excellent packaging .\n->excellent packaging .\n[{'aspect': 'NULL', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'SHIPPING#GENERAL'}]\ntext: this unit is extremely well built .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis unit is extremely well built .\n->", + "output": "{\"text\": \"this unit is extremely well built .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'well built', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Should you happen to be impressed by the cuisine definitely try it .\n->Should you happen to be impressed by the cuisine definitely try it .\n[{'aspect': 'cuisine', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'cuisine', 'opinion': 'try', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: My husband and I enjoyed each of the 6 taste size portions and left completely full .\n->My husband and I enjoyed each of the 6 taste size portions and left completely full .\n[{'aspect': 'portions', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the speed at which this charges and reboots is amazing , and the battery life is long .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe speed at which this charges and reboots is amazing , and the battery life is long .\n->", + "output": "{\"text\": \"the speed at which this charges and reboots is amazing , and the battery life is long .\", \"labels\": \"[{'aspect': 'charges', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'reboots', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the sushi is amazing ! ! !\n->the sushi is amazing ! ! !\n[{'aspect': 'sushi', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: stick with the chicken , beef , and lamb dishes .\n->stick with the chicken , beef , and lamb dishes .\n[{'aspect': 'chicken', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'beef', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'lamb dishes', 'opinion': 'stick', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the keyboard is also one of the nicest most comfortable i have ever used .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is also one of the nicest most comfortable i have ever used .\n->", + "output": "{\"text\": \"the keyboard is also one of the nicest most comfortable i have ever used .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: In the evening , this place attracted a well dressed , with it , NY crowd .\n->In the evening , this place attracted a well dressed , with it , NY crowd .\n[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: it has a good blend of functionality and performance at a great price point .\n->it has a good blend of functionality and performance at a great price point .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nin closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\n->", + "output": "{\"text\": \"in closing , if you are looking for a nice on the go computer or something to take to college , this is an excellent choice .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: food was just average . . . if they lowered the prices just a bit , it would be a bigger draw .\n->food was just average . . . if they lowered the prices just a bit , it would be a bigger draw .\n[{'aspect': 'food', 'opinion': 'average', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: beyond that , less than a week into the ownership trial , the power _ supply failed .\n->beyond that , less than a week into the ownership trial , the power _ supply failed .\n[{'aspect': 'power _ supply failed', 'opinion': '.', 'polarity': 'negative', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\ntext: the screen is crisp and bright .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is crisp and bright .\n->", + "output": "{\"text\": \"the screen is crisp and bright .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'crisp', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'bright', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n->slightly above average wines start at $ 70 + with only one selection listed at $ 30 + .\n[{'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#QUALITY'}, {'aspect': 'wines', 'opinion': 'above average', 'polarity': 'negative', 'category': 'DRINKS#PRICES'}]\nExample:\ntext: Again , the waitress was awesome .\n->Again , the waitress was awesome .\n[{'aspect': 'waitress', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'NULL'}]\ntext: battery life is good at about 10 hours .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbattery life is good at about 10 hours .\n->", + "output": "{\"text\": \"battery life is good at about 10 hours .\", \"labels\": \"[{'aspect': 'battery life', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in the evening , this place attracted a well dressed , with it , ny crowd .\n->in the evening , this place attracted a well dressed , with it , ny crowd .\n[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: prices are very good .\n->prices are very good .\n[{'aspect': 'NULL', 'opinion': 'good', 'polarity': 'positive', 'category': 'RESTAURANT#PRICES'}]\ntext: the keyboard is spacious , and has a nice tactile feel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is spacious , and has a nice tactile feel .\n->", + "output": "{\"text\": \"the keyboard is spacious , and has a nice tactile feel .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'spacious', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Most importantly , food is excellent .\n->Most importantly , food is excellent .\n[{'aspect': 'food', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the machine looks amazing doesn ' t it !\n->the machine looks amazing doesn ' t it !\n[{'aspect': 'machine', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: running android apps themselves are a pretty ` ` meh ` ` experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nrunning android apps themselves are a pretty ` ` meh ` ` experience .\n->", + "output": "{\"text\": \"running android apps themselves are a pretty ` ` meh ` ` experience .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'meh', 'polarity': 'negative', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu is very limited - i think we counted 4 or 5 entrees .\n->The menu is very limited - i think we counted 4 or 5 entrees .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: we ' d go back again\n->we ' d go back again\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: games are hit and miss .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngames are hit and miss .\n->", + "output": "{\"text\": \"games are hit and miss .\", \"labels\": \"[{'aspect': 'games', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SOFTWARE#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: good price , good quality and good service in korea .\n->good price , good quality and good service in korea .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'service', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: the computer literally blue screened on the second day because system 32 was corrupt .\n->the computer literally blue screened on the second day because system 32 was corrupt .\n[{'aspect': 'system 32', 'opinion': 'corrupt', 'polarity': 'negative', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\n->", + "output": "{\"text\": \"as far as a chromebook device this is the chromebook to get in early 2017 , and worth every bit of the $ 499 price tag imho .\", \"labels\": \"[{'aspect': 'chromebook device', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'worth', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: we did tip , i guess the model / waitress just wanted more and complained to the manager .\n->we did tip , i guess the model / waitress just wanted more and complained to the manager .\n[{'aspect': 'waitress', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n->We usually just get some of the dinner specials and they are very reasonably priced and very tasty .\n[{'aspect': 'dinner specials', 'opinion': 'reasonably priced', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'dinner specials', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'priced', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'NULL'}]\ntext: a great chromebook .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \na great chromebook .\n->", + "output": "{\"text\": \"a great chromebook .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n->for example , many of the apps currently available on the play store which work perfectly well on your android phone or tablet do not work in chromeos .\n[{'aspect': 'chromeos', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'OS#USABILITY'}]\nExample:\ntext: we ' d go back again\n->we ' d go back again\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\ntext: looks wise it ' s beautiful , i love the minimal design and layout .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nlooks wise it ' s beautiful , i love the minimal design and layout .\n->", + "output": "{\"text\": \"looks wise it ' s beautiful , i love the minimal design and layout .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'design', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'layout', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the farro salad and the mashed yukon potatoes were also extremely tasty .\n->the farro salad and the mashed yukon potatoes were also extremely tasty .\n[{'aspect': 'farro salad', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'mashed yukon potatoes', 'opinion': 'tasty', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: - fast boot up ( 3 seconds )\n->- fast boot up ( 3 seconds )\n[{'aspect': 'boot up', 'opinion': 'fast', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}]\ntext: the keyboard feels nice to use , the keys have a satisfying travel .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard feels nice to use , the keys have a satisfying travel .\n->", + "output": "{\"text\": \"the keyboard feels nice to use , the keys have a satisfying travel .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keys', 'opinion': 'satisfying', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: people said it wasn ' t bright enough but i run at 50 to 75 percent and its white bright .\n->people said it wasn ' t bright enough but i run at 50 to 75 percent and its white bright .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n->initial impression is that this thing is super light , almost 1 / 2 the weight of my asus ux501vw but with the same screen dimensions .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: touchpad is nice and responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntouchpad is nice and responsive .\n->", + "output": "{\"text\": \"touchpad is nice and responsive .\", \"labels\": \"[{'aspect': 'touchpad', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touchpad', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n->the shame of it is knowing it took me 15 minutes and $ 12 to fix it and acer wanted to rob me of $ 170 just to look at it .\n[{'aspect': 'acer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n->this was my frist time at cafe st . bart ' s and i must say how delicious the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: the included charger charges it very quickly though .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe included charger charges it very quickly though .\n->", + "output": "{\"text\": \"the included charger charges it very quickly though .\", \"labels\": \"[{'aspect': 'included charger', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Priced at upper intermediate range .\n->Priced at upper intermediate range .\n[{'aspect': 'Priced', 'opinion': 'upper intermediate', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n->first one had a white line on the screen , then went and got a replacement , not even six months into this one and the graphics card failed .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}, {'aspect': 'graphics', 'opinion': 'failed', 'polarity': 'negative', 'category': 'GRAPHICS#GENERAL'}]\ntext: definitely recommend this chromebook , it ' s a beautiful machine .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndefinitely recommend this chromebook , it ' s a beautiful machine .\n->", + "output": "{\"text\": \"definitely recommend this chromebook , it ' s a beautiful machine .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'machine', 'opinion': 'beautiful', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i took one bite from the $ 24 salmon , and i have never , in the 17 years i have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in flatbush farms .\n->i took one bite from the $ 24 salmon , and i have never , in the 17 years i have been going to restaurants tasted salmon as fishy , as dry , and as bland as the one in flatbush farms .\n[{'aspect': 'salmon', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'FOOD#PRICES'}, {'aspect': 'salmon', 'opinion': 'fishy', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon', 'opinion': 'dry', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'salmon', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: this is the first macbook i have ever purchased , i wish i had purchased sooner .\n->this is the first macbook i have ever purchased , i wish i had purchased sooner .\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: but this asus c302ca has blown me away .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut this asus c302ca has blown me away .\n->", + "output": "{\"text\": \"but this asus c302ca has blown me away .\", \"labels\": \"[{'aspect': 'asus c302ca', 'opinion': 'blown me away', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The portion sizes here are huge , and the sushi is good .\n->The portion sizes here are huge , and the sushi is good .\n[{'aspect': 'portion sizes', 'opinion': 'huge', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'good', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: every time i have a special occasion with my boyfriend i have a hard time going anywhere else .\n->every time i have a special occasion with my boyfriend i have a hard time going anywhere else .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\ntext: portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nportability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\n->", + "output": "{\"text\": \"portability is also great due to the fact it ' s slim and very light and the fact it ' s a convertible is added value .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'slim', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'convertible', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the problem is that it never charged .\n->the problem is that it never charged .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: it ' s now been totally reliable for half a year or so .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s now been totally reliable for half a year or so .\n->", + "output": "{\"text\": \"it ' s now been totally reliable for half a year or so .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'reliable', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n->product keyboard just stopped working after 6 months of use - called asus , and you have to ship it off for a two weeks repair .\n[{'aspect': 'keyboard', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: do your research , learn how to optimize your experience , and you ' ll love it !\n->do your research , learn how to optimize your experience , and you ' ll love it !\n[{'aspect': 'NULL', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the build quality on this laptop is awesome for the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe build quality on this laptop is awesome for the price .\n->", + "output": "{\"text\": \"the build quality on this laptop is awesome for the price .\", \"labels\": \"[{'aspect': 'build quality', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: hope this review can save others from the initial hassle i endured because the chromebook 3 looks terrific in any other way .\n->hope this review can save others from the initial hassle i endured because the chromebook 3 looks terrific in any other way .\n[{'aspect': 'chromebook 3', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: it was free with my phone , so the price was phenomenal .\n->it was free with my phone , so the price was phenomenal .\n[{'aspect': 'NULL', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: the keyboard is clicky , has decent amount of travel , and is backlit .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is clicky , has decent amount of travel , and is backlit .\n->", + "output": "{\"text\": \"the keyboard is clicky , has decent amount of travel , and is backlit .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'clicky', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - the bread at the beginning is super tasty and makes you want more - the pizza is delicious and comes in personal sizes , however be warned that the Peter 's Favourite pizza with prosciutto and baby arugula is actually a margarite pizza with cold prosciutto and baby arugula on top , like a salad .\n->- the bread at the beginning is super tasty and makes you want more - the pizza is delicious and comes in personal sizes , however be warned that the Peter 's Favourite pizza with prosciutto and baby arugula is actually a margarite pizza with cold prosciutto and baby arugula on top , like a salad .\n[{'aspect': 'bread', 'opinion': 'super tasty', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'pizza', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The exotic food is beautifully presented and is a delight in delicious combinations .\n->The exotic food is beautifully presented and is a delight in delicious combinations .\n[{'aspect': 'exotic food', 'opinion': 'beautifully presented', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'exotic food', 'opinion': 'delight', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\n->", + "output": "{\"text\": \"the screen is very good all around , though it may get bright enough for comfortable outdoor use in direct sunlight .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}, {'aspect': 'screen', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'DISPLAY#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n->Considering we were the last patrons there and it was after the closing time , the waitstaff did not rush us at all and made us feel comfortable and relaxed .\n[{'aspect': 'waitstaff', 'opinion': 'comfortable', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waitstaff', 'opinion': 'relaxed', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: screen maximum brightness is still not bright enough\n->screen maximum brightness is still not bright enough\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\ntext: the processor is very snappy and will likely never be an issue for anything a normal chromebook user would need it for .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe processor is very snappy and will likely never be an issue for anything a normal chromebook user would need it for .\n->", + "output": "{\"text\": \"the processor is very snappy and will likely never be an issue for anything a normal chromebook user would need it for .\", \"labels\": \"[{'aspect': 'processor', 'opinion': 'snappy', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i hate the new keyboard the newer version comes with .\n->i hate the new keyboard the newer version comes with .\n[{'aspect': 'keyboard', 'opinion': 'hate', 'polarity': 'negative', 'category': 'KEYBOARD#GENERAL'}]\nExample:\ntext: i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n->i read reviews that called the restaurant too expensive and i thought to myself , but may be it is worth it .\n[{'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}]\ntext: the screen wasn ' t as clear or bright as i hoped .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen wasn ' t as clear or bright as i hoped .\n->", + "output": "{\"text\": \"the screen wasn ' t as clear or bright as i hoped .\", \"labels\": \"[{'aspect': 'screen', 'opinion': \"' t as clear or bright\", 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the staff is very good .\n->the staff is very good .\n[{'aspect': 'staff', 'opinion': 'good', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: can \u2019 t believe how an expensive nyc restaurant can be so disrespectful to its clients .\n->can \u2019 t believe how an expensive nyc restaurant can be so disrespectful to its clients .\n[{'aspect': 'restaurant', 'opinion': 'expensive', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}, {'aspect': 'NULL', 'opinion': 'disrespectful', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: it starts up right away and has decent battery life that looks nice .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit starts up right away and has decent battery life that looks nice .\n->", + "output": "{\"text\": \"it starts up right away and has decent battery life that looks nice .\", \"labels\": \"[{'aspect': 'starts up', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'decent', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'nice', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just straight up cheap , good food .\n->just straight up cheap , good food .\n[{'aspect': 'food', 'opinion': 'good', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'food', 'opinion': 'cheap', 'polarity': 'positive', 'category': 'FOOD#PRICES'}]\nExample:\ntext: The menu is limited but almost all of the dishes are excellent .\n->The menu is limited but almost all of the dishes are excellent .\n[{'aspect': 'menu', 'opinion': 'limited', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'dishes', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: when it has served me well , this asus chromebook flip c302ca is amazing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen it has served me well , this asus chromebook flip c302ca is amazing .\n->", + "output": "{\"text\": \"when it has served me well , this asus chromebook flip c302ca is amazing .\", \"labels\": \"[{'aspect': 'asus chromebook flip c302ca', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'asus chromebook flip c302ca', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n->my girlfriend and i stumbled onto this hopping place the other night and had a great time !\n[{'aspect': 'place', 'opinion': 'great time', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n->it feels like a tablet / computer for a child since it ' s so bulky and heavy .\n[{'aspect': 'tablet', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'tablet', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'bulky', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'computer', 'opinion': 'heavy', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ndownloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\n->", + "output": "{\"text\": \"downloading android apps was easy after doing a standard os update , and really adds to the chrome experience .\", \"labels\": \"[{'aspect': 'downloading android apps', 'opinion': 'easy', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The staff was the friendliest that have seen in New York .\n->The staff was the friendliest that have seen in New York .\n[{'aspect': 'staff', 'opinion': 'friendliest', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: i love margherita pizza \u2013 i looove east village pizza\n->i love margherita pizza \u2013 i looove east village pizza\n[{'aspect': 'margherita pizza', 'opinion': 'love', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'east village pizza', 'opinion': 'looove', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntouchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\n->", + "output": "{\"text\": \"touchscreen is phenomenal and although awkward to hold when in full tablet mode , a neat feature to have .\", \"labels\": \"[{'aspect': 'touchscreen', 'opinion': 'phenomenal', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'touchscreen', 'opinion': 'neat', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'tablet mode', 'opinion': 'awkward', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This place is not worth the prices .\n->This place is not worth the prices .\n[{'aspect': 'prices', 'opinion': 'not worth', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: THe Pizza and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .\n->THe Pizza and wine were excellent -- the service too -- but what really MADE this place was the backyard dining area .\n[{'aspect': 'Pizza', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'NULL'}]\ntext: speakers are nice and loud .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nspeakers are nice and loud .\n->", + "output": "{\"text\": \"speakers are nice and loud .\", \"labels\": \"[{'aspect': 'speakers', 'opinion': 'nice', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'loud', 'polarity': 'positive', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n->I had a terrific meal , and our server guided us toward a very nice wine in our price range , instead of allowing us to purchase a similarly priced wine that was n't as good .\n[{'aspect': 'meal', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'wine', 'opinion': 'nice', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n->i ' m probably a bit biased on macs since i ' m a professional graphic designer , and it ' s just the industry standard .\n[{'aspect': 'NULL', 'opinion': 'biased', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\ntext: i give it 4 stars because i believe that these devices are perfect for adults who just want to surf the internet or watch netflix .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni give it 4 stars because i believe that these devices are perfect for adults who just want to surf the internet or watch netflix .\n->", + "output": "{\"text\": \"i give it 4 stars because i believe that these devices are perfect for adults who just want to surf the internet or watch netflix .\", \"labels\": \"[{'aspect': 'devices', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n->Still , try it once , since if you end up loving the food , it could be one of your best dining experiences .\n[{'aspect': 'food', 'opinion': 'loving', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'dining experiences', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: we received it in august , and it worked fine for just 5 months and then the touch screen stopped working .\n->we received it in august , and it worked fine for just 5 months and then the touch screen stopped working .\n[{'aspect': 'NULL', 'opinion': 'fine', 'polarity': 'neutral', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'touch screen', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\ntext: not having to switch to / from desktop version of websites is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nnot having to switch to / from desktop version of websites is great .\n->", + "output": "{\"text\": \"not having to switch to / from desktop version of websites is great .\", \"labels\": \"[{'aspect': 'websites', 'opinion': 'great', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this thing is amazing .\n->this thing is amazing .\n[{'aspect': 'NULL', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: speakers sound tinny .\n->speakers sound tinny .\n[{'aspect': 'speakers', 'opinion': 'tinny', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: overall it has been a wonder experience and quality product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \noverall it has been a wonder experience and quality product .\n->", + "output": "{\"text\": \"overall it has been a wonder experience and quality product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'wonder', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'product', 'opinion': 'quality', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n->This was my frist time at Cafe St. Bart 's and I must say how delicious the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicious', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the back lit keyboard is one of the nicest keyboards i have ever typed on .\n->the back lit keyboard is one of the nicest keyboards i have ever typed on .\n[{'aspect': 'back lit keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboards', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\ntext: the chromebook looks very nice and works very well .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe chromebook looks very nice and works very well .\n->", + "output": "{\"text\": \"the chromebook looks very nice and works very well .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'chromebook', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice little laptop .\n->nice little laptop .\n[{'aspect': 'laptop', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n->the form factor is nice , but the power key is in the upper right , easy to hit when using backspac nor page up .\n[{'aspect': 'form factor', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'power key', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: i use it mostly when traveling it works well for that purpose .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use it mostly when traveling it works well for that purpose .\n->", + "output": "{\"text\": \"i use it mostly when traveling it works well for that purpose .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: go there once and oh yes . . . you will go back . . . you will . . .\n->go there once and oh yes . . . you will go back . . . you will . . .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n->i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n[{'aspect': 'touch pad', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\ntext: i went with this one because keys are better and i like the laptop feel and look to it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni went with this one because keys are better and i like the laptop feel and look to it .\n->", + "output": "{\"text\": \"i went with this one because keys are better and i like the laptop feel and look to it .\", \"labels\": \"[{'aspect': 'keys', 'opinion': 'better', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'laptop', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: for each course we waited over 1 / 2 hour to 45 minutes and were never offered a drink .\n->for each course we waited over 1 / 2 hour to 45 minutes and were never offered a drink .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: picked this up as something light and easy to carry around for working on personal coding projects while riding the bus .\n->picked this up as something light and easy to carry around for working on personal coding projects while riding the bus .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: this laptop will be for school , netflix and youtube mostly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis laptop will be for school , netflix and youtube mostly .\n->", + "output": "{\"text\": \"this laptop will be for school , netflix and youtube mostly .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The well mannered , pleasant staff that Tony has in his employ .\n->The well mannered , pleasant staff that Tony has in his employ .\n[{'aspect': 'staff', 'opinion': 'pleasant', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the food is outstanding and the service is quick , friendly and very professional .\n->the food is outstanding and the service is quick , friendly and very professional .\n[{'aspect': 'food', 'opinion': 'outstanding', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'service', 'opinion': 'quick', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'professional', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: i bought this for $ 389 on cyber monday 2017 .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni bought this for $ 389 on cyber monday 2017 .\n->", + "output": "{\"text\": \"i bought this for $ 389 on cyber monday 2017 .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: samsung needs to fix that issue .\n->samsung needs to fix that issue .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'COMPANY#GENERAL'}]\nExample:\ntext: ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n->ordered in november 2017 but used fewer than 100 hours when vertical red line appeared on monitor .\n[{'aspect': 'monitor', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\ntext: if you ' re on the fence i recommend this asus .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nif you ' re on the fence i recommend this asus .\n->", + "output": "{\"text\": \"if you ' re on the fence i recommend this asus .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n->i guess like anything else nothing will always be perfect , and so far this chromebook is everything i hoped for .\n[{'aspect': 'chromebook', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: great item !\n->great item !\n[{'aspect': 'item', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: there is plenty of room for positives , it looks sharp , runs great and is the perfect size for traveling and working on the go .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthere is plenty of room for positives , it looks sharp , runs great and is the perfect size for traveling and working on the go .\n->", + "output": "{\"text\": \"there is plenty of room for positives , it looks sharp , runs great and is the perfect size for traveling and working on the go .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'sharp', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'traveling', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !\n->i asked for an open faced cheese sandwich and the manager basically told me to take my business elsewhere !\n[{'aspect': 'manager', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: the screen is surprisingly poor .\n->the screen is surprisingly poor .\n[{'aspect': 'screen', 'opinion': 'poor', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\ntext: the negatives , the battery will need charging what feels like every 6ish hours depending on use and the speakers are insufficient .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe negatives , the battery will need charging what feels like every 6ish hours depending on use and the speakers are insufficient .\n->", + "output": "{\"text\": \"the negatives , the battery will need charging what feels like every 6ish hours depending on use and the speakers are insufficient .\", \"labels\": \"[{'aspect': 'battery', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'BATTERY#OPERATION_PERFORMANCE'}, {'aspect': 'speakers', 'opinion': 'insufficient', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: acer had no answer for that question .\n->acer had no answer for that question .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: this laptop is actually horrible .\n->this laptop is actually horrible .\n[{'aspect': 'laptop', 'opinion': 'horrible', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\ntext: i run around with it a lot , it doesn ' t just sit on a desk , and it ' s portability , ease of use , and flexibility in how i use it has been wonderful .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni run around with it a lot , it doesn ' t just sit on a desk , and it ' s portability , ease of use , and flexibility in how i use it has been wonderful .\n->", + "output": "{\"text\": \"i run around with it a lot , it doesn ' t just sit on a desk , and it ' s portability , ease of use , and flexibility in how i use it has been wonderful .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'ease', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}, {'aspect': 'NULL', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Be careful of portions - they 're HUGE .\n->Be careful of portions - they 're HUGE .\n[{'aspect': 'portions', 'opinion': 'HUGE', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: - the hard drive is really slow and really loud .\n->- the hard drive is really slow and really loud .\n[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}, {'aspect': 'hard drive', 'opinion': 'loud', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\ntext: this is the first laptop i ' ve had that i enjoy so much that i use it when i ' m not working too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is the first laptop i ' ve had that i enjoy so much that i use it when i ' m not working too .\n->", + "output": "{\"text\": \"this is the first laptop i ' ve had that i enjoy so much that i use it when i ' m not working too .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'enjoy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the retina screen does an excellent job of not tiring my eyes after a long day of computer work .\n->the retina screen does an excellent job of not tiring my eyes after a long day of computer work .\n[{'aspect': 'retina screen', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'DISPLAY#OPERATION_PERFORMANCE'}]\nExample:\ntext: microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n->microphone is not really working ( you have to basically yell while alsmost touching it with you lips in order for someone else to hear you ) .\n[{'aspect': 'microphone', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\ntext: typing is responsive , the touchescreen is a joy and it ' s fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntyping is responsive , the touchescreen is a joy and it ' s fast .\n->", + "output": "{\"text\": \"typing is responsive , the touchescreen is a joy and it ' s fast .\", \"labels\": \"[{'aspect': 'typing', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'touchescreen', 'opinion': 'joy', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n->sure , another generation back was arguably better , but i find this computer to be adequate to my needs right now ( though it is a disappointment that i can ' t upgrade the ram , which definitely limits the lifetime of how long this product will last ) .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: the atmosphere was great .\n->the atmosphere was great .\n[{'aspect': 'atmosphere', 'opinion': 'great', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}]\ntext: this is exactly what i needed .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis is exactly what i needed .\n->", + "output": "{\"text\": \"this is exactly what i needed .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hot dogs are top notch , and they ' re slamwich is amazing !\n->the hot dogs are top notch , and they ' re slamwich is amazing !\n[{'aspect': 'hot dogs', 'opinion': 'top notch', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'slamwich', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n->they seemed to continue to rush us along , taking plates without asking if we were done ( my sister still had her fork in hand ) .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\ntext: the keyboard has excellent travel and just feels right .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard has excellent travel and just feels right .\n->", + "output": "{\"text\": \"the keyboard has excellent travel and just feels right .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'excellent', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'keyboard', 'opinion': 'right', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the speed at which this charges and reboots is amazing , and the battery life is long .\n->the speed at which this charges and reboots is amazing , and the battery life is long .\n[{'aspect': 'charges', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'POWER_SUPPLY#OPERATION_PERFORMANCE'}, {'aspect': 'reboots', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'OS#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'long', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n->One would think we 'd get an apology or complimentary drinks - instead , we got a snobby waiter would n't even take our order for 15 minutes and gave us lip when we asked him to do so .\n[{'aspect': 'waiter', 'opinion': 'snobby', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the support of the play store in beta is also a nice addition like icing on a cake .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe support of the play store in beta is also a nice addition like icing on a cake .\n->", + "output": "{\"text\": \"the support of the play store in beta is also a nice addition like icing on a cake .\", \"labels\": \"[{'aspect': 'support of the play store', 'opinion': 'nice', 'polarity': 'positive', 'category': 'SOFTWARE#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n->i was disappointed with the touch pad would stick and when it broke loose it over shot the target .\n[{'aspect': 'touch pad', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\nExample:\ntext: The restaurant is cute but not upscale .\n->The restaurant is cute but not upscale .\n[{'aspect': 'restaurant', 'opinion': 'cute', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'restaurant', 'opinion': 'not upscale', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: i highly recommend this product to any one whose needs are simple and mostly web based .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni highly recommend this product to any one whose needs are simple and mostly web based .\n->", + "output": "{\"text\": \"i highly recommend this product to any one whose needs are simple and mostly web based .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'recommend', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'simple', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as soon as i turned on the computer , it froze as i tried to sync information .\n->as soon as i turned on the computer , it froze as i tried to sync information .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: unfortunately , before i purchased it , i failed to research what the thunderbolt ports were .\n->unfortunately , before i purchased it , i failed to research what the thunderbolt ports were .\n[{'aspect': 'thunderbolt ports', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#DESIGN_FEATURES'}]\ntext: screen is very close to retina display not exactly but close .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nscreen is very close to retina display not exactly but close .\n->", + "output": "{\"text\": \"screen is very close to retina display not exactly but close .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: + nice , large screen\n->+ nice , large screen\n[{'aspect': 'screen', 'opinion': 'nice', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'large', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\nExample:\ntext: An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n->An oasis of refinement : Food , though somewhat uneven , often reaches the pinnacles of new American fine cuisine - chef 's passion ( and kitchen 's precise execution ) is most evident in the fish dishes and soups .\n[{'aspect': 'chef', 'opinion': 'passion', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish dishes', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'soups', 'opinion': 'evident', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'kitchen', 'opinion': 'precise execution', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i have been using this notebook for a month and i absolutely love it !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have been using this notebook for a month and i absolutely love it !\n->", + "output": "{\"text\": \"i have been using this notebook for a month and i absolutely love it !\", \"labels\": \"[{'aspect': 'notebook', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: as for actual device it is completely gorgeous and ( now ) works flawlessly .\n->as for actual device it is completely gorgeous and ( now ) works flawlessly .\n[{'aspect': 'device', 'opinion': 'gorgeous', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'device', 'opinion': 'flawlessly', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: Try the green curry ! ! !\n->Try the green curry ! ! !\n[{'aspect': 'green curry', 'opinion': 'Try', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i use this for work , school , and to just watch videos or read books !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni use this for work , school , and to just watch videos or read books !\n->", + "output": "{\"text\": \"i use this for work , school , and to just watch videos or read books !\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: two replacements had the same problem all in 4 weeks .\n->two replacements had the same problem all in 4 weeks .\n[{'aspect': 'replacements', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: - clean and secure operating system that is very lean and gets the most out of the systems modest specs\n->- clean and secure operating system that is very lean and gets the most out of the systems modest specs\n[{'aspect': 'operating system', 'opinion': 'clean', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'operating system', 'opinion': 'secure', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'operating system', 'opinion': 'clean', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}, {'aspect': 'specs', 'opinion': 'modest', 'polarity': 'positive', 'category': 'OS#DESIGN_FEATURES'}]\ntext: android apps from google play also running well with latest chrome update .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nandroid apps from google play also running well with latest chrome update .\n->", + "output": "{\"text\": \"android apps from google play also running well with latest chrome update .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The menu may be small , but everything on it is delicious .\n->The menu may be small , but everything on it is delicious .\n[{'aspect': 'menu', 'opinion': 'small', 'polarity': 'negative', 'category': 'NULL'}]\nExample:\ntext: i really can ' t say enough about this awesome laptop .\n->i really can ' t say enough about this awesome laptop .\n[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: everything is very smooth and fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neverything is very smooth and fast .\n->", + "output": "{\"text\": \"everything is very smooth and fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i can carry both of them in a reasonably sized purse and not hurt my shoulder .\n->i can carry both of them in a reasonably sized purse and not hurt my shoulder .\n[{'aspect': 'NULL', 'opinion': 'reasonably', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\nExample:\ntext: and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n->and since i do a lot of photos for work , the google environment makes inserting photos into docs a breeze .\n[{'aspect': 'google environment', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'SOFTWARE#USABILITY'}]\ntext: i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\n->", + "output": "{\"text\": \"i ' m also using it to make apps so i installed ubuntu with android studio and intellij in it , the performance is still very acceptable and i ' m so satisfied i can carry it around as a tablet and i can sit down write code with it whenever i want , best of both worlds .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: keeps disconnecting from my wifi at work .\n->keeps disconnecting from my wifi at work .\n[{'aspect': 'wifi', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'PORTS#OPERATION_PERFORMANCE'}]\nExample:\ntext: the entree was bland and small , dessert was not inspired .\n->the entree was bland and small , dessert was not inspired .\n[{'aspect': 'entree', 'opinion': 'bland', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}, {'aspect': 'entree', 'opinion': 'small', 'polarity': 'negative', 'category': 'FOOD#STYLE_OPTIONS'}, {'aspect': 'dessert', 'opinion': 'not inspired', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\ntext: it ' s fast , the display looks like a macbook pro , as does the aluminum case .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit ' s fast , the display looks like a macbook pro , as does the aluminum case .\n->", + "output": "{\"text\": \"it ' s fast , the display looks like a macbook pro , as does the aluminum case .\", \"labels\": \"[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: beware that stains in the coating of the display have been detected in all of the macbook retina editions .\n->beware that stains in the coating of the display have been detected in all of the macbook retina editions .\n[{'aspect': 'display', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: of course , it is crowded but who cares .\n->of course , it is crowded but who cares .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'AMBIENCE#GENERAL'}]\ntext: however , my unit had several issues .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , my unit had several issues .\n->", + "output": "{\"text\": \"however , my unit had several issues .\", \"labels\": \"[{'aspect': 'unit', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the vivobook f510ua is a great laptop with fantastic specs .\n->the vivobook f510ua is a great laptop with fantastic specs .\n[{'aspect': 'vivobook f510ua', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'specs', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: great machine out of the box .\n->great machine out of the box .\n[{'aspect': 'machine', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: when i put it into tablet mode , everything is great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwhen i put it into tablet mode , everything is great .\n->", + "output": "{\"text\": \"when i put it into tablet mode , everything is great .\", \"labels\": \"[{'aspect': 'tablet mode', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: in the evening , this place attracted a well dressed , with it , ny crowd .\n->in the evening , this place attracted a well dressed , with it , ny crowd .\n[{'aspect': 'crowd', 'opinion': 'attracted', 'polarity': 'positive', 'category': 'RESTAURANT#MISCELLANEOUS'}]\nExample:\ntext: We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->We are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'NULL'}]\ntext: however , when i switch back to laptop mode , the keyboard and trackpad are completely disabled and require a reboot to work again .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nhowever , when i switch back to laptop mode , the keyboard and trackpad are completely disabled and require a reboot to work again .\n->", + "output": "{\"text\": \"however , when i switch back to laptop mode , the keyboard and trackpad are completely disabled and require a reboot to work again .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'disabled', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'trackpad', 'opinion': 'disabled', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n->the baked clams octopus we shared as appetizers were the best we ' ve ever had ! !\n[{'aspect': 'baked clams octopus', 'opinion': 'best', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: we are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n->we are very particular about sushi and were both please with every choice which included : ceviche mix ( special ) , crab dumplings , assorted sashimi , sushi and rolls , two types of sake , and the banana tempura .\n[{'aspect': 'sushi', 'opinion': 'particular', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'ceviche mix ( special )', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'crab dumplings', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'assorted sashimi', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'sushi', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'rolls', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'two types of sake', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'banana tempura', 'opinion': 'please', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\ntext: the volume and backlit - keyboard brightness controls also do not work properly .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe volume and backlit - keyboard brightness controls also do not work properly .\n->", + "output": "{\"text\": \"the volume and backlit - keyboard brightness controls also do not work properly .\", \"labels\": \"[{'aspect': 'volume', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}, {'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hard drive is definitely slow .\n->the hard drive is definitely slow .\n[{'aspect': 'hard drive', 'opinion': 'slow', 'polarity': 'negative', 'category': 'HARD_DISC#OPERATION_PERFORMANCE'}]\nExample:\ntext: The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i did not expect to have to return this product for an exchange the same day i got it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did not expect to have to return this product for an exchange the same day i got it .\n->", + "output": "{\"text\": \"i did not expect to have to return this product for an exchange the same day i got it .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just made the move from pc to macbook !\n->just made the move from pc to macbook !\n[{'aspect': 'macbook', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: up until this point , asus chromebooks have been my favorite .\n->up until this point , asus chromebooks have been my favorite .\n[{'aspect': 'asus chromebooks', 'opinion': 'favorite', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: i love this laptop because of its size , speed , battery life , and backlit keyboard .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love this laptop because of its size , speed , battery life , and backlit keyboard .\n->", + "output": "{\"text\": \"i love this laptop because of its size , speed , battery life , and backlit keyboard .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'size', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'speed', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'battery life', 'opinion': 'love', 'polarity': 'positive', 'category': 'BATTERY#GENERAL'}, {'aspect': 'backlit keyboard', 'opinion': 'love', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n->the hot dogs were juicy and tender inside and had plenty of crunch and snap on the outside .\n[{'aspect': 'hot dogs', 'opinion': 'juicy', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}, {'aspect': 'hot dogs', 'opinion': 'tender', 'polarity': 'positive', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it gets rave reviews both on the ' net and amazon .\n->it gets rave reviews both on the ' net and amazon .\n[{'aspect': 'NULL', 'opinion': 'rave', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: it also has enough power to multi - task .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit also has enough power to multi - task .\n->", + "output": "{\"text\": \"it also has enough power to multi - task .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n->the aluminum casing adds to the durable feel of the unit and gives it a nice attractive look as well .\n[{'aspect': 'aluminum casing', 'opinion': 'durable', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'unit', 'opinion': 'attractive', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\nExample:\ntext: i ' ve had to reset the computer multiple times .\n->i ' ve had to reset the computer multiple times .\n[{'aspect': 'computer', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\n->", + "output": "{\"text\": \"the keyboard makes it easy to type notes quickly and write papers , and the backlit keyboard is a major plus .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'easy', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'keyboard', 'opinion': 'quickly', 'polarity': 'positive', 'category': 'KEYBOARD#USABILITY'}, {'aspect': 'backlit keyboard', 'opinion': 'plus', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: nice screen , nice feel .\n->nice screen , nice feel .\n[{'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'screen', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}]\nExample:\ntext: it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n->it ' s great in many ways ; the build is solid , the screen is good ( and the resolution is insane ) , and the keyboard is decent , though only time will tell if it ' s as diet coke - resistant as the samsung ' s keys are .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'build', 'opinion': 'solid', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'screen', 'opinion': 'good', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'resolution', 'opinion': 'insane', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'decent', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\ntext: even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \neven though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\n->", + "output": "{\"text\": \"even though i was upset at first for receiving a lemon , the performance of the working unit far outweighs any cons .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'upset', 'polarity': 'negative', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'working unit', 'opinion': 'outweighs', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the perfect spot .\n->the perfect spot .\n[{'aspect': 'spot', 'opinion': 'perfect', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: the screen display is absolutely amazing and totally blows me away .\n->the screen display is absolutely amazing and totally blows me away .\n[{'aspect': 'screen display', 'opinion': 'amazing', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\ntext: i have to say , this is a very nice product .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni have to say , this is a very nice product .\n->", + "output": "{\"text\": \"i have to say , this is a very nice product .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: everything we had was good or ok . . . . but definitely nothing great .\n->everything we had was good or ok . . . . but definitely nothing great .\n[{'aspect': 'NULL', 'opinion': 'nothing great', 'polarity': 'neutral', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: it ' s pretty light , too , so it ' s easy to travel with .\n->it ' s pretty light , too , so it ' s easy to travel with .\n[{'aspect': 'NULL', 'opinion': 'light', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#PORTABILITY'}]\ntext: well designed , nice fit and finish , and the build quality seems exceptional .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nwell designed , nice fit and finish , and the build quality seems exceptional .\n->", + "output": "{\"text\": \"well designed , nice fit and finish , and the build quality seems exceptional .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'nice', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'build quality', 'opinion': 'exceptional', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n->this computer freeze , reboots , and crashes in a regular basis if you ' re trying to do multiple stuff at once .\n[{'aspect': 'computer', 'opinion': 'crashes', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: This was my frist time at Cafe St. Bart 's and I must say how delicous the food and the service was .\n->This was my frist time at Cafe St. Bart 's and I must say how delicous the food and the service was .\n[{'aspect': 'food', 'opinion': 'delicous', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'delicous', 'polarity': 'positive', 'category': 'NULL'}]\ntext: but now , i ' m totally satisfied with this chromebook !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbut now , i ' m totally satisfied with this chromebook !\n->", + "output": "{\"text\": \"but now , i ' m totally satisfied with this chromebook !\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'satisfied', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i plan to take it with me when working as i have gotten approximately 8 - 9 hrs of battery time usage so far .\n->i plan to take it with me when working as i have gotten approximately 8 - 9 hrs of battery time usage so far .\n[{'aspect': 'battery time usage', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\nExample:\ntext: From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n->From the incredible food , to the warm atmosphere , to the friendly service , this downtown neighborhood spot does n't miss a beat .\n[{'aspect': 'food', 'opinion': 'incredible', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'atmosphere', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->", + "output": "{\"text\": \"the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: but the service is horrid !\n->but the service is horrid !\n[{'aspect': 'service', 'opinion': 'horrid', 'polarity': 'negative', 'category': 'SERVICE#GENERAL'}]\nExample:\ntext: works great .\n->works great .\n[{'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: this asus worked right out of the box and was very responsive .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthis asus worked right out of the box and was very responsive .\n->", + "output": "{\"text\": \"this asus worked right out of the box and was very responsive .\", \"labels\": \"[{'aspect': 'asus', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: and even more so unpleasant because it was so costly for such an unpleasant experience .\n->and even more so unpleasant because it was so costly for such an unpleasant experience .\n[{'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'unpleasant', 'polarity': 'negative', 'category': 'RESTAURANT#GENERAL'}, {'aspect': 'NULL', 'opinion': 'costly', 'polarity': 'negative', 'category': 'RESTAURANT#PRICES'}]\nExample:\ntext: bad battery , speaker and touchpad\n->bad battery , speaker and touchpad\n[{'aspect': 'battery', 'opinion': 'bad', 'polarity': 'negative', 'category': 'BATTERY#GENERAL'}, {'aspect': 'speaker', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}, {'aspect': 'touchpad', 'opinion': 'bad', 'polarity': 'negative', 'category': 'HARDWARE#GENERAL'}]\ntext: i am very impressed with this computer .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni am very impressed with this computer .\n->", + "output": "{\"text\": \"i am very impressed with this computer .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'impressed', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n->purchased this mid november , because of the 8th gen i5 , and other good specs for the great price .\n[{'aspect': '8th gen i5', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'CPU#GENERAL'}, {'aspect': 'specs', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'NULL', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The food arrived 20 minutes after I called , cold and soggy .\n->The food arrived 20 minutes after I called , cold and soggy .\n[{'aspect': 'food', 'opinion': 'cold', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'soggy', 'polarity': 'negative', 'category': 'NULL'}]\ntext: very fast , terrific screen , smooth operation .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nvery fast , terrific screen , smooth operation .\n->", + "output": "{\"text\": \"very fast , terrific screen , smooth operation .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'terrific', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'operation', 'opinion': 'smooth', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i replug and restarted the laptop 3 times and it still does n ' t work .\n->i replug and restarted the laptop 3 times and it still does n ' t work .\n[{'aspect': 'laptop', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: this is one of the best purchases i have made in years .\n->this is one of the best purchases i have made in years .\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: the screen looks fantastic and movies look great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe screen looks fantastic and movies look great .\n->", + "output": "{\"text\": \"the screen looks fantastic and movies look great .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'fantastic', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Great atmoshere and worth every bit .\n->Great atmoshere and worth every bit .\n[{'aspect': 'atmoshere', 'opinion': 'Great', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n->The food was not very tasty , the portioins were tiny even for such a high quality restaurant .\n[{'aspect': 'food', 'opinion': 'not very tasty', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'portioins', 'opinion': 'tiny', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the back lit keyboard is one of the nicest keyboards i have ever typed on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe back lit keyboard is one of the nicest keyboards i have ever typed on .\n->", + "output": "{\"text\": \"the back lit keyboard is one of the nicest keyboards i have ever typed on .\", \"labels\": \"[{'aspect': 'back lit keyboard', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'keyboards', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n->i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n[{'aspect': 'asus customer service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\nExample:\ntext: i had this laptop for a little over a year and it worked well at first .\n->i had this laptop for a little over a year and it worked well at first .\n[{'aspect': 'laptop', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: most of my android apps have worked well ( i have had minor issues with a couple ) .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmost of my android apps have worked well ( i have had minor issues with a couple ) .\n->", + "output": "{\"text\": \"most of my android apps have worked well ( i have had minor issues with a couple ) .\", \"labels\": \"[{'aspect': 'android apps', 'opinion': 'well', 'polarity': 'positive', 'category': 'SOFTWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: quiet keyboard .\n->quiet keyboard .\n[{'aspect': 'keyboard', 'opinion': 'quiet', 'polarity': 'positive', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: samsung rma ' d the device to replace the screen .\n->samsung rma ' d the device to replace the screen .\n[{'aspect': 'samsung', 'opinion': 'NULL', 'polarity': 'neutral', 'category': 'SUPPORT#OPERATION_PERFORMANCE'}]\ntext: the computer itself looks great .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe computer itself looks great .\n->", + "output": "{\"text\": \"the computer itself looks great .\", \"labels\": \"[{'aspect': 'computer', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: superb value for money and powerful performance from this quad core computer .\n->superb value for money and powerful performance from this quad core computer .\n[{'aspect': 'quad core computer', 'opinion': 'powerful', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}, {'aspect': 'quad core computer', 'opinion': 'superb', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\nExample:\ntext: The crust is thin , the ingredients are fresh and the staff is friendly .\n->The crust is thin , the ingredients are fresh and the staff is friendly .\n[{'aspect': 'crust', 'opinion': 'thin', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'staff', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ingredients', 'opinion': 'fresh', 'polarity': 'positive', 'category': 'NULL'}]\ntext: the only complaint i have is relatively minor in that the screen is a little small for my taste .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe only complaint i have is relatively minor in that the screen is a little small for my taste .\n->", + "output": "{\"text\": \"the only complaint i have is relatively minor in that the screen is a little small for my taste .\", \"labels\": \"[{'aspect': 'screen', 'opinion': 'complaint', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}, {'aspect': 'screen', 'opinion': 'small', 'polarity': 'negative', 'category': 'DISPLAY#DESIGN_FEATURES'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: it is the nicest chrome computer i have ever owned .\n->it is the nicest chrome computer i have ever owned .\n[{'aspect': 'chrome computer', 'opinion': 'nicest', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\nExample:\ntext: Do n't expect to sit down inside though , there are only a few tables and they are always full .\n->Do n't expect to sit down inside though , there are only a few tables and they are always full .\n[{'aspect': 'tables', 'opinion': 'few', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'tables', 'opinion': 'full', 'polarity': 'negative', 'category': 'NULL'}]\ntext: my first chromebook , and so far ( about one month of use ) i like it .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nmy first chromebook , and so far ( about one month of use ) i like it .\n->", + "output": "{\"text\": \"my first chromebook , and so far ( about one month of use ) i like it .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'like', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i also really like the finish on the case .\n->i also really like the finish on the case .\n[{'aspect': 'case', 'opinion': 'like', 'polarity': 'positive', 'category': 'HARDWARE#DESIGN_FEATURES'}]\nExample:\ntext: The view is spectacular , and the food is great .\n->The view is spectacular , and the food is great .\n[{'aspect': 'view', 'opinion': 'spectacular', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'food', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}]\ntext: good size , responsive keyboard , great screen , easy to use .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ngood size , responsive keyboard , great screen , easy to use .\n->", + "output": "{\"text\": \"good size , responsive keyboard , great screen , easy to use .\", \"labels\": \"[{'aspect': 'size', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'keyboard', 'opinion': 'responsive', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}, {'aspect': 'screen', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}, {'aspect': 'NULL', 'opinion': 'easy', 'polarity': 'positive', 'category': 'LAPTOP#USABILITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n->i run windows and linux vms fairly regularly and while i could run them on a comparable windows system , adding windows systems to an already portly windows system has never worked out well for me , so while disappointed in the new upgrades , this 2015 model still rocks vms and does all i need it too for far less $ $ $ .\n[{'aspect': 'the new upgrades', 'opinion': 'disappointed', 'polarity': 'negative', 'category': 'SOFTWARE#USABILITY'}]\nExample:\ntext: Check out the secret back room .\n->Check out the secret back room .\n[{'aspect': 'back room', 'opinion': 'secret', 'polarity': 'positive', 'category': 'NULL'}]\ntext: track pad is a little spongy , but definitely not a showstopper .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ntrack pad is a little spongy , but definitely not a showstopper .\n->", + "output": "{\"text\": \"track pad is a little spongy , but definitely not a showstopper .\", \"labels\": \"[{'aspect': 'track pad', 'opinion': 'spongy', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Best of all is the warm vibe , the owner is super friendly and service is fast .\n->Best of all is the warm vibe , the owner is super friendly and service is fast .\n[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: this samsung works as expected and is a good , basic chromebook .\n->this samsung works as expected and is a good , basic chromebook .\n[{'aspect': 'samsung', 'opinion': 'good', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\ntext: so far i really love this product !\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nso far i really love this product !\n->", + "output": "{\"text\": \"so far i really love this product !\", \"labels\": \"[{'aspect': 'product', 'opinion': 'love', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n->Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really ca n't top the setting for a romantic dinner in NYC .\n[{'aspect': 'dinner', 'opinion': 'romantic', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: the space bar on the keyboard is inconsistent .\n->the space bar on the keyboard is inconsistent .\n[{'aspect': 'space bar', 'opinion': 'inconsistent', 'polarity': 'negative', 'category': 'KEYBOARD#QUALITY'}]\ntext: the trackpad works well and the screen display is great too .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe trackpad works well and the screen display is great too .\n->", + "output": "{\"text\": \"the trackpad works well and the screen display is great too .\", \"labels\": \"[{'aspect': 'trackpad', 'opinion': 'well', 'polarity': 'positive', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}, {'aspect': 'screen display', 'opinion': 'great', 'polarity': 'positive', 'category': 'DISPLAY#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: - power button next to delete button ?\n->- power button next to delete button ?\n[{'aspect': 'power button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}, {'aspect': 'delete button', 'opinion': 'NULL', 'polarity': 'negative', 'category': 'KEYBOARD#DESIGN_FEATURES'}]\nExample:\ntext: the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n->the look and feel of it are every bit as ` ` sleek ` ` as a mac , which my son and daughters have - - - - - - - but this is about 1 / 4 of the price .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#PRICE'}]\ntext: i love the screen quality and it is very fast for browsing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni love the screen quality and it is very fast for browsing .\n->", + "output": "{\"text\": \"i love the screen quality and it is very fast for browsing .\", \"labels\": \"[{'aspect': 'screen quality', 'opinion': 'love', 'polarity': 'positive', 'category': 'DISPLAY#QUALITY'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the hardware specs on this are very nice compared to other offerings : 4g ram , 32g storage , quad - core processor , etc .\n->the hardware specs on this are very nice compared to other offerings : 4g ram , 32g storage , quad - core processor , etc .\n[{'aspect': 'hardware specs', 'opinion': 'nice', 'polarity': 'positive', 'category': 'HARDWARE#GENERAL'}]\nExample:\ntext: also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n->also it ' s great to have dinner in a very romantic and comfortable place , the service it ' s just perfect . . . they ' re so frendly that we never want to live the place !\n[{'aspect': 'service', 'opinion': 'place', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'service', 'opinion': 'the', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'just', 'opinion': \"' re\", 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: it is so fast .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nit is so fast .\n->", + "output": "{\"text\": \"it is so fast .\", \"labels\": \"[{'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n->The atmosphere is noisy and the waiters are literally walking around doing things as fast as they can .\n[{'aspect': 'atmosphere', 'opinion': 'noisy', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'fast', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: hit the power button and plug it in , it will be ready before you are .\n->hit the power button and plug it in , it will be ready before you are .\n[{'aspect': 'NULL', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\ntext: i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\n->", + "output": "{\"text\": \"i did have to call asus customer service when i had a problem , and they were very helpful and solved my problem .\", \"labels\": \"[{'aspect': 'asus customer service', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'SUPPORT#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: the laptop is in great physical conditions , no scratches or anything , but certain actions run slowly , specifically any file read or write , like copying a document , uploading an image , creating a new file , etc .\n->the laptop is in great physical conditions , no scratches or anything , but certain actions run slowly , specifically any file read or write , like copying a document , uploading an image , creating a new file , etc .\n[{'aspect': 'laptop', 'opinion': 'great', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'laptop', 'opinion': 'slowly', 'polarity': 'negative', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: best of all is the warm vibe , the owner is super friendly and service is fast .\n->best of all is the warm vibe , the owner is super friendly and service is fast .\n[{'aspect': 'vibe', 'opinion': 'warm', 'polarity': 'positive', 'category': 'AMBIENCE#GENERAL'}, {'aspect': 'owner', 'opinion': 'friendly', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}, {'aspect': 'service', 'opinion': 'fast', 'polarity': 'positive', 'category': 'SERVICE#GENERAL'}]\ntext: as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nas a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\n->", + "output": "{\"text\": \"as a former google chrome ninja , i can tell you that this chromebook is the culmination of the intent and desire for this platform .\", \"labels\": \"[{'aspect': 'chromebook', 'opinion': 'NULL', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: when it works , it works well .\n->when it works , it works well .\n[{'aspect': 'NULL', 'opinion': 'well', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\nExample:\ntext: the battery will last a full day or two so it ' s very good for a middle school student .\n->the battery will last a full day or two so it ' s very good for a middle school student .\n[{'aspect': 'battery', 'opinion': 'good', 'polarity': 'positive', 'category': 'BATTERY#OPERATION_PERFORMANCE'}]\ntext: i really can ' t say enough about this awesome laptop .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni really can ' t say enough about this awesome laptop .\n->", + "output": "{\"text\": \"i really can ' t say enough about this awesome laptop .\", \"labels\": \"[{'aspect': 'laptop', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: next , is that the track pad is insanely wobbly .\n->next , is that the track pad is insanely wobbly .\n[{'aspect': 'track pad', 'opinion': 'wobbly', 'polarity': 'negative', 'category': 'HARDWARE#OPERATION_PERFORMANCE'}]\nExample:\ntext: everything from the power cord to the computer looks brand new .\n->everything from the power cord to the computer looks brand new .\n[{'aspect': 'power cord', 'opinion': 'new', 'polarity': 'positive', 'category': 'POWER_SUPPLY#DESIGN_FEATURES'}, {'aspect': 'computer', 'opinion': 'new', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}]\ntext: i think the sound could be better .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni think the sound could be better .\n->", + "output": "{\"text\": \"i think the sound could be better .\", \"labels\": \"[{'aspect': 'sound', 'opinion': 'could be better', 'polarity': 'neutral', 'category': 'MULTIMEDIA_DEVICES#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: super off balance with respect to screen .\n->super off balance with respect to screen .\n[{'aspect': 'screen', 'opinion': 'off balance', 'polarity': 'negative', 'category': 'DISPLAY#GENERAL'}]\nExample:\ntext: My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n->My husband had the mesclun , salmon , and ice cream and he enjoyed all 3 courses .\n[{'aspect': 'mesclun', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'salmon', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ice cream', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'courses', 'opinion': 'enjoyed', 'polarity': 'positive', 'category': 'NULL'}]\ntext: i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \ni didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\n->", + "output": "{\"text\": \"i didn ' t want to spend much on this as it is my first laptop , but i was convinced by a few reviews to upgrade to this model as it has more ram and performs better in several areas .\", \"labels\": \"[{'aspect': 'performs', 'opinion': 'better', 'polarity': 'positive', 'category': 'LAPTOP#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: just not good at all .\n->just not good at all .\n[{'aspect': 'NULL', 'opinion': 'not good', 'polarity': 'negative', 'category': 'FOOD#QUALITY'}]\nExample:\ntext: Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n->Have always found that the waiters will go out of their way to be helpful , despite the fact they are often busy with lots of diners .\n[{'aspect': 'waiters', 'opinion': 'helpful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'waiters', 'opinion': 'busy', 'polarity': 'positive', 'category': 'NULL'}]\ntext: an awesome product , well built - well worth your time and money .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nan awesome product , well built - well worth your time and money .\n->", + "output": "{\"text\": \"an awesome product , well built - well worth your time and money .\", \"labels\": \"[{'aspect': 'product', 'opinion': 'awesome', 'polarity': 'positive', 'category': 'LAPTOP#GENERAL'}, {'aspect': 'product', 'opinion': 'well built', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}, {'aspect': 'product', 'opinion': 'well worth', 'polarity': 'positive', 'category': 'LAPTOP#QUALITY'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: one of the best\n->one of the best\n[{'aspect': 'NULL', 'opinion': 'best', 'polarity': 'positive', 'category': 'RESTAURANT#GENERAL'}]\nExample:\ntext: The food is okay and the prices here are mediocre .\n->The food is okay and the prices here are mediocre .\n[{'aspect': 'food', 'opinion': 'okay', 'polarity': 'neutral', 'category': 'NULL'}, {'aspect': 'prices', 'opinion': 'mediocre', 'polarity': 'neutral', 'category': 'NULL'}]\ntext: backlit keyboard is great ; feels sturdy ; fast processing .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nbacklit keyboard is great ; feels sturdy ; fast processing .\n->", + "output": "{\"text\": \"backlit keyboard is great ; feels sturdy ; fast processing .\", \"labels\": \"[{'aspect': 'backlit keyboard', 'opinion': 'great', 'polarity': 'positive', 'category': 'KEYBOARD#GENERAL'}, {'aspect': 'NULL', 'opinion': 'sturdy', 'polarity': 'positive', 'category': 'LAPTOP#DESIGN_FEATURES'}, {'aspect': 'NULL', 'opinion': 'fast', 'polarity': 'positive', 'category': 'CPU#OPERATION_PERFORMANCE'}]\"}" + }, + { + "input": "", + "instruction": "\nFor sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. \nYour task is to predict the sentiment analysis output for each text, following the same output format as the provided examples.\nExample:\ntext: Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n->Very romantic fires - I 've literally spent hours at Lanterna , drinking wine from their extensive wine and enjoying the ambience .\n[{'aspect': 'wine', 'opinion': 'extensive', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'ambience', 'opinion': 'enjoying', 'polarity': 'positive', 'category': 'NULL'}]\nExample:\ntext: My chow fun and chow see was really bland and oily .\n->My chow fun and chow see was really bland and oily .\n[{'aspect': 'chow fun and chow see', 'opinion': 'bland', 'polarity': 'negative', 'category': 'NULL'}, {'aspect': 'chow fun and chow see', 'opinion': 'oily', 'polarity': 'negative', 'category': 'NULL'}]\ntext: the keyboard is nice to type on .\n\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \nthe keyboard is nice to type on .\n->", + "output": "{\"text\": \"the keyboard is nice to type on .\", \"labels\": \"[{'aspect': 'keyboard', 'opinion': 'nice', 'polarity': 'positive', 'category': 'KEYBOARD#OPERATION_PERFORMANCE'}]\"}" + } +] \ No newline at end of file diff --git a/pyabsa/__init__.py b/pyabsa/__init__.py index 1797de366..5f06d98f2 100644 --- a/pyabsa/__init__.py +++ b/pyabsa/__init__.py @@ -7,7 +7,7 @@ # Copyright (C) 2021. All Rights Reserved. __name__ = "pyabsa" -__version__ = "2.3.4" +__version__ = "2.4.1.post1" from pyabsa.utils.notification_utils.notification_utils import ( diff --git a/pyabsa/framework/flag_class/flag_template.py b/pyabsa/framework/flag_class/flag_template.py index 824ae241b..4b63da5b6 100644 --- a/pyabsa/framework/flag_class/flag_template.py +++ b/pyabsa/framework/flag_class/flag_template.py @@ -22,6 +22,10 @@ class TaskNameOption(dict): "tad": "Text Adversarial Defense", "rnac": "RNA Sequence Classification", "rnar": "RNA Sequence Regression", + "pr": "Protein Sequence Regression", + "cdd": "Code Defect Detection", + "acos": "Aspect Category Opinion Sentiment Triplet Extraction", + "universal_sentiment_analysis": "Universal Sentiment Analysis", "APC": "Aspect-based Sentiment Classification", "ATEPC": "Aspect Term Extraction and Polarity Classification", "ASTE": "Aspect Sentiment Triple Extraction", @@ -31,6 +35,8 @@ class TaskNameOption(dict): "RNAR": "RNA Sequence Regression", "PR": "Protein Sequence Regression", "CDD": "Code Defect Detection", + "ACOS": "Aspect Category Opinion Sentiment Triplet Extraction", + "USA": "Universal Sentiment Analysis", } def __init__(self): @@ -60,6 +66,8 @@ class TaskCodeOption: RNASequenceRegression = "RNAR" ProteinSequenceRegression = "PR" CodeDefectDetection = "CDD" + Aspect_Category_Opinion_Sentiment_Triplet_Extraction = "ACOS" + Universal_Sentiment_Analysis = "USA" class LabelPaddingOption: diff --git a/pyabsa/framework/instructor_class/instructor_template.py b/pyabsa/framework/instructor_class/instructor_template.py index 361d912f3..dae0a4b19 100644 --- a/pyabsa/framework/instructor_class/instructor_template.py +++ b/pyabsa/framework/instructor_class/instructor_template.py @@ -11,15 +11,14 @@ import os import pickle import random - import re from hashlib import sha256 import numpy +import pytorch_warmup as warmup import torch from findfile import find_file, find_files from termcolor import colored - from torch.utils.data import ( DataLoader, random_split, @@ -30,9 +29,6 @@ from transformers import BertModel from pyabsa.framework.flag_class.flag_template import DeviceTypeOption - -import pytorch_warmup as warmup - from pyabsa.framework.sampler_class.imblanced_sampler import ImbalancedDatasetSampler from pyabsa.utils.pyabsa_utils import print_args, fprint @@ -241,16 +237,6 @@ def _prepare_dataloader(self): pin_memory=True, ) - # Set up the testing dataloader - if self.test_set and not self.test_dataloader: - test_sampler = SequentialSampler(self.test_set) - self.test_dataloader = DataLoader( - dataset=self.test_set, - batch_size=self.config.batch_size, - sampler=test_sampler, - pin_memory=True, - ) - # Cross-validation else: split_dataset = self.train_set @@ -290,6 +276,16 @@ def _prepare_dataloader(self): ) ) + # Set up the testing dataloader + if self.test_set and not self.test_dataloader: + test_sampler = SequentialSampler(self.test_set) + self.test_dataloader = DataLoader( + dataset=self.test_set, + batch_size=self.config.batch_size, + sampler=test_sampler, + pin_memory=True, + ) + def _prepare_env(self): """ Prepares the environment for training, including setting the tokenizer and embedding matrix, @@ -455,7 +451,8 @@ def _resume_from_checkpoint(self): self.model.load_state_dict( torch.load( state_dict_path[0], map_location=self.config.device - ) + ), + strict=False, ) self.model.config = self.config self.model.to(self.config.device) diff --git a/pyabsa/framework/prediction_class/predictor_template.py b/pyabsa/framework/prediction_class/predictor_template.py index 5ff835f65..bcc9de599 100644 --- a/pyabsa/framework/prediction_class/predictor_template.py +++ b/pyabsa/framework/prediction_class/predictor_template.py @@ -9,10 +9,10 @@ import time from typing import Union -from pyabsa.utils.text_utils.mlm import get_mlm_and_tokenizer from torch import cuda -from pyabsa import TaskCodeOption, DeviceTypeOption +import pyabsa +from pyabsa.utils.text_utils.mlm import get_mlm_and_tokenizer class InferenceModel: @@ -57,10 +57,10 @@ def cpu(self): """ Sets the device to CPU for performing inference. """ - self.config.device = DeviceTypeOption.CPU - self.model.to(DeviceTypeOption.CPU) + self.config.device = pyabsa.DeviceTypeOption.CPU + self.model.to(pyabsa.DeviceTypeOption.CPU) if hasattr(self, "MLM"): - self.MLM.to(DeviceTypeOption.CPU) + self.MLM.to(pyabsa.DeviceTypeOption.CPU) def cuda(self, device="cuda:0"): """ diff --git a/pyabsa/framework/tokenizer_class/tokenizer_class.py b/pyabsa/framework/tokenizer_class/tokenizer_class.py index 9e929fa19..54aa527d8 100644 --- a/pyabsa/framework/tokenizer_class/tokenizer_class.py +++ b/pyabsa/framework/tokenizer_class/tokenizer_class.py @@ -183,7 +183,15 @@ def __init__(self, config, **kwargs): - None """ self.config = config - self.tokenizer = AutoTokenizer.from_pretrained(config.pretrained_bert, **kwargs) + try: + self.tokenizer = AutoTokenizer.from_pretrained( + config.pretrained_bert, trust_remote_code=True, **kwargs + ) + except: + # try to load use_fast=False + self.tokenizer = AutoTokenizer.from_pretrained( + config.pretrained_bert, use_fast=False, trust_remote_code=True, **kwargs + ) self.max_seq_len = self.config.max_seq_len self.pad_token_id = self.tokenizer.pad_token_id self.unk_token_id = self.tokenizer.unk_token_id @@ -394,19 +402,35 @@ def pad_and_truncate(sequence, max_seq_len, value, **kwargs): Returns: np.ndarray or list: The padded or truncated sequence, as a list or numpy array, depending on the type of the input sequence. """ - if isinstance(sequence, ndarray): - sequence = list(sequence) - if len(sequence) > max_seq_len: - sequence = sequence[:max_seq_len] + padding = kwargs.pop("padding", "right") + if padding == "right": + if isinstance(sequence, ndarray): + sequence = list(sequence) + if len(sequence) > max_seq_len: + sequence = sequence[:max_seq_len] + else: + sequence = sequence + [value] * (max_seq_len - len(sequence)) + return np.array(sequence) else: - sequence = sequence + [value] * (max_seq_len - len(sequence)) - return np.array(sequence) - else: - if len(sequence) > max_seq_len: - sequence = sequence[:max_seq_len] + if len(sequence) > max_seq_len: + sequence = sequence[:max_seq_len] + else: + sequence = sequence + [value] * (max_seq_len - len(sequence)) + return sequence + elif padding == "left": + if isinstance(sequence, ndarray): + sequence = list(sequence) + if len(sequence) > max_seq_len: + sequence = sequence[-max_seq_len:] + else: + sequence = [value] * (max_seq_len - len(sequence)) + sequence + return np.array(sequence) else: - sequence = sequence + [value] * (max_seq_len - len(sequence)) - return sequence + if len(sequence) > max_seq_len: + sequence = sequence[-max_seq_len:] + else: + sequence = [value] * (max_seq_len - len(sequence)) + sequence + return sequence def _load_word_vec(path, word2idx=None, embed_dim=300): diff --git a/pyabsa/framework/trainer_class/trainer_template.py b/pyabsa/framework/trainer_class/trainer_template.py index eed34b46c..6717ac260 100644 --- a/pyabsa/framework/trainer_class/trainer_template.py +++ b/pyabsa/framework/trainer_class/trainer_template.py @@ -9,6 +9,7 @@ # Copyright (C) 2021. All Rights Reserved. import os import time +import warnings from pathlib import Path from typing import Union @@ -19,21 +20,16 @@ from transformers import AutoConfig from pyabsa import __version__ as PyABSAVersion +from pyabsa.utils.logger.logger import get_logger +from pyabsa.utils.pyabsa_utils import set_device, fprint from ..configuration_class.config_verification import config_check +from ..configuration_class.configuration_template import ConfigManager from ..dataset_class.dataset_dict_class import DatasetDict - from ..flag_class.flag_template import DeviceTypeOption, ModelSaveOption -from ..configuration_class.configuration_template import ConfigManager - -from pyabsa.utils.logger.logger import get_logger - -from pyabsa.utils.pyabsa_utils import set_device, fprint from ...utils.check_utils import query_local_datasets_version from ...utils.data_utils.dataset_item import DatasetItem from ...utils.data_utils.dataset_manager import detect_dataset -import warnings - warnings.filterwarnings("once") @@ -59,10 +55,13 @@ def init_config(config): # if using a pretrained BERT model, set hidden_dim and embed_dim from the model's configuration if config.get("pretrained_bert", None): try: - pretrain_config = AutoConfig.from_pretrained(config.pretrained_bert) + pretrain_config = AutoConfig.from_pretrained( + config.pretrained_bert, trust_remote_code=True + ) config.hidden_dim = pretrain_config.hidden_size config.embed_dim = pretrain_config.hidden_size - except: + except Exception as e: + print(e) pass # if hidden_dim or embed_dim are not set, use default values of 768 elif not config.get("hidden_dim", None) or not config.get("embed_dim", None): diff --git a/pyabsa/tasks/ABSAInstruction/data_utils.py b/pyabsa/tasks/ABSAInstruction/data_utils.py index 300fe6226..8d2aa9406 100644 --- a/pyabsa/tasks/ABSAInstruction/data_utils.py +++ b/pyabsa/tasks/ABSAInstruction/data_utils.py @@ -22,12 +22,12 @@ class InstructDatasetLoader: def __init__( - self, - train_df_id, - test_df_id, - train_df_ood=None, - test_df_ood=None, - sample_size=1, + self, + train_df_id, + test_df_id, + train_df_ood=None, + test_df_ood=None, + sample_size=1, ): self.train_df_id = train_df_id.sample(frac=sample_size, random_state=1999) self.test_df_id = test_df_id diff --git a/pyabsa/tasks/ABSAInstruction/instruction.py b/pyabsa/tasks/ABSAInstruction/instruction.py index 394e97e92..f54b4ee70 100644 --- a/pyabsa/tasks/ABSAInstruction/instruction.py +++ b/pyabsa/tasks/ABSAInstruction/instruction.py @@ -85,10 +85,10 @@ def __init__(self, bos_instruction=None, eos_instruction=None): def prepare_input(self, input_text, aspects): return ( - self.bos_instruction - + input_text - + f"The aspects are: {aspects}" - + self.eos_instruction + self.bos_instruction + + input_text + + f"The aspects are: {aspects}" + + self.eos_instruction ) @@ -123,10 +123,10 @@ def __init__(self, bos_instruction=None, eos_instruction=None): def prepare_input(self, input_text, aspects): return ( - self.bos_instruction - + input_text - + f"The aspects are: {aspects}" - + self.eos_instruction + self.bos_instruction + + input_text + + f"The aspects are: {aspects}" + + self.eos_instruction ) @@ -161,8 +161,8 @@ def __init__(self, bos_instruction=None, eos_instruction=None): def prepare_input(self, input_text, aspects): return ( - self.bos_instruction - + input_text - + f"The aspects are: {aspects}" - + self.eos_instruction + self.bos_instruction + + input_text + + f"The aspects are: {aspects}" + + self.eos_instruction ) diff --git a/pyabsa/tasks/ABSAInstruction/model.py b/pyabsa/tasks/ABSAInstruction/model.py index d7eb627c8..95fe2fde8 100644 --- a/pyabsa/tasks/ABSAInstruction/model.py +++ b/pyabsa/tasks/ABSAInstruction/model.py @@ -1,11 +1,10 @@ import autocuda -import sklearn +import numpy as np import torch -from pyabsa.framework.checkpoint_class.checkpoint_template import CheckpointManager -from torch.utils.data import DataLoader +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import DataLoader from tqdm import tqdm -import numpy as np from transformers import ( DataCollatorForSeq2Seq, AutoTokenizer, @@ -15,7 +14,8 @@ Trainer, Seq2SeqTrainer, ) -from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score + +from pyabsa.framework.checkpoint_class.checkpoint_template import CheckpointManager from .instruction import ( ATEInstruction, APCInstruction, @@ -154,12 +154,12 @@ def predict(self, text, **kwargs): return ensemble_result def get_labels( - self, - tokenized_dataset, - trained_model_path=None, - predictor=None, - batch_size=4, - sample_set="train", + self, + tokenized_dataset, + trained_model_path=None, + predictor=None, + batch_size=4, + sample_set="train", ): """ Get the predictions from the trained model. @@ -315,7 +315,7 @@ def train(self, tokenized_datasets, **kwargs): return trainer def get_labels( - self, tokenized_dataset, predictor=None, batch_size=4, sample_set="train" + self, tokenized_dataset, predictor=None, batch_size=4, sample_set="train" ): """ Get the predictions from the trained model. diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/classic_glove_apc_utils.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/classic_glove_apc_utils.py index 0fb816396..024901901 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/classic_glove_apc_utils.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/classic_glove_apc_utils.py @@ -53,7 +53,7 @@ def syntax_distance_alignment(tokens, dist, max_seq_len, tokenizer): text = text[1:] dep_dist = dep_dist[1:] - bert_tokens = bert_tokens[len(tmp_tokens):] + bert_tokens = bert_tokens[len(tmp_tokens) :] else: text = text[1:] bert_tokens = bert_tokens[1:] @@ -109,7 +109,7 @@ def prepare_input_for_apc(config, tokenizer, text_left, text_right, aspect): text_raw = text_left + " " + aspect + " " + text_right text_spc = ( - bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token + bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token ) text_indices = text_to_sequence(tokenizer, text_spc, config.max_seq_len) text_raw_bert_indices = text_to_sequence( @@ -186,7 +186,7 @@ def get_syntax_distance(text_raw, aspect, tokenizer, config): def get_lca_ids_and_cdm_vec( - config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None + config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None ): SRD = config.SRD cdm_vec = np.zeros((config.max_seq_len), dtype=np.int64) @@ -206,7 +206,7 @@ def get_lca_ids_and_cdm_vec( def get_cdw_vec( - config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None + config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None ): SRD = config.SRD cdw_vec = np.zeros((config.max_seq_len), dtype=np.float32) @@ -246,15 +246,15 @@ def build_spc_mask_vec(config, text_ids): def build_sentiment_window( - examples, tokenizer, similarity_threshold, input_demands=None + examples, tokenizer, similarity_threshold, input_demands=None ): copy_side_aspect("left", examples[0], examples[0], examples, input_demands) for idx in range(1, len(examples)): if is_similar( - examples[idx - 1]["text_indices"], - examples[idx]["text_indices"], - tokenizer=None, - similarity_threshold=similarity_threshold, + examples[idx - 1]["text_indices"], + examples[idx]["text_indices"], + tokenizer=None, + similarity_threshold=similarity_threshold, ): copy_side_aspect( "right", examples[idx - 1], examples[idx], examples, input_demands diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/data_utils_for_inference.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/data_utils_for_inference.py index 83fee2d97..b37bcd1fa 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/data_utils_for_inference.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/data_utils_for_inference.py @@ -63,8 +63,8 @@ def process_data(self, samples, ignore_error=True): text = text_left + " " + aspect + " " + text_right if ( - validate_absa_example(text, aspect, polarity, self.config) - or not aspect + validate_absa_example(text, aspect, polarity, self.config) + or not aspect ): continue @@ -106,11 +106,11 @@ def process_data(self, samples, ignore_error=True): "constant", ) dependency_graph = dependency_graph[ - :, range(0, self.config.max_seq_len) - ] + :, range(0, self.config.max_seq_len) + ] dependency_graph = dependency_graph[ - range(0, self.config.max_seq_len), : - ] + range(0, self.config.max_seq_len), : + ] aspect_begin = np.count_nonzero( self.tokenizer.text_to_sequence(text_left) diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/dependency_graph.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/dependency_graph.py index 5631f7e77..862072b8a 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/dependency_graph.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__classic__/dependency_graph.py @@ -105,7 +105,7 @@ def prepare_dependency_graph(dataset_list, graph_path, max_seq_len, config): lines = fin.readlines() fin.close() for i in tqdm.tqdm( - range(0, len(lines), 3), desc="Construct graph for {}".format(filename) + range(0, len(lines), 3), desc="Construct graph for {}".format(filename) ): text_left, _, text_right = [ s.strip() for s in lines[i].partition("$T$") diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils.py index ef7de1a40..642eff541 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils.py @@ -51,7 +51,7 @@ def syntax_distance_alignment(tokens, dist, max_seq_len, tokenizer): text = text[1:] dep_dist = dep_dist[1:] - bert_tokens = bert_tokens[len(tmp_tokens):] + bert_tokens = bert_tokens[len(tmp_tokens) :] else: text = text[1:] bert_tokens = bert_tokens[1:] @@ -87,7 +87,7 @@ def pad_syntax_based_srd(text, dep_dist, tokenizer, config): def prepare_input_for_apc( - config, tokenizer, text_left, text_right, aspect, input_demands + config, tokenizer, text_left, text_right, aspect, input_demands ): if hasattr(config, "dynamic_truncate") and config.dynamic_truncate: reserved_num = 3 @@ -110,7 +110,7 @@ def prepare_input_for_apc( text_raw = text_left + " " + aspect + " " + text_right text_spc = ( - bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token + bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token ) text_indices = text_to_sequence(tokenizer, text_spc, config.max_seq_len) text_raw_bert_indices = text_to_sequence( @@ -223,7 +223,7 @@ def get_syntax_distance(text_raw, aspect, tokenizer, config): def get_lca_ids_and_cdm_vec( - config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None + config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None ): SRD = config.SRD cdm_vec = np.zeros((config.max_seq_len), dtype=np.int64) @@ -243,7 +243,7 @@ def get_lca_ids_and_cdm_vec( def get_cdw_vec( - config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None + config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None ): SRD = config.SRD cdw_vec = np.zeros((config.max_seq_len), dtype=np.float32) @@ -284,15 +284,15 @@ def build_spc_mask_vec(config, text_ids): def build_sentiment_window( - examples, tokenizer, similarity_threshold, input_demands=None + examples, tokenizer, similarity_threshold, input_demands=None ): copy_side_aspect("left", examples[0], examples[0], examples, input_demands) for idx in range(1, len(examples)): if is_similar( - examples[idx - 1]["text_indices"], - examples[idx]["text_indices"], - tokenizer=tokenizer, - similarity_threshold=similarity_threshold, + examples[idx - 1]["text_indices"], + examples[idx]["text_indices"], + tokenizer=tokenizer, + similarity_threshold=similarity_threshold, ): copy_side_aspect( "right", examples[idx - 1], examples[idx], examples, input_demands @@ -358,11 +358,11 @@ def is_similar(s1, s2, tokenizer, similarity_threshold): s1 = list(s1) s2 = list(s2) s1 = s1[ - : s1.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s1 else len(s1) - ] + : s1.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s1 else len(s1) + ] s2 = s2[ - : s2.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s2 else len(s2) - ] + : s2.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s2 else len(s2) + ] len1 = len(s1) len2 = len(s2) while s1 and s2: diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils_for_dlcf_dca.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils_for_dlcf_dca.py index 1326fad79..6fac6c050 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils_for_dlcf_dca.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/apc_utils_for_dlcf_dca.py @@ -34,12 +34,12 @@ def prepare_input_for_dlcf_dca(config, tokenizer, text_left, text_right, aspect) # test code text_left = " ".join( text_left.split(" ")[ - int(-(config.max_seq_len - len(aspect.split())) / 2) - 1: + int(-(config.max_seq_len - len(aspect.split())) / 2) - 1 : ] ) text_right = " ".join( text_right.split(" ")[ - : int((config.max_seq_len - len(aspect.split())) / 2) + 1 + : int((config.max_seq_len - len(aspect.split())) / 2) + 1 ] ) bos_token = tokenizer.bos_token if tokenizer.bos_token else "[CLS]" @@ -47,15 +47,15 @@ def prepare_input_for_dlcf_dca(config, tokenizer, text_left, text_right, aspect) text_raw = text_left + " " + aspect + " " + text_right text_spc = ( - bos_token - + " " - + text_raw - + " " - + eos_token - + " " - + aspect - + " " - + eos_token + bos_token + + " " + + text_raw + + " " + + eos_token + + " " + + aspect + + " " + + eos_token ) text_indices = text_to_sequence(tokenizer, text_spc, config.max_seq_len) aspect_bert_indices = text_to_sequence(tokenizer, aspect, config.max_seq_len) @@ -119,12 +119,12 @@ def prepare_input_for_dlcf_dca(config, tokenizer, text_left, text_right, aspect) def get_dynamic_cdw_vec( - config, - max_dist, - bert_spc_indices, - aspect_indices, - aspect_begin, - syntactical_dist=None, + config, + max_dist, + bert_spc_indices, + aspect_indices, + aspect_begin, + syntactical_dist=None, ): # the function is used to set dynamic threshold and calculate cdm/cdw for DLCF_DCA_BERT a = config.dlcf_a @@ -167,12 +167,12 @@ def get_dynamic_cdw_vec( def get_dynamic_cdm_vec( - config, - max_dist, - bert_spc_indices, - aspect_indices, - aspect_begin, - syntactical_dist=None, + config, + max_dist, + bert_spc_indices, + aspect_indices, + aspect_begin, + syntactical_dist=None, ): # the function is used to set dynamic threshold and calculate cdm/cdw for DLCF_DCA_BERT a = config.dlcf_a diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_inference.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_inference.py index 79332cc85..a4321b039 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_inference.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_inference.py @@ -180,8 +180,8 @@ def process_data(self, samples, ignore_error=True): lcfs_vec = prepared_inputs["lcfs_vec"] if ( - self.config.model_name == "dlcf_dca_bert" - or self.config.model_name == "dlcfs_dca_bert" + self.config.model_name == "dlcf_dca_bert" + or self.config.model_name == "dlcfs_dca_bert" ): configure_dlcf_spacy_model(self.config) prepared_inputs = prepare_input_for_dlcf_dca( diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_training.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_training.py index b806db1db..f01473790 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_training.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__lcf__/data_utils_for_training.py @@ -86,8 +86,8 @@ def load_data_from_file(self, file_path, **kwargs): # continue if ( - self.config.model_name == "dlcf_dca_bert" - or self.config.model_name == "dlcfs_dca_bert" + self.config.model_name == "dlcf_dca_bert" + or self.config.model_name == "dlcfs_dca_bert" ): configure_dlcf_spacy_model(self.config) prepared_inputs = prepare_input_for_dlcf_dca( diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/classic_bert_apc_utils.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/classic_bert_apc_utils.py index c997da3f5..85e175d2f 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/classic_bert_apc_utils.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/classic_bert_apc_utils.py @@ -51,7 +51,7 @@ def syntax_distance_alignment(tokens, dist, max_seq_len, tokenizer): text = text[1:] dep_dist = dep_dist[1:] - bert_tokens = bert_tokens[len(tmp_tokens):] + bert_tokens = bert_tokens[len(tmp_tokens) :] else: text = text[1:] bert_tokens = bert_tokens[1:] @@ -106,7 +106,7 @@ def prepare_input_for_apc(config, tokenizer, text_left, text_right, aspect): text_raw = text_left + " " + aspect + " " + text_right text_spc = ( - bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token + bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token ) text_indices = text_to_sequence(tokenizer, text_spc, config.max_seq_len) text_raw_bert_indices = text_to_sequence( @@ -183,7 +183,7 @@ def get_syntax_distance(text_raw, aspect, tokenizer, config): def get_lca_ids_and_cdm_vec( - config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None + config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None ): SRD = config.SRD cdm_vec = np.zeros((config.max_seq_len), dtype=np.int64) @@ -203,7 +203,7 @@ def get_lca_ids_and_cdm_vec( def get_cdw_vec( - config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None + config, bert_spc_indices, aspect_indices, aspect_begin, syntactical_dist=None ): SRD = config.SRD cdw_vec = np.zeros((config.max_seq_len), dtype=np.float32) @@ -243,15 +243,15 @@ def build_spc_mask_vec(config, text_ids): def build_sentiment_window( - examples, tokenizer, similarity_threshold, input_demands=None + examples, tokenizer, similarity_threshold, input_demands=None ): copy_side_aspect("left", examples[0], examples[0], examples, input_demands) for idx in range(1, len(examples)): if is_similar( - examples[idx - 1]["text_indices"], - examples[idx]["text_indices"], - tokenizer=tokenizer.tokenizer, - similarity_threshold=similarity_threshold, + examples[idx - 1]["text_indices"], + examples[idx]["text_indices"], + tokenizer=tokenizer.tokenizer, + similarity_threshold=similarity_threshold, ): copy_side_aspect( "right", examples[idx - 1], examples[idx], examples, input_demands @@ -317,11 +317,11 @@ def is_similar(s1, s2, tokenizer, similarity_threshold): s1 = list(s1) s2 = list(s2) s1 = s1[ - : s1.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s1 else len(s1) - ] + : s1.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s1 else len(s1) + ] s2 = s2[ - : s2.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s2 else len(s2) - ] + : s2.index(tokenizer.eos_token_id) if tokenizer.eos_token_id in s2 else len(s2) + ] len1 = len(s1) len2 = len(s2) while s1 and s2: diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/data_utils_for_inference.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/data_utils_for_inference.py index 1ab9a5ff2..d90d3035d 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/data_utils_for_inference.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/data_utils_for_inference.py @@ -58,8 +58,8 @@ def process_data(self, samples, ignore_error=True): # polarity = int(polarity) if ( - validate_absa_example(text, aspect, polarity, self.config) - or not aspect + validate_absa_example(text, aspect, polarity, self.config) + or not aspect ): continue @@ -113,11 +113,11 @@ def process_data(self, samples, ignore_error=True): ) dependency_graph = dependency_graph[ - :, range(0, self.config.max_seq_len) - ] + :, range(0, self.config.max_seq_len) + ] dependency_graph = dependency_graph[ - range(0, self.config.max_seq_len), : - ] + range(0, self.config.max_seq_len), : + ] data = { "ex_id": ex_id, diff --git a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/dependency_graph.py b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/dependency_graph.py index db2b4d037..bee18e05f 100644 --- a/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/dependency_graph.py +++ b/pyabsa/tasks/AspectPolarityClassification/dataset_utils/__plm__/dependency_graph.py @@ -96,7 +96,7 @@ def prepare_dependency_graph(dataset_list, graph_path, max_seq_len, config): lines = fin.readlines() fin.close() for i in tqdm.tqdm( - range(0, len(lines), 3), desc="Construct graph for {}".format(filename) + range(0, len(lines), 3), desc="Construct graph for {}".format(filename) ): text_left, _, text_right = [ s.strip() for s in lines[i].partition("$T$") diff --git a/pyabsa/tasks/AspectPolarityClassification/instructor/apc_instructor.py b/pyabsa/tasks/AspectPolarityClassification/instructor/apc_instructor.py index 0ae9eca72..dc7d49713 100644 --- a/pyabsa/tasks/AspectPolarityClassification/instructor/apc_instructor.py +++ b/pyabsa/tasks/AspectPolarityClassification/instructor/apc_instructor.py @@ -126,9 +126,9 @@ def _train_and_evaluate(self, criterion): targets = sample_batched["polarity"].to(self.config.device) if ( - isinstance(outputs, dict) - and "loss" in outputs - and outputs["loss"] != 0 + isinstance(outputs, dict) + and "loss" in outputs + and outputs["loss"] != 0 ): loss = outputs["loss"] else: @@ -191,8 +191,8 @@ def _train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_apc_test_acc"] + test_acc + > self.config.max_test_metrics["max_apc_test_acc"] ): self.config.max_test_metrics[ "max_apc_test_acc" @@ -343,7 +343,7 @@ def _k_fold_train_and_evaluate(self, criterion): self.config.max_test_metrics = {"max_apc_test_acc": 0, "max_apc_test_f1": 0} for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -403,9 +403,9 @@ def _k_fold_train_and_evaluate(self, criterion): targets = sample_batched["polarity"].to(self.config.device) if ( - isinstance(outputs, dict) - and "loss" in outputs - and outputs["loss"] != 0 + isinstance(outputs, dict) + and "loss" in outputs + and outputs["loss"] != 0 ): loss = outputs["loss"] else: @@ -447,7 +447,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -466,19 +466,19 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics[ - "max_apc_test_acc" - ] + test_acc + > self.config.max_test_metrics[ + "max_apc_test_acc" + ] ): self.config.max_test_metrics[ "max_apc_test_acc" ] = test_acc if ( - f1 - > self.config.max_test_metrics[ - "max_apc_test_f1" - ] + f1 + > self.config.max_test_metrics[ + "max_apc_test_f1" + ] ): self.config.max_test_metrics[ "max_apc_test_f1" @@ -499,8 +499,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) iterator.set_postfix_str(postfix) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, @@ -647,7 +647,8 @@ def _evaluate_acc_f1(self, test_dataloader): torch.argmax(t_outputs_all.cpu(), -1), target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) ) diff --git a/pyabsa/tasks/AspectPolarityClassification/instructor/ensembler.py b/pyabsa/tasks/AspectPolarityClassification/instructor/ensembler.py index 60b590c0f..a74bba51a 100644 --- a/pyabsa/tasks/AspectPolarityClassification/instructor/ensembler.py +++ b/pyabsa/tasks/AspectPolarityClassification/instructor/ensembler.py @@ -90,9 +90,9 @@ def __init__(self, config, load_dataset=True, **kwargs): ) if ( - load_dataset - and os.path.exists(cache_path) - and not self.config.overwrite_cache + load_dataset + and os.path.exists(cache_path) + and not self.config.overwrite_cache ): fprint(colored("Loading dataset cache: {}".format(cache_path), "green")) with open(cache_path, mode="rb") as f_cache: @@ -133,9 +133,9 @@ def __init__(self, config, load_dataset=True, **kwargs): exit(-1) if ( - load_dataset - and not os.path.exists(cache_path) - or self.config.overwrite_cache + load_dataset + and not os.path.exists(cache_path) + or self.config.overwrite_cache ): self.train_set = ( ABSADataset(self.config, self.tokenizer, dataset_type="train") @@ -167,9 +167,9 @@ def __init__(self, config, load_dataset=True, **kwargs): ) if ( - load_dataset - and not os.path.exists(cache_path) - or self.config.overwrite_cache + load_dataset + and not os.path.exists(cache_path) + or self.config.overwrite_cache ): self.train_set = ( BERTBaselineABSADataset( @@ -225,9 +225,9 @@ def __init__(self, config, load_dataset=True, **kwargs): ) if ( - load_dataset - and not os.path.exists(cache_path) - or self.config.overwrite_cache + load_dataset + and not os.path.exists(cache_path) + or self.config.overwrite_cache ): self.train_set = ( GloVeABSADataset( @@ -262,9 +262,9 @@ def __init__(self, config, load_dataset=True, **kwargs): self.config.embedding_matrix = self.embedding_matrix if ( - self.config.cache_dataset - and not os.path.exists(cache_path) - and not self.config.overwrite_cache + self.config.cache_dataset + and not os.path.exists(cache_path) + and not self.config.overwrite_cache ): fprint( colored( diff --git a/pyabsa/tasks/AspectPolarityClassification/models/__classic__/cabasc.py b/pyabsa/tasks/AspectPolarityClassification/models/__classic__/cabasc.py index 29156d956..1f7efec41 100644 --- a/pyabsa/tasks/AspectPolarityClassification/models/__classic__/cabasc.py +++ b/pyabsa/tasks/AspectPolarityClassification/models/__classic__/cabasc.py @@ -74,8 +74,8 @@ def context_attention(self, x_l, x_r, memory, memory_len, aspect_len): memory[i][idx] *= attn_l[i][idx] elif idx < aspect_end: memory[i][idx] *= ( - attn_l[i][idx] + attn_r[i][idx - aspect_start] - ) / 2 + attn_l[i][idx] + attn_r[i][idx - aspect_start] + ) / 2 else: memory[i][idx] *= attn_r[i][idx - aspect_start] diff --git a/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcf_dca_bert.py b/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcf_dca_bert.py index abb9e9f7e..7d4de0ecc 100644 --- a/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcf_dca_bert.py +++ b/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcf_dca_bert.py @@ -13,15 +13,15 @@ def weight_distrubute_local( - bert_local_out, depend_weight, depended_weight, depend_vec, depended_vec, config + bert_local_out, depend_weight, depended_weight, depend_vec, depended_vec, config ): bert_local_out2 = torch.zeros_like(bert_local_out) depend_vec2 = torch.mul(depend_vec, depend_weight.unsqueeze(2)) depended_vec2 = torch.mul(depended_vec, depended_weight.unsqueeze(2)) bert_local_out2 = ( - bert_local_out2 - + torch.mul(bert_local_out, depend_vec2) - + torch.mul(bert_local_out, depended_vec2) + bert_local_out2 + + torch.mul(bert_local_out, depend_vec2) + + torch.mul(bert_local_out, depended_vec2) ) for j in range(depend_weight.size()[0]): bert_local_out2[j][0] = bert_local_out[j][0] @@ -113,13 +113,13 @@ def weight_calculate(self, sa, pool, lin, d_w, ded_w, depend_out, depended_out): weight_sum = depend_weight[i].item() + depended_weight[i].item() if weight_sum != 0: depend_weight[i] = ( - 2 * depend_weight[i] / weight_sum - ) ** self.config.dca_p + 2 * depend_weight[i] / weight_sum + ) ** self.config.dca_p if depend_weight[i] > 2: depend_weight[i] = 2 depended_weight[i] = ( - 2 * depended_weight[i] / weight_sum - ) ** self.config.dca_p + 2 * depended_weight[i] / weight_sum + ) ** self.config.dca_p if depended_weight[i] > 2: depended_weight[i] = 2 else: diff --git a/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcfs_dca_bert.py b/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcfs_dca_bert.py index 337651523..258751da6 100644 --- a/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcfs_dca_bert.py +++ b/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/dlcfs_dca_bert.py @@ -13,15 +13,15 @@ def weight_distrubute_local( - bert_local_out, depend_weight, depended_weight, depend_vec, depended_vec, config + bert_local_out, depend_weight, depended_weight, depend_vec, depended_vec, config ): bert_local_out2 = torch.zeros_like(bert_local_out) depend_vec2 = torch.mul(depend_vec, depend_weight.unsqueeze(2)) depended_vec2 = torch.mul(depended_vec, depended_weight.unsqueeze(2)) bert_local_out2 = ( - bert_local_out2 - + torch.mul(bert_local_out, depend_vec2) - + torch.mul(bert_local_out, depended_vec2) + bert_local_out2 + + torch.mul(bert_local_out, depend_vec2) + + torch.mul(bert_local_out, depended_vec2) ) for j in range(depend_weight.size()[0]): bert_local_out2[j][0] = bert_local_out[j][0] @@ -113,13 +113,13 @@ def weight_calculate(self, sa, pool, lin, d_w, ded_w, depend_out, depended_out): weight_sum = depend_weight[i].item() + depended_weight[i].item() if weight_sum != 0: depend_weight[i] = ( - 2 * depend_weight[i] / weight_sum - ) ** self.config.dca_p + 2 * depend_weight[i] / weight_sum + ) ** self.config.dca_p if depend_weight[i] > 2: depend_weight[i] = 2 depended_weight[i] = ( - 2 * depended_weight[i] / weight_sum - ) ** self.config.dca_p + 2 * depended_weight[i] / weight_sum + ) ** self.config.dca_p if depended_weight[i] > 2: depended_weight[i] = 2 else: diff --git a/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/lca_bert.py b/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/lca_bert.py index 870e2bf5e..547c8430b 100644 --- a/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/lca_bert.py +++ b/pyabsa/tasks/AspectPolarityClassification/models/__lcf__/lca_bert.py @@ -71,7 +71,7 @@ def forward(self, inputs): "logits": sent_logits, "hidden_state": pooled_out, "loss": (1 - self.config.sigma) * sent_loss - + self.config.sigma * lcp_loss, + + self.config.sigma * lcp_loss, } else: return {"logits": sent_logits, "hidden_state": pooled_out} diff --git a/pyabsa/tasks/AspectPolarityClassification/models/__plm__/cabasc_bert.py b/pyabsa/tasks/AspectPolarityClassification/models/__plm__/cabasc_bert.py index 4df3b25c0..452cc305b 100644 --- a/pyabsa/tasks/AspectPolarityClassification/models/__plm__/cabasc_bert.py +++ b/pyabsa/tasks/AspectPolarityClassification/models/__plm__/cabasc_bert.py @@ -75,8 +75,8 @@ def context_attention(self, x_l, x_r, memory, memory_len, aspect_len): memory[i][idx] *= attn_l[i][idx] elif idx < aspect_end: memory[i][idx] *= ( - attn_l[i][idx] + attn_r[i][idx - aspect_start] - ) / 2 + attn_l[i][idx] + attn_r[i][idx - aspect_start] + ) / 2 else: memory[i][idx] *= attn_r[i][idx - aspect_start] diff --git a/pyabsa/tasks/AspectPolarityClassification/prediction/sentiment_classifier.py b/pyabsa/tasks/AspectPolarityClassification/prediction/sentiment_classifier.py index f1d5fa7e6..9214adc21 100644 --- a/pyabsa/tasks/AspectPolarityClassification/prediction/sentiment_classifier.py +++ b/pyabsa/tasks/AspectPolarityClassification/prediction/sentiment_classifier.py @@ -134,12 +134,12 @@ def __init__(self, checkpoint=None, **kwargs): self.__post_init__(**kwargs) def batch_infer( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ A deprecated version of batch_predict method. @@ -178,12 +178,12 @@ def infer(self, text: str = None, print_result=True, ignore_error=True, **kwargs ) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict the sentiment from a file of sentences. @@ -220,11 +220,11 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict the sentiment from a sentence or a list of sentences. @@ -253,7 +253,7 @@ def merge_results(self, results): for result in results: # Check if the final_res list is not empty and if the previous result has the same input text as the current result if final_res and "".join(final_res[-1]["text"].split()) == "".join( - result["text"].split() + result["text"].split() ): # If the input texts match, append the aspect, sentiment, confidence, probabilities, reference sentiment, # reference check, and perplexity to the corresponding lists in the previous result @@ -398,8 +398,8 @@ def _run_prediction(self, save_path=None, print_result=True, **kwargs): text_printing = result["text"] for i in range(len(result["aspect"])): if ( - result["ref_sentiment"][i] - != LabelPaddingOption.SENTIMENT_PADDING + result["ref_sentiment"][i] + != LabelPaddingOption.SENTIMENT_PADDING ): if result["sentiment"][i] == result["ref_sentiment"][i]: aspect_info = colored( @@ -460,7 +460,8 @@ def _run_prediction(self, save_path=None, print_result=True, **kwargs): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( diff --git a/pyabsa/tasks/AspectPolarityClassification/trainer/apc_trainer.py b/pyabsa/tasks/AspectPolarityClassification/trainer/apc_trainer.py index c7d7aaf5f..c52407ca2 100644 --- a/pyabsa/tasks/AspectPolarityClassification/trainer/apc_trainer.py +++ b/pyabsa/tasks/AspectPolarityClassification/trainer/apc_trainer.py @@ -23,14 +23,14 @@ class APCTrainer(Trainer): def __init__( - self, - config: APCConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: APCConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/AspectSentimentTripletExtraction/__init__.py b/pyabsa/tasks/AspectSentimentTripletExtraction/__init__.py index eeee9238b..6b51dd54b 100644 --- a/pyabsa/tasks/AspectSentimentTripletExtraction/__init__.py +++ b/pyabsa/tasks/AspectSentimentTripletExtraction/__init__.py @@ -7,9 +7,10 @@ # ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research # Copyright (C) 2022. All Rights Reserved. -# for Aspect-sentiment-triplet-extraction -from .trainer.trainer import ASTETrainer from .configuration.configuration import ASTEConfigManager -from .models import ASTEModelList from .dataset_utils.dataset_list import ASTEDatasetList +from .models import ASTEModelList from .prediction.predictor import AspectSentimentTripletExtractor + +# for Aspect-sentiment-triplet-extraction +from .trainer.trainer import ASTETrainer diff --git a/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/aste_utils.py b/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/aste_utils.py index 84e68519d..964025d44 100644 --- a/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/aste_utils.py +++ b/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/aste_utils.py @@ -68,14 +68,14 @@ def get_evaluate_spans(tags, length, token_range): class Instance(object): def __init__( - self, - tokenizer, - sentence_pack, - post_vocab, - deprel_vocab, - postag_vocab, - synpost_vocab, - config, + self, + tokenizer, + sentence_pack, + post_vocab, + deprel_vocab, + postag_vocab, + synpost_vocab, + config, ): self.id = sentence_pack["id"] self.sentence = sentence_pack["sentence"] @@ -119,11 +119,11 @@ def __init__( token_start = token_end assert self.length == self.token_range[-1][-1] + 2, "length error" - self.aspect_tags[self.length:] = -1 + self.aspect_tags[self.length :] = -1 self.aspect_tags[0] = -1 self.aspect_tags[self.length - 1] = -1 - self.opinion_tags[self.length:] = -1 + self.opinion_tags[self.length :] = -1 self.opinion_tags[0] = -1 self.opinion_tags[self.length - 1] = -1 @@ -156,10 +156,10 @@ def __init__( set_tag = 1 if i == l else 2 al, ar = self.token_range[i] self.aspect_tags[al] = set_tag - self.aspect_tags[al + 1: ar + 1] = -1 + self.aspect_tags[al + 1 : ar + 1] = -1 """mask positions of sub words""" - self.tags[al + 1: ar + 1, :] = -1 - self.tags[:, al + 1: ar + 1] = -1 + self.tags[al + 1 : ar + 1, :] = -1 + self.tags[:, al + 1 : ar + 1] = -1 """set tag for opinion""" for l, r in opinion_span: @@ -178,9 +178,9 @@ def __init__( set_tag = 1 if i == l else 2 pl, pr = self.token_range[i] self.opinion_tags[pl] = set_tag - self.opinion_tags[pl + 1: pr + 1] = -1 - self.tags[pl + 1: pr + 1, :] = -1 - self.tags[:, pl + 1: pr + 1] = -1 + self.opinion_tags[pl + 1 : pr + 1] = -1 + self.tags[pl + 1 : pr + 1, :] = -1 + self.tags[:, pl + 1 : pr + 1] = -1 for al, ar in aspect_span: for pl, pr in opinion_span: @@ -188,7 +188,7 @@ def __init__( for j in range(pl, pr + 1): sal, sar = self.token_range[i] spl, spr = self.token_range[j] - self.tags[sal: sar + 1, spl: spr + 1] = -1 + self.tags[sal : sar + 1, spl : spr + 1] = -1 if config.task == "pair": if i > j: self.tags[spl][sal] = 7 @@ -315,7 +315,7 @@ def get_data(self): def load_data_instances( - sentence_packs, post_vocab, deprel_vocab, postag_vocab, synpost_vocab, config + sentence_packs, post_vocab, deprel_vocab, postag_vocab, synpost_vocab, config ): instances = list() tokenizer = BertTokenizer.from_pretrained(config.pretrained_bert) @@ -368,13 +368,13 @@ def load_tokens(filename): class Metric: def __init__( - self, - config, - predictions, - goldens, - bert_lengths, - sen_lengths, - tokens_ranges, + self, + config, + predictions, + goldens, + bert_lengths, + sen_lengths, + tokens_ranges, ): self.config = config self.predictions = predictions @@ -829,8 +829,8 @@ def get_batch(self, index): word_pair_synpost = [] for i in range( - index * self.config.batch_size, - min((index + 1) * self.config.batch_size, len(self.instances)), + index * self.config.batch_size, + min((index + 1) * self.config.batch_size, len(self.instances)), ): sentence_ids.append(self.instances[i].id) sentences.append(self.instances[i].sentence) diff --git a/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/data_utils_for_training.py b/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/data_utils_for_training.py index 4b54c60c6..9aabd8fab 100644 --- a/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/data_utils_for_training.py +++ b/pyabsa/tasks/AspectSentimentTripletExtraction/dataset_utils/data_utils_for_training.py @@ -271,11 +271,11 @@ def get_dependencies(self, tokens): def get_vocabs(self): if ( - self.config.get("syn_post_vocab") is None - and self.config.get("postag_vocab") is None - and self.config.get("deprel_vocab") is None - and self.config.get("syn_post_vocab") is None - and self.config.get("token_vocab") is None + self.config.get("syn_post_vocab") is None + and self.config.get("postag_vocab") is None + and self.config.get("deprel_vocab") is None + and self.config.get("syn_post_vocab") is None + and self.config.get("token_vocab") is None ): token_counter = Counter(self.all_tokens) deprel_counter = Counter(self.all_deprel) diff --git a/pyabsa/tasks/AspectSentimentTripletExtraction/instructor/instructor.py b/pyabsa/tasks/AspectSentimentTripletExtraction/instructor/instructor.py index 12dcc0678..9a95450b4 100644 --- a/pyabsa/tasks/AspectSentimentTripletExtraction/instructor/instructor.py +++ b/pyabsa/tasks/AspectSentimentTripletExtraction/instructor/instructor.py @@ -388,8 +388,8 @@ def _train_and_evaluate(self, criterion): ) if ( - joint_f1 - > self.config.max_test_metrics["max_apc_test_f1"] + joint_f1 + > self.config.max_test_metrics["max_apc_test_f1"] ): self.config.max_test_metrics[ "max_apc_test_f1" @@ -488,7 +488,7 @@ def _k_fold_train_and_evaluate(self, criterion): self.config.max_test_metrics = {"max_apc_test_acc": 0, "max_apc_test_f1": 0} for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -548,9 +548,9 @@ def _k_fold_train_and_evaluate(self, criterion): targets = sample_batched["polarity"].to(self.config.device) if ( - isinstance(outputs, dict) - and "loss" in outputs - and outputs["loss"] != 0 + isinstance(outputs, dict) + and "loss" in outputs + and outputs["loss"] != 0 ): loss = outputs["loss"] else: @@ -592,7 +592,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -611,19 +611,19 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics[ - "max_apc_test_acc" - ] + test_acc + > self.config.max_test_metrics[ + "max_apc_test_acc" + ] ): self.config.max_test_metrics[ "max_apc_test_acc" ] = test_acc if ( - f1 - > self.config.max_test_metrics[ - "max_apc_test_f1" - ] + f1 + > self.config.max_test_metrics[ + "max_apc_test_f1" + ] ): self.config.max_test_metrics[ "max_apc_test_f1" @@ -644,8 +644,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) iterator.set_postfix_str(postfix) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, @@ -820,7 +820,7 @@ def _init_misc(self): p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay) - and any(nd in n for nd in diff_part) + and any(nd in n for nd in diff_part) ], "weight_decay": self.config.l2reg, "lr": self.config.learning_rate, @@ -830,7 +830,7 @@ def _init_misc(self): p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay) - and any(nd in n for nd in diff_part) + and any(nd in n for nd in diff_part) ], "weight_decay": 0.0, "lr": self.config.learning_rate, @@ -840,7 +840,7 @@ def _init_misc(self): p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay) - and not any(nd in n for nd in diff_part) + and not any(nd in n for nd in diff_part) ], "weight_decay": self.config.l2reg, "lr": 1e-3, @@ -850,7 +850,7 @@ def _init_misc(self): p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay) - and not any(nd in n for nd in diff_part) + and not any(nd in n for nd in diff_part) ], "weight_decay": 0.0, "lr": 1e-3, diff --git a/pyabsa/tasks/AspectSentimentTripletExtraction/models/model.py b/pyabsa/tasks/AspectSentimentTripletExtraction/models/model.py index ee1bfa6bc..dc2431dad 100644 --- a/pyabsa/tasks/AspectSentimentTripletExtraction/models/model.py +++ b/pyabsa/tasks/AspectSentimentTripletExtraction/models/model.py @@ -99,7 +99,7 @@ def forward(self, weight_prob_softmax, weight_adj, gcn_inputs, self_loop): class Biaffine(nn.Module): def __init__( - self, config, in1_features, in2_features, out_features, bias=(True, True) + self, config, in1_features, in2_features, out_features, bias=(True, True) ): super(Biaffine, self).__init__() self.config = config @@ -239,16 +239,16 @@ def forward(self, inputs): # Apply softmax to weight probabilities and mask padded tokens biaffine_edge_softmax = F.softmax(biaffine_edge, dim=-1) * tensor_masks word_pair_post_emb_softmax = ( - F.softmax(word_pair_post_emb, dim=-1) * tensor_masks + F.softmax(word_pair_post_emb, dim=-1) * tensor_masks ) word_pair_deprel_emb_softmax = ( - F.softmax(word_pair_deprel_emb, dim=-1) * tensor_masks + F.softmax(word_pair_deprel_emb, dim=-1) * tensor_masks ) word_pair_postag_emb_softmax = ( - F.softmax(word_pair_postag_emb, dim=-1) * tensor_masks + F.softmax(word_pair_postag_emb, dim=-1) * tensor_masks ) word_pair_synpost_emb_softmax = ( - F.softmax(word_pair_synpost_emb, dim=-1) * tensor_masks + F.softmax(word_pair_synpost_emb, dim=-1) * tensor_masks ) # Create identity matrix for self-loop connections @@ -256,11 +256,11 @@ def forward(self, inputs): for _ in range(batch): self_loop.append(torch.eye(seq)) self_loop = ( - torch.stack(self_loop) - .to(self.config.device) - .unsqueeze(1) - .expand(batch, 5 * self.config.output_dim, seq, seq) - * tensor_masks.permute(0, 3, 1, 2).contiguous() + torch.stack(self_loop) + .to(self.config.device) + .unsqueeze(1) + .expand(batch, 5 * self.config.output_dim, seq, seq) + * tensor_masks.permute(0, 3, 1, 2).contiguous() ) # Concatenate weight probabilities diff --git a/pyabsa/tasks/AspectSentimentTripletExtraction/prediction/predictor.py b/pyabsa/tasks/AspectSentimentTripletExtraction/prediction/predictor.py index f17ab13d5..168453a96 100644 --- a/pyabsa/tasks/AspectSentimentTripletExtraction/prediction/predictor.py +++ b/pyabsa/tasks/AspectSentimentTripletExtraction/prediction/predictor.py @@ -110,12 +110,12 @@ def __init__(self, checkpoint=None, **kwargs): self.__post_init__(**kwargs) def batch_infer( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ A deprecated version of batch_predict method. @@ -154,12 +154,12 @@ def infer(self, text: str = None, print_result=True, ignore_error=True, **kwargs ) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict the sentiment from a file of sentences. @@ -191,11 +191,11 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict the sentiment from a sentence or a list of sentences. @@ -287,10 +287,10 @@ def _run_prediction(self, save_path=None, print_result=True, **kwargs): asp_head, asp_tail, opn_head, opn_tail, polarity = triplet triplet = { "Aspect": " ".join( - sentences[j].split()[asp_head: asp_tail + 1] + sentences[j].split()[asp_head : asp_tail + 1] ), "Opinion": " ".join( - sentences[j].split()[opn_head: opn_tail + 1] + sentences[j].split()[opn_head : opn_tail + 1] ), "Polarity": self.config.index_to_label[polarity], } @@ -310,10 +310,10 @@ def _run_prediction(self, save_path=None, print_result=True, **kwargs): asp_head, asp_tail, opn_head, opn_tail, polarity = triplet triplet = { "Aspect": " ".join( - sentences[j].split()[asp_head: asp_tail + 1] + sentences[j].split()[asp_head : asp_tail + 1] ), "Opinion": " ".join( - sentences[j].split()[opn_head: opn_tail + 1] + sentences[j].split()[opn_head : opn_tail + 1] ), "Polarity": self.config.index_to_label[polarity], } diff --git a/pyabsa/tasks/AspectSentimentTripletExtraction/trainer/trainer.py b/pyabsa/tasks/AspectSentimentTripletExtraction/trainer/trainer.py index e1b62510f..c6c13c262 100644 --- a/pyabsa/tasks/AspectSentimentTripletExtraction/trainer/trainer.py +++ b/pyabsa/tasks/AspectSentimentTripletExtraction/trainer/trainer.py @@ -1,5 +1,5 @@ # -*- coding: utf-8 -*- -# file: apc_trainer.py +# file: aste_trainer.py # time: 02/11/2022 21:34 # author: YANG, HENG (杨恒) # github: https://github.com/yangheng95 @@ -17,20 +17,20 @@ ) from pyabsa.framework.trainer_class.trainer_template import Trainer from ..configuration.configuration import ASTEConfigManager -from ..prediction.predictor import AspectSentimentTripletExtractor from ..instructor.instructor import ASTETrainingInstructor +from ..prediction.predictor import AspectSentimentTripletExtractor class ASTETrainer(Trainer): def __init__( - self, - config: ASTEConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: ASTEConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/atepc_utils.py b/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/atepc_utils.py index 2990e926b..dad23bab7 100644 --- a/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/atepc_utils.py +++ b/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/atepc_utils.py @@ -94,7 +94,7 @@ def prepare_input_for_atepc(config, tokenizer, text_left, text_right, aspect): text_raw = text_left + " " + aspect + " " + text_right text_spc = ( - bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token + bos_token + " " + text_raw + " " + eos_token + " " + aspect + " " + eos_token ) text_bert_tokens = tokenizer.tokenize(text_spc) diff --git a/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_inference.py b/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_inference.py index b9157c179..ac7ffaf97 100644 --- a/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_inference.py +++ b/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_inference.py @@ -21,13 +21,13 @@ class InputExample(object): """A single training_tutorials/test example for simple sequence classification.""" def __init__( - self, - guid, - text_a, - text_b=None, - IOB_label=None, - aspect_label=None, - polarity=None, + self, + guid, + text_a, + text_b=None, + IOB_label=None, + aspect_label=None, + polarity=None, ): """Constructs a InputExample. @@ -52,19 +52,19 @@ class InputFeatures(object): """A single set of features of raw_data.""" def __init__( - self, - input_ids_spc, - input_mask, - segment_ids, - label_id, - aspect=None, - positions=None, - polarity=None, - valid_ids=None, - label_mask=None, - tokens=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + input_mask, + segment_ids, + label_id, + aspect=None, + positions=None, + polarity=None, + valid_ids=None, + label_mask=None, + tokens=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): self.input_ids_spc = input_ids_spc self.aspect = aspect @@ -161,7 +161,7 @@ def _create_examples(self, lines): def convert_ate_examples_to_features( - examples, label_list, max_seq_len, tokenizer, config=None + examples, label_list, max_seq_len, tokenizer, config=None ): """Loads a raw_data file into a list of `InputBatch`s.""" @@ -185,7 +185,7 @@ def convert_ate_examples_to_features( valid = [] label_mask = [] enum_tokens = ( - [bos_token] + text_tokens + [eos_token] + aspect_tokens + [eos_token] + [bos_token] + text_tokens + [eos_token] + aspect_tokens + [eos_token] ) IOB_label = [bos_token] + IOB_label + [eos_token] + aspect_label + [eos_token] @@ -200,9 +200,9 @@ def convert_ate_examples_to_features( valid.append(1) else: valid.append(0) - tokens = tokens[0: min(len(tokens), max_seq_len - 2)] - labels = labels[0: min(len(labels), max_seq_len - 2)] - valid = valid[0: min(len(valid), max_seq_len - 2)] + tokens = tokens[0 : min(len(tokens), max_seq_len - 2)] + labels = labels[0 : min(len(labels), max_seq_len - 2)] + valid = valid[0 : min(len(valid), max_seq_len - 2)] # segment_ids = [0] * len(example.text_a[:]) + [1] * (max_seq_len - len([0] * len(example.text_a[:]))) # segment_ids = segment_ids[:max_seq_len] @@ -249,7 +249,7 @@ def convert_ate_examples_to_features( def convert_apc_examples_to_features( - examples, label_list, max_seq_len, tokenizer, config=None + examples, label_list, max_seq_len, tokenizer, config=None ): """Loads a raw_data file into a list of `InputBatch`s.""" @@ -271,9 +271,9 @@ def convert_apc_examples_to_features( # aspect_label = example.aspect_label aspect_label = ["B-ASP"] * len(aspect_tokens) polarity = ( - [LabelPaddingOption.SENTIMENT_PADDING] - + example.polarity - + [LabelPaddingOption.SENTIMENT_PADDING] + [LabelPaddingOption.SENTIMENT_PADDING] + + example.polarity + + [LabelPaddingOption.SENTIMENT_PADDING] ) positions = np.where(np.array(polarity) > 0)[0].tolist() tokens = [] @@ -281,7 +281,7 @@ def convert_apc_examples_to_features( valid = [] label_mask = [] enum_tokens = ( - [bos_token] + text_tokens + [eos_token] + aspect_tokens + [eos_token] + [bos_token] + text_tokens + [eos_token] + aspect_tokens + [eos_token] ) IOB_label = [bos_token] + IOB_label + [eos_token] + aspect_label + [eos_token] enum_tokens = enum_tokens[:max_seq_len] @@ -318,11 +318,11 @@ def convert_apc_examples_to_features( valid.append(1) else: valid.append(0) - tokens = tokens[0: min(len(tokens), max_seq_len - 2)] - labels = labels[0: min(len(labels), max_seq_len - 2)] - valid = valid[0: min(len(valid), max_seq_len - 2)] + tokens = tokens[0 : min(len(tokens), max_seq_len - 2)] + labels = labels[0 : min(len(labels), max_seq_len - 2)] + valid = valid[0 : min(len(valid), max_seq_len - 2)] segment_ids = [0] * len(example.text_a[:]) + [1] * ( - max_seq_len - len([0] * len(example.text_a[:])) + max_seq_len - len([0] * len(example.text_a[:])) ) segment_ids = segment_ids[:max_seq_len] label_ids = [] diff --git a/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_training.py b/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_training.py index adbcac5f1..0b43d63ba 100644 --- a/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_training.py +++ b/pyabsa/tasks/AspectTermExtraction/dataset_utils/__lcf__/data_utils_for_training.py @@ -26,13 +26,13 @@ class InputExample(object): """A single training_tutorials/test example for simple sequence classification.""" def __init__( - self, - guid, - text_a, - text_b=None, - IOB_label=None, - aspect_label=None, - polarity=None, + self, + guid, + text_a, + text_b=None, + IOB_label=None, + aspect_label=None, + polarity=None, ): """Constructs a InputExample. @@ -57,17 +57,17 @@ class InputFeatures(object): """A single set of features of raw_data.""" def __init__( - self, - input_ids_spc, - input_mask, - segment_ids, - label_id, - polarity=None, - valid_ids=None, - label_mask=None, - tokens=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + input_mask, + segment_ids, + label_id, + polarity=None, + valid_ids=None, + label_mask=None, + tokens=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): self.input_ids_spc = input_ids_spc self.input_mask = input_mask @@ -120,33 +120,33 @@ def readfile(filename): # for more IOB labels support, but can not split cases in some particular conditions, e.g., (B,I,E,O) for p_idx in range(len(p) - 1): if ( - p[p_idx] != p[p_idx + 1] - and p[p_idx] != str(LabelPaddingOption.SENTIMENT_PADDING) - and p[p_idx + 1] != str(LabelPaddingOption.SENTIMENT_PADDING) + p[p_idx] != p[p_idx + 1] + and p[p_idx] != str(LabelPaddingOption.SENTIMENT_PADDING) + and p[p_idx + 1] != str(LabelPaddingOption.SENTIMENT_PADDING) ) or ( - p[p_idx] != str(LabelPaddingOption.SENTIMENT_PADDING) - and p[p_idx + 1] == str(LabelPaddingOption.SENTIMENT_PADDING) + p[p_idx] != str(LabelPaddingOption.SENTIMENT_PADDING) + and p[p_idx + 1] == str(LabelPaddingOption.SENTIMENT_PADDING) ): - _p = p[: p_idx + 1] + polarity_padding[p_idx + 1:] - p = polarity_padding[: p_idx + 1] + p[p_idx + 1:] + _p = p[: p_idx + 1] + polarity_padding[p_idx + 1 :] + p = polarity_padding[: p_idx + 1] + p[p_idx + 1 :] prepared_data.append((s, t, _p)) else: for t_idx in range(1, len(t)): # for 3 IOB label (B, I, O) if p[t_idx - 1] != str( - LabelPaddingOption.SENTIMENT_PADDING + LabelPaddingOption.SENTIMENT_PADDING ) and split_aspect(t[t_idx - 1], t[t_idx]): _p = p[:t_idx] + polarity_padding[t_idx:] p = polarity_padding[:t_idx] + p[t_idx:] prepared_data.append((s, t, _p)) if ( - p[t_idx] != str(LabelPaddingOption.SENTIMENT_PADDING) - and t_idx == len(t) - 1 - and split_aspect(t[t_idx]) + p[t_idx] != str(LabelPaddingOption.SENTIMENT_PADDING) + and t_idx == len(t) - 1 + and split_aspect(t[t_idx]) ): - _p = p[: t_idx + 1] + polarity_padding[t_idx + 1:] - p = polarity_padding[: t_idx + 1] + p[t_idx + 1:] + _p = p[: t_idx + 1] + polarity_padding[t_idx + 1 :] + p = polarity_padding[: t_idx + 1] + p[t_idx + 1 :] prepared_data.append((s, t, _p)) return prepared_data @@ -282,7 +282,7 @@ def convert_examples_to_features(examples, max_seq_len, tokenizer, config=None): features = [] polarities_set = set() for ex_index, example in enumerate( - tqdm.tqdm(examples, desc="convert examples to features") + tqdm.tqdm(examples, desc="convert examples to features") ): text_tokens = example.text_a[:] aspect_tokens = example.text_b[:] @@ -290,8 +290,8 @@ def convert_examples_to_features(examples, max_seq_len, tokenizer, config=None): aspect_label = example.aspect_label polarity = example.polarity if ( - polarity != LabelPaddingOption.SENTIMENT_PADDING - or int(polarity) != LabelPaddingOption.SENTIMENT_PADDING + polarity != LabelPaddingOption.SENTIMENT_PADDING + or int(polarity) != LabelPaddingOption.SENTIMENT_PADDING ): # bad case handle in Chinese atepc_datasets polarities_set.add(polarity) # ignore samples without polarities tokens = [] @@ -299,7 +299,7 @@ def convert_examples_to_features(examples, max_seq_len, tokenizer, config=None): valid = [] label_mask = [] enum_tokens = ( - [bos_token] + text_tokens + [eos_token] + aspect_tokens + [eos_token] + [bos_token] + text_tokens + [eos_token] + aspect_tokens + [eos_token] ) IOB_label = [bos_token] + IOB_label + [eos_token] + aspect_label + [eos_token] @@ -332,9 +332,9 @@ def convert_examples_to_features(examples, max_seq_len, tokenizer, config=None): valid.append(1) else: valid.append(0) - tokens = tokens[0: min(len(tokens), max_seq_len - 2)] - labels = labels[0: min(len(labels), max_seq_len - 2)] - valid = valid[0: min(len(valid), max_seq_len - 2)] + tokens = tokens[0 : min(len(tokens), max_seq_len - 2)] + labels = labels[0 : min(len(labels), max_seq_len - 2)] + valid = valid[0 : min(len(valid), max_seq_len - 2)] # segment_ids = [0] * len(example.text_a[:]) + [1] * (max_seq_len - len([0] * len(example.text_a[:]))) # segment_ids = segment_ids[:max_seq_len] diff --git a/pyabsa/tasks/AspectTermExtraction/instructor/atepc_instructor.py b/pyabsa/tasks/AspectTermExtraction/instructor/atepc_instructor.py index 3306bdf6f..afad10d6c 100644 --- a/pyabsa/tasks/AspectTermExtraction/instructor/atepc_instructor.py +++ b/pyabsa/tasks/AspectTermExtraction/instructor/atepc_instructor.py @@ -205,12 +205,12 @@ def _load_dataset_and_prepare_dataloader(self): self.test_set = None self.num_train_optimization_steps = ( - int( - len(self.train_set) - / self.config.batch_size - / self.config.gradient_accumulation_steps - ) - * self.config.num_epoch + int( + len(self.train_set) + / self.config.batch_size + / self.config.gradient_accumulation_steps + ) + * self.config.num_epoch ) train_sampler = RandomSampler(self.train_set) @@ -348,7 +348,7 @@ def _train_and_evaluate(self, criterion): ate_loss_weight = self.config.args.get("ate_loss_weight", 1.0) loss = ( - loss_ate + ate_loss_weight * loss_apc + loss_ate + ate_loss_weight * loss_apc ) # the optimal weight of loss may be different according to dataset sum_loss += loss.item() @@ -394,31 +394,31 @@ def _train_and_evaluate(self, criterion): self.config.metrics_of_this_checkpoint["ate_f1"] = ate_result if ( - apc_result["apc_test_acc"] - > self.config.max_test_metrics["max_apc_test_acc"] - or apc_result["apc_test_f1"] - > self.config.max_test_metrics["max_apc_test_f1"] - or ate_result - > self.config.max_test_metrics["max_ate_test_f1"] + apc_result["apc_test_acc"] + > self.config.max_test_metrics["max_apc_test_acc"] + or apc_result["apc_test_f1"] + > self.config.max_test_metrics["max_apc_test_f1"] + or ate_result + > self.config.max_test_metrics["max_ate_test_f1"] ): patience = self.config.patience - 1 if ( - apc_result["apc_test_acc"] - > self.config.max_test_metrics["max_apc_test_acc"] + apc_result["apc_test_acc"] + > self.config.max_test_metrics["max_apc_test_acc"] ): self.config.max_test_metrics[ "max_apc_test_acc" ] = apc_result["apc_test_acc"] if ( - apc_result["apc_test_f1"] - > self.config.max_test_metrics["max_apc_test_f1"] + apc_result["apc_test_f1"] + > self.config.max_test_metrics["max_apc_test_f1"] ): self.config.max_test_metrics[ "max_apc_test_f1" ] = apc_result["apc_test_f1"] if ( - ate_result - > self.config.max_test_metrics["max_ate_test_f1"] + ate_result + > self.config.max_test_metrics["max_ate_test_f1"] ): self.config.max_test_metrics[ "max_ate_test_f1" @@ -698,7 +698,8 @@ def _evaluate_acc_f1(self, test_dataloader, eval_ATE=True, eval_APC=True): torch.argmax(test_polarities_all, -1).cpu(), target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -739,11 +740,11 @@ def _init_misc(self): ) self.config.batch_size = ( - self.config.batch_size // self.config.gradient_accumulation_steps + self.config.batch_size // self.config.gradient_accumulation_steps ) if self.config.model_path_to_save and not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/bert_base_atepc.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/bert_base_atepc.py index 7184d82ea..42124b07e 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/bert_base_atepc.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/bert_base_atepc.py @@ -44,7 +44,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.bert4global.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -53,20 +53,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.bert4global.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): if self.config.use_bert_spc: input_ids_spc = self.get_ids_for_local_context_extractor(input_ids_spc) diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcf_atepc.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcf_atepc.py index e5daaf9d7..aab62fbc6 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcf_atepc.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcf_atepc.py @@ -44,7 +44,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.bert4global.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -52,20 +52,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.bert4global.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcfs_atepc.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcfs_atepc.py index 482ecfcb8..cdca29c67 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcfs_atepc.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/fast_lcfs_atepc.py @@ -45,7 +45,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.bert4global.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -53,20 +53,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.bert4global.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc.py index 33bc93bb0..c1f4e7582 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc.py @@ -44,7 +44,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.bert4global.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -52,20 +52,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.bert4global.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc_large.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc_large.py index c008681b5..12cbb75fd 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc_large.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_atepc_large.py @@ -47,7 +47,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.bert4global.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -55,20 +55,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.bert4global.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_template_atepc.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_template_atepc.py index 7f98d133f..adb70af28 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_template_atepc.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcf_template_atepc.py @@ -31,7 +31,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.config.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -39,20 +39,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.config.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc.py index fa5abd3a8..720a25ba2 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc.py @@ -44,7 +44,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.bert4global.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -52,20 +52,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.bert4global.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None diff --git a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc_large.py b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc_large.py index b2a972892..46f2505bd 100644 --- a/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc_large.py +++ b/pyabsa/tasks/AspectTermExtraction/models/__lcf__/lcfs_atepc_large.py @@ -47,7 +47,7 @@ def get_batch_token_labels_bert_base_indices(self, labels): labels = labels.detach().cpu().numpy() for text_i in range(len(labels)): sep_index = np.argmax((labels[text_i] == self.num_labels - 1)) - labels[text_i][sep_index + 1:] = 0 + labels[text_i][sep_index + 1 :] = 0 return torch.tensor(labels).to(self.bert4global.device) def get_ids_for_local_context_extractor(self, text_indices): @@ -55,20 +55,20 @@ def get_ids_for_local_context_extractor(self, text_indices): text_ids = text_indices.detach().cpu().numpy() for text_i in range(len(text_ids)): sep_index = np.argmax((text_ids[text_i] == self.config.sep_indices)) - text_ids[text_i][sep_index + 1:] = 0 + text_ids[text_i][sep_index + 1 :] = 0 return torch.tensor(text_ids).to(self.bert4global.device) def forward( - self, - input_ids_spc, - token_type_ids=None, - attention_mask=None, - labels=None, - polarity=None, - valid_ids=None, - attention_mask_label=None, - lcf_cdm_vec=None, - lcf_cdw_vec=None, + self, + input_ids_spc, + token_type_ids=None, + attention_mask=None, + labels=None, + polarity=None, + valid_ids=None, + attention_mask_label=None, + lcf_cdm_vec=None, + lcf_cdw_vec=None, ): lcf_cdm_vec = lcf_cdm_vec.unsqueeze(2) if lcf_cdm_vec is not None else None lcf_cdw_vec = lcf_cdw_vec.unsqueeze(2) if lcf_cdw_vec is not None else None diff --git a/pyabsa/tasks/AspectTermExtraction/prediction/aspect_extractor.py b/pyabsa/tasks/AspectTermExtraction/prediction/aspect_extractor.py index 8f0ac61f4..82107adc3 100644 --- a/pyabsa/tasks/AspectTermExtraction/prediction/aspect_extractor.py +++ b/pyabsa/tasks/AspectTermExtraction/prediction/aspect_extractor.py @@ -116,13 +116,13 @@ def __init__(self, checkpoint=None, **kwargs): self.config.pretrained_bert.split("/")[-1] ), do_lower_case="uncased" - in self.config.pretrained_bert, + in self.config.pretrained_bert, ) else: self.tokenizer = AutoTokenizer.from_pretrained( self.config.pretrained_bert, do_lower_case="uncased" - in self.config.pretrained_bert, + in self.config.pretrained_bert, ) except ValueError: self.tokenizer = pickle.load(f) @@ -173,7 +173,7 @@ def merge_result(self, sentence_res, results): for item1, item2 in zip(results["extraction_res"], results["polarity_res"]): cur_example_id = item1[3] assert ( - cur_example_id == item2["example_id"] + cur_example_id == item2["example_id"] ), "ate and apc results should be same ordered" if pre_example_id is None or cur_example_id != pre_example_id: merged_results[cur_example_id] = { @@ -211,20 +211,29 @@ def merge_result(self, sentence_res, results): } ) else: - for item in sentence_res: + for item1, item2 in zip(sentence_res, results["extraction_res"]): final_res.append( - {"sentence": " ".join(item[0]), "IOB": item[1], "tokens": item[0]} + { + "sentence": " ".join(item2[0]), + "IOB": item2[1], + "tokens": item1[0], + "aspect": item2[3], + "position": [], + "sentiment": [], + "probs": [], + "confidence": [], + } ) return final_res def extract_aspect( - self, - inference_source: Union[List[Path], list, str], - save_result=True, - print_result=True, - pred_sentiment=True, - **kwargs + self, + inference_source: Union[List[Path], list, str], + save_result=True, + print_result=True, + pred_sentiment=True, + **kwargs ): """ Extract aspects and their corresponding polarities from a list of input files. @@ -245,12 +254,12 @@ def extract_aspect( ) def predict( - self, - text: Union[str, List[str]], - save_result=True, - print_result=True, - pred_sentiment=True, - **kwargs + self, + text: Union[str, List[str]], + save_result=True, + print_result=True, + pred_sentiment=True, + **kwargs ): """ Args: @@ -269,12 +278,12 @@ def predict( ) def batch_predict( - self, - target_file: Union[List[Path], list, str], - save_result=True, - print_result=True, - pred_sentiment=True, - **kwargs + self, + target_file: Union[List[Path], list, str], + save_result=True, + print_result=True, + pred_sentiment=True, + **kwargs ): """ Args: @@ -287,7 +296,7 @@ def batch_predict( self.config.eval_batch_size = kwargs.get("eval_batch_size", 32) - results = {"extraction_res": OrderedDict(), "polarity_res": OrderedDict()} + results = {"extraction_res": None, "polarity_res": None} if isinstance(target_file, DatasetItem) or isinstance(target_file, str): # using integrated inference dataset inference_set = detect_infer_dataset( @@ -305,6 +314,24 @@ def batch_predict( if target_file: extraction_res, sentence_res = self._extract(target_file) + if not pred_sentiment: + filtered_res = [] + for i, res in enumerate(extraction_res): + bio_tags = res[1] + aspect = [] + for idx, tag in enumerate(bio_tags): + if "B-ASP" in tag: + aspect.append(res[0][idx]) + elif "I-ASP" in tag and aspect: + aspect[-1] += " " + res[0][idx] + if not filtered_res: + filtered_res.append((res[0], aspect, res[2], aspect)) + else: + if filtered_res[-1][0] != res[0]: + filtered_res.append((res[0], res[1], res[2], aspect)) + else: + filtered_res[-1][1].extend(aspect) + extraction_res = filtered_res results["extraction_res"] = extraction_res if pred_sentiment: results["polarity_res"] = self._run_prediction( @@ -329,7 +356,7 @@ def batch_predict( for ex_id, r in enumerate(results): colored_text = r["sentence"][:] for aspect, sentiment, confidence in zip( - r["aspect"], r["sentiment"], r["confidence"] + r["aspect"], r["sentiment"], r["confidence"] ): if sentiment.upper() == "POSITIVE": colored_aspect = colored( @@ -433,13 +460,13 @@ def _extract(self, examples): else: it = self.infer_dataloader for i_batch, ( - input_ids_spc, - segment_ids, - input_mask, - label_ids, - polarity, - valid_ids, - l_mask, + input_ids_spc, + segment_ids, + input_mask, + label_ids, + polarity, + valid_ids, + l_mask, ) in enumerate(it): input_ids_spc = input_ids_spc.to(self.config.device) segment_ids = segment_ids.to(self.config.device) @@ -474,7 +501,7 @@ def _extract(self, examples): if j == 0: continue elif len(pred_iobs) == len( - all_tokens[i + (self.config.eval_batch_size * i_batch)] + all_tokens[i + (self.config.eval_batch_size * i_batch)] ): break else: @@ -483,7 +510,7 @@ def _extract(self, examples): ate_result = [] polarity = [] for t, l in zip( - all_tokens[i + (self.config.eval_batch_size * i_batch)], pred_iobs + all_tokens[i + (self.config.eval_batch_size * i_batch)], pred_iobs ): ate_result.append("{}({})".format(t, l)) if "ASP" in l: @@ -500,7 +527,7 @@ def _extract(self, examples): pred_iobs = process_iob_tags(pred_iobs) for idx in range(1, len(polarity)): if polarity[idx - 1] != str( - LabelPaddingOption.SENTIMENT_PADDING + LabelPaddingOption.SENTIMENT_PADDING ) and split_aspect(pred_iobs[idx - 1], pred_iobs[idx]): _polarity = polarity[:idx] + POLARITY_PADDING[idx:] polarity = POLARITY_PADDING[:idx] + polarity[idx:] @@ -514,12 +541,12 @@ def _extract(self, examples): ) if ( - polarity[idx] != str(LabelPaddingOption.SENTIMENT_PADDING) - and idx == len(polarity) - 1 - and split_aspect(pred_iobs[idx]) + polarity[idx] != str(LabelPaddingOption.SENTIMENT_PADDING) + and idx == len(polarity) - 1 + and split_aspect(pred_iobs[idx]) ): - _polarity = polarity[: idx + 1] + POLARITY_PADDING[idx + 1:] - polarity = POLARITY_PADDING[: idx + 1] + polarity[idx + 1:] + _polarity = polarity[: idx + 1] + POLARITY_PADDING[idx + 1 :] + polarity = POLARITY_PADDING[: idx + 1] + polarity[idx + 1 :] extraction_res.append( ( all_tokens[i + (self.config.eval_batch_size * i_batch)], @@ -633,9 +660,9 @@ def _run_prediction(self, examples): ) for i, i_apc_logits in enumerate(apc_logits): if ( - "index_to_label" in self.config.args - and int(i_apc_logits.argmax(axis=-1)) - in self.config.index_to_label + "index_to_label" in self.config.args + and int(i_apc_logits.argmax(axis=-1)) + in self.config.index_to_label ): sent = self.config.index_to_label.get( int(i_apc_logits.argmax(axis=-1)) diff --git a/pyabsa/tasks/AspectTermExtraction/trainer/atepc_trainer.py b/pyabsa/tasks/AspectTermExtraction/trainer/atepc_trainer.py index 625e97899..ef3b6210a 100644 --- a/pyabsa/tasks/AspectTermExtraction/trainer/atepc_trainer.py +++ b/pyabsa/tasks/AspectTermExtraction/trainer/atepc_trainer.py @@ -9,28 +9,28 @@ from typing import Union -from pyabsa.framework.flag_class import ( +from pyabsa.framework.flag_class.flag_template import ( DeviceTypeOption, ModelSaveOption, TaskCodeOption, TaskNameOption, ) +from pyabsa.framework.trainer_class.trainer_template import Trainer from ..configuration.atepc_configuration import ATEPCConfigManager -from ..prediction.aspect_extractor import AspectExtractor from ..instructor.atepc_instructor import ATEPCTrainingInstructor -from pyabsa.framework.trainer_class.trainer_template import Trainer +from ..prediction.aspect_extractor import AspectExtractor class ATEPCTrainer(Trainer): def __init__( - self, - config: ATEPCConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: ATEPCConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_inference.py b/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_inference.py index 68ebc5ec3..e68a99708 100644 --- a/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_inference.py +++ b/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_inference.py @@ -124,10 +124,10 @@ def prepare_token_ids(self, code_ids, sliding_window=False): # print((x + 1) * (self.config.max_seq_len - 2) // 2 + (self.config.max_seq_len - 2) // 2) for x in range(len(code_ids) // (self.config.max_seq_len - 2) + 1): _code_ids = code_ids[ - x - * (self.config.max_seq_len - 2): (x + 1) - * (self.config.max_seq_len - 2) - ] + x + * (self.config.max_seq_len - 2) : (x + 1) + * (self.config.max_seq_len - 2) + ] _code_ids = pad_and_truncate( _code_ids, self.config.max_seq_len - 2, diff --git a/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_training.py b/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_training.py index e106a46b3..eddb1b067 100644 --- a/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_training.py +++ b/pyabsa/tasks/CodeDefectDetection/dataset_utils/__plm__/data_utils_for_training.py @@ -38,7 +38,7 @@ def load_data_from_file(self, dataset_file, **kwargs): c_label_set = set() for ex_id, line in enumerate( - tqdm.tqdm(natural_examples, desc="preparing dataloader") + tqdm.tqdm(natural_examples, desc="preparing dataloader") ): code_src, label = line.strip().split("$LABEL$") if "$FEATURE$" in code_src: @@ -82,10 +82,10 @@ def load_data_from_file(self, dataset_file, **kwargs): for _ in range(self.config.get("noise_instance_num", 0)): for ex_id, line in enumerate( - tqdm.tqdm( - corrupt_examples, - desc="preparing corrupted code dataloader for training set", - ) + tqdm.tqdm( + corrupt_examples, + desc="preparing corrupted code dataloader for training set", + ) ): code_src, label = line.strip().split("$LABEL$") if label == "0": @@ -149,10 +149,10 @@ def prepare_token_ids(self, code_ids, sliding_window=False): # print((x + 1) * (self.config.max_seq_len - 2) // 2 + (self.config.max_seq_len - 2) // 2) for x in range(len(code_ids) // (self.config.max_seq_len - 2) + 1): _code_ids = code_ids[ - x - * (self.config.max_seq_len - 2): (x + 1) - * (self.config.max_seq_len - 2) - ] + x + * (self.config.max_seq_len - 2) : (x + 1) + * (self.config.max_seq_len - 2) + ] _code_ids = pad_and_truncate( _code_ids, self.config.max_seq_len - 2, diff --git a/pyabsa/tasks/CodeDefectDetection/instructor/cdd_instructor.py b/pyabsa/tasks/CodeDefectDetection/instructor/cdd_instructor.py index 9afb9729f..2cb0138b0 100644 --- a/pyabsa/tasks/CodeDefectDetection/instructor/cdd_instructor.py +++ b/pyabsa/tasks/CodeDefectDetection/instructor/cdd_instructor.py @@ -279,8 +279,8 @@ def _train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -418,7 +418,7 @@ def _k_fold_train_and_evaluate(self, criterion): losses = [] for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -519,7 +519,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -541,8 +541,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -565,8 +565,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) iterator.set_postfix_str(postfix) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, @@ -789,12 +789,18 @@ def _evaluate_acc_f1(self, test_dataloader): t_targets_all.cpu(), torch.argmax(t_outputs_all.cpu(), -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) - fprint('\n---------------------------- Confusion Matrix ----------------------------\n') + fprint( + "\n---------------------------- Confusion Matrix ----------------------------\n" + ) rprint(report) - fprint('\n---------------------------- Confusion Matrix ----------------------------\n') + fprint( + "\n---------------------------- Confusion Matrix ----------------------------\n" + ) report = metrics.classification_report( t_c_targets_all.cpu(), diff --git a/pyabsa/tasks/CodeDefectDetection/models/__plm__/bert.py b/pyabsa/tasks/CodeDefectDetection/models/__plm__/bert.py index 56c8e69d4..604113346 100644 --- a/pyabsa/tasks/CodeDefectDetection/models/__plm__/bert.py +++ b/pyabsa/tasks/CodeDefectDetection/models/__plm__/bert.py @@ -46,8 +46,8 @@ def __init__(self, bert, config): self.loss_fct1 = nn.CrossEntropyLoss() self.loss_fct2 = nn.CrossEntropyLoss() elif ( - self.config.get("loss_fn", "CrossEntropyLoss") - == "ClassBalanceCrossEntropyLoss" + self.config.get("loss_fn", "CrossEntropyLoss") + == "ClassBalanceCrossEntropyLoss" ): fprint("Using ClassBalanceCrossEntropyLoss") self.loss_fct1 = ClassBalanceCrossEntropyLoss() @@ -98,8 +98,8 @@ def get_bart_vec(self, source_ids): def get_roberta_vec(self, source_ids): attention_mask = source_ids.ne(self.tokenizer.pad_token_id) vec = self.encoder(input_ids=source_ids, attention_mask=attention_mask)[0][ - :, 0, : - ] + :, 0, : + ] return vec def forward(self, inputs): diff --git a/pyabsa/tasks/CodeDefectDetection/models/__plm__/models.py b/pyabsa/tasks/CodeDefectDetection/models/__plm__/models.py index d39e78749..a260492e6 100644 --- a/pyabsa/tasks/CodeDefectDetection/models/__plm__/models.py +++ b/pyabsa/tasks/CodeDefectDetection/models/__plm__/models.py @@ -172,7 +172,7 @@ def __init__(self, para_dict=None): super(ClassBalanceCE, self).__init__(para_dict) self.beta = self.para_dict["cfg"].LOSS.ClassBalanceCE.BETA self.class_balanced_weight = np.array( - [(1 - self.beta) / (1 - self.beta ** N) for N in self.num_class_list] + [(1 - self.beta) / (1 - self.beta**N) for N in self.num_class_list] ) self.class_balanced_weight = torch.FloatTensor( self.class_balanced_weight @@ -261,8 +261,8 @@ def get_bart_vec(self, source_ids): def get_roberta_vec(self, source_ids): attention_mask = source_ids.ne(self.tokenizer.pad_token_id) vec = self.encoder(input_ids=source_ids, attention_mask=attention_mask)[0][ - :, 0, : - ] + :, 0, : + ] return vec def forward(self, source_ids=None, labels=None, corrupt_labels=None): @@ -314,14 +314,14 @@ class Seq2Seq(nn.Module): """ def __init__( - self, - encoder, - decoder, - config, - beam_size=None, - max_length=None, - sos_id=None, - eos_id=None, + self, + encoder, + decoder, + config, + beam_size=None, + max_length=None, + sos_id=None, + eos_id=None, ): super(Seq2Seq, self).__init__() self.encoder = encoder @@ -354,18 +354,18 @@ def tie_weights(self): ) def forward( - self, - source_ids=None, - source_mask=None, - target_ids=None, - target_mask=None, - args=None, + self, + source_ids=None, + source_mask=None, + target_ids=None, + target_mask=None, + args=None, ): outputs = self.encoder(source_ids, attention_mask=source_mask) encoder_output = outputs[0].permute([1, 0, 2]).contiguous() if target_ids is not None: attn_mask = -1e4 * ( - 1 - self.bias[: target_ids.shape[1], : target_ids.shape[1]] + 1 - self.bias[: target_ids.shape[1], : target_ids.shape[1]] ) tgt_embeddings = ( self.encoder.embeddings(target_ids).permute([1, 0, 2]).contiguous() @@ -397,8 +397,8 @@ def forward( preds = [] zero = torch.cuda.LongTensor(1).fill_(0) for i in range(source_ids.shape[0]): - context = encoder_output[:, i: i + 1] - context_mask = source_mask[i: i + 1, :] + context = encoder_output[:, i : i + 1] + context_mask = source_mask[i : i + 1, :] beam = Beam(self.beam_size, self.sos_id, self.eos_id) input_ids = beam.getCurrentState() context = context.repeat(1, self.beam_size, 1) @@ -407,7 +407,7 @@ def forward( if beam.done(): break attn_mask = -1e4 * ( - 1 - self.bias[: input_ids.shape[1], : input_ids.shape[1]] + 1 - self.bias[: input_ids.shape[1], : input_ids.shape[1]] ) tgt_embeddings = ( self.encoder.embeddings(input_ids) diff --git a/pyabsa/tasks/CodeDefectDetection/prediction/code_defect_detector.py b/pyabsa/tasks/CodeDefectDetection/prediction/code_defect_detector.py index 766cb51ac..e8ff888ec 100644 --- a/pyabsa/tasks/CodeDefectDetection/prediction/code_defect_detector.py +++ b/pyabsa/tasks/CodeDefectDetection/prediction/code_defect_detector.py @@ -126,7 +126,7 @@ def __init__(self, checkpoint=None, cal_perplexity=False, **kwargs): ) if not hasattr( - GloVeCDDModelList, self.config.model.__name__ + GloVeCDDModelList, self.config.model.__name__ ) and not hasattr(BERTCDDModelList, self.config.model.__name__): raise KeyError( "The checkpoint and PyABSA you are loading is not from classifier model." @@ -162,12 +162,12 @@ def _log_write_args(self): fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) def batch_infer( - self, - target_file=None, # A file containing text inputs to perform inference on - print_result=True, # Whether to print the result of each prediction - save_result=False, # Whether to save the result of each prediction - ignore_error=True, # Whether to ignore errors encountered during inference - **kwargs # Additional keyword arguments to be passed to batch_predict method + self, + target_file=None, # A file containing text inputs to perform inference on + print_result=True, # Whether to print the result of each prediction + save_result=False, # Whether to save the result of each prediction + ignore_error=True, # Whether to ignore errors encountered during inference + **kwargs # Additional keyword arguments to be passed to batch_predict method ): """ Perform batch inference on a given target file. @@ -191,11 +191,11 @@ def batch_infer( ) def infer( - self, - text: Union[str, list] = None, # The text inputs to perform inference on - print_result=True, # Whether to print the result of each prediction - ignore_error=True, # Whether to ignore errors encountered during inference - **kwargs # Additional keyword arguments to be passed to predict method + self, + text: Union[str, list] = None, # The text inputs to perform inference on + print_result=True, # Whether to print the result of each prediction + ignore_error=True, # Whether to ignore errors encountered during inference + **kwargs # Additional keyword arguments to be passed to predict method ): """ Perform inference on a given text input. @@ -214,12 +214,12 @@ def infer( ) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict from a file of labelences. @@ -256,11 +256,11 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict from a labelence or a list of labelences. @@ -398,8 +398,8 @@ def _run_prediction(self, save_path=None, print_result=True): else: real_label = "N.A." if ( - real_label != LabelPaddingOption.LABEL_PADDING - and real_label != str(LabelPaddingOption.LABEL_PADDING) + real_label != LabelPaddingOption.LABEL_PADDING + and real_label != str(LabelPaddingOption.LABEL_PADDING) ): n_labeled += 1 @@ -494,7 +494,8 @@ def _run_prediction(self, save_path=None, print_result=True): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -509,7 +510,9 @@ def _run_prediction(self, save_path=None, print_result=True): targets_all, np.argmax(t_outputs_all, -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( @@ -540,7 +543,9 @@ def _run_prediction(self, save_path=None, print_result=True): c_targets_all, np.argmax(t_c_outputs_all, -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( diff --git a/pyabsa/tasks/CodeDefectDetection/trainer/cdd_trainer.py b/pyabsa/tasks/CodeDefectDetection/trainer/cdd_trainer.py index 13cfc8e4f..0e0a01ad4 100644 --- a/pyabsa/tasks/CodeDefectDetection/trainer/cdd_trainer.py +++ b/pyabsa/tasks/CodeDefectDetection/trainer/cdd_trainer.py @@ -23,14 +23,14 @@ class CDDTrainer(Trainer): def __init__( - self, - config: CDDConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: CDDConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/RNAClassification/dataset_utils/__classic__/data_utils_for_training.py b/pyabsa/tasks/RNAClassification/dataset_utils/__classic__/data_utils_for_training.py index 6410b7acc..344b721a9 100644 --- a/pyabsa/tasks/RNAClassification/dataset_utils/__classic__/data_utils_for_training.py +++ b/pyabsa/tasks/RNAClassification/dataset_utils/__classic__/data_utils_for_training.py @@ -22,7 +22,7 @@ def load_data_from_dict(self, dataset_dict, **kwargs): all_data = [] for ex_id, data in enumerate( - tqdm.tqdm(dataset_dict[self.dataset_type], desc="preparing dataloader") + tqdm.tqdm(dataset_dict[self.dataset_type], desc="preparing dataloader") ): exon1, intron, exon2, label = ( data["exon1"], @@ -60,7 +60,7 @@ def load_data_from_file(self, dataset_file, **kwargs): label_set = set() for ex_id, i in enumerate( - tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") ): text, _, label = lines[i].strip().partition("$LABEL$") label = label.strip() if label else LabelPaddingOption.LABEL_PADDING diff --git a/pyabsa/tasks/RNAClassification/dataset_utils/data_utils_for_training.py b/pyabsa/tasks/RNAClassification/dataset_utils/data_utils_for_training.py index 4070671a4..f264efdfc 100644 --- a/pyabsa/tasks/RNAClassification/dataset_utils/data_utils_for_training.py +++ b/pyabsa/tasks/RNAClassification/dataset_utils/data_utils_for_training.py @@ -22,7 +22,7 @@ def load_data_from_dict(self, dataset_dict, **kwargs): all_data = [] for ex_id, data in enumerate( - tqdm.tqdm(dataset_dict[self.dataset_type], desc="preparing dataloader") + tqdm.tqdm(dataset_dict[self.dataset_type], desc="preparing dataloader") ): exon1, intron, exon2, label = ( data["exon1"], @@ -60,7 +60,7 @@ def load_data_from_file(self, dataset_file, **kwargs): label_set = set() for ex_id, i in enumerate( - tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") ): text, _, label = lines[i].strip().partition("$LABEL$") label = label.strip() if label else LabelPaddingOption.LABEL_PADDING diff --git a/pyabsa/tasks/RNAClassification/instructor/rnac_instructor.py b/pyabsa/tasks/RNAClassification/instructor/rnac_instructor.py index e8af2d55d..f6cdff6d3 100644 --- a/pyabsa/tasks/RNAClassification/instructor/rnac_instructor.py +++ b/pyabsa/tasks/RNAClassification/instructor/rnac_instructor.py @@ -187,8 +187,8 @@ def _train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -306,7 +306,7 @@ def _k_fold_train_and_evaluate(self, criterion): self.config.max_test_metrics = {"max_test_acc": 0, "max_test_f1": 0} for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -386,8 +386,8 @@ def _k_fold_train_and_evaluate(self, criterion): # evaluate if test set is available if global_step % self.config.log_step == 0: if ( - self.valid_dataloader - and epoch >= self.config.evaluate_begin + self.valid_dataloader + and epoch >= self.config.evaluate_begin ): test_acc, f1 = self._evaluate_acc_f1(valid_dataloader) @@ -404,7 +404,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -423,8 +423,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -447,8 +447,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) iterator.set_postfix_str(postfix) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, @@ -572,7 +572,8 @@ def _evaluate_acc_f1(self, test_dataloader): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -587,7 +588,9 @@ def _evaluate_acc_f1(self, test_dataloader): t_targets_all.cpu(), torch.argmax(t_outputs_all.cpu(), -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( diff --git a/pyabsa/tasks/RNAClassification/models/__classic__/transformer.py b/pyabsa/tasks/RNAClassification/models/__classic__/transformer.py index fb2728c59..8e48355cf 100644 --- a/pyabsa/tasks/RNAClassification/models/__classic__/transformer.py +++ b/pyabsa/tasks/RNAClassification/models/__classic__/transformer.py @@ -29,14 +29,14 @@ def __init__(self, embedding_matrix, config): self.classifier = nn.Linear(self.config.hidden_dim, self.config.output_dim) def forward( - self, - input_ids, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - labels=None, + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + labels=None, ): transformer_outputs = self.transformer( input_ids=input_ids, diff --git a/pyabsa/tasks/RNAClassification/models/__plm__/bert.py b/pyabsa/tasks/RNAClassification/models/__plm__/bert.py index b78ea4b96..6004830b5 100644 --- a/pyabsa/tasks/RNAClassification/models/__plm__/bert.py +++ b/pyabsa/tasks/RNAClassification/models/__plm__/bert.py @@ -31,6 +31,7 @@ def forward(self, inputs): text_raw_indices = inputs[0] last_hidden_state = self.bert(text_raw_indices)["last_hidden_state"] pooled_out = self.pooler(last_hidden_state) + pooled_out = self.dropout(pooled_out) out = self.dense(pooled_out) if self.sigmoid: out = self.sigmoid(out) diff --git a/pyabsa/tasks/RNAClassification/prediction/rna_classifier.py b/pyabsa/tasks/RNAClassification/prediction/rna_classifier.py index ff50863a3..a0b0dbbe5 100644 --- a/pyabsa/tasks/RNAClassification/prediction/rna_classifier.py +++ b/pyabsa/tasks/RNAClassification/prediction/rna_classifier.py @@ -126,7 +126,7 @@ def __init__(self, checkpoint=None, cal_perplexity=False, **kwargs): ) if not hasattr( - GloVeRNACModelList, self.config.model.__name__ + GloVeRNACModelList, self.config.model.__name__ ) and not hasattr(BERTRNACModelList, self.config.model.__name__): raise KeyError( "The checkpoint you are loading is not from classifier model." @@ -162,12 +162,12 @@ def _log_write_args(self): fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict from a file of sentences. @@ -204,11 +204,11 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict from a sentence or a list of sentences. @@ -383,7 +383,8 @@ def _run_prediction(self, save_path=None, print_result=True): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -398,7 +399,9 @@ def _run_prediction(self, save_path=None, print_result=True): t_targets_all, np.argmax(t_outputs_all, -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( diff --git a/pyabsa/tasks/RNAClassification/trainer/rnac_trainer.py b/pyabsa/tasks/RNAClassification/trainer/rnac_trainer.py index 7815730f6..c10b5eed0 100644 --- a/pyabsa/tasks/RNAClassification/trainer/rnac_trainer.py +++ b/pyabsa/tasks/RNAClassification/trainer/rnac_trainer.py @@ -23,14 +23,14 @@ class RNACTrainer(Trainer): def __init__( - self, - config: RNACConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: RNACConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_inference.py b/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_inference.py index 95d3998d4..c51bafd8b 100644 --- a/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_inference.py +++ b/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_inference.py @@ -75,10 +75,10 @@ def process_data(self, samples, ignore_error=True): # continue for x in range(len(seq) // (self.config.max_seq_len * 3) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 3): (x + 1) - * (self.config.max_seq_len * 3) - ] + x + * (self.config.max_seq_len * 3) : (x + 1) + * (self.config.max_seq_len * 3) + ] rna_indices = self.tokenizer.text_to_sequence(_seq) rna_indices = pad_and_truncate( rna_indices, diff --git a/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_training.py b/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_training.py index 4a1a12440..d5e7628c6 100644 --- a/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_training.py +++ b/pyabsa/tasks/RNARegression/dataset_utils/__classic__/data_utils_for_training.py @@ -29,7 +29,7 @@ def load_data_from_file(self, dataset_file, **kwargs): label_set = set() for ex_id, i in enumerate( - tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") ): line = ( lines[i].strip().split("\t") @@ -56,10 +56,10 @@ def load_data_from_file(self, dataset_file, **kwargs): # _seq = seq[x * (config.max_seq_len * 2):(x + 1) * (config.max_seq_len * 2)] for x in range(len(seq) // (self.config.max_seq_len * 3) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 3): (x + 1) - * (self.config.max_seq_len * 3) - ] + x + * (self.config.max_seq_len * 3) : (x + 1) + * (self.config.max_seq_len * 3) + ] rna_indices = self.tokenizer.text_to_sequence(_seq) rna_indices = pad_and_truncate( rna_indices, diff --git a/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_inference.py b/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_inference.py index 334731f2e..3868a06ce 100644 --- a/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_inference.py +++ b/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_inference.py @@ -64,10 +64,10 @@ def process_data(self, samples, ignore_error=True): # continue for x in range(len(seq) // (self.config.max_seq_len * 2) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 2): (x + 1) - * (self.config.max_seq_len * 2) - ] + x + * (self.config.max_seq_len * 2) : (x + 1) + * (self.config.max_seq_len * 2) + ] # rna_indices = self.tokenizer.text_to_sequence(_seq) rna_indices = self.tokenizer.convert_tokens_to_ids(list(seq)) diff --git a/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_training.py b/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_training.py index 363dc4442..90aef0599 100644 --- a/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_training.py +++ b/pyabsa/tasks/RNARegression/dataset_utils/__plm__/data_utils_for_training.py @@ -27,7 +27,7 @@ def load_data_from_file(self, dataset_file, **kwargs): all_data = [] for ex_id, i in enumerate( - tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") ): line = ( lines[i].strip().split("\t") @@ -47,10 +47,10 @@ def load_data_from_file(self, dataset_file, **kwargs): for x in range(len(seq) // (self.config.max_seq_len * 2) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 2): (x + 1) - * (self.config.max_seq_len * 2) - ] + x + * (self.config.max_seq_len * 2) : (x + 1) + * (self.config.max_seq_len * 2) + ] rna_indices = self.tokenizer.text_to_sequence(_seq) rna_indices = pad_and_truncate( rna_indices, diff --git a/pyabsa/tasks/RNARegression/instructor/rnar_instructor.py b/pyabsa/tasks/RNARegression/instructor/rnar_instructor.py index 2158a3c57..98cbf41b3 100644 --- a/pyabsa/tasks/RNARegression/instructor/rnar_instructor.py +++ b/pyabsa/tasks/RNARegression/instructor/rnar_instructor.py @@ -98,7 +98,9 @@ def _load_dataset_and_prepare_dataloader(self): try: self.bert = AutoModel.from_pretrained( - self.config.pretrained_bert, ignore_mismatched_sizes=True + self.config.pretrained_bert, + trust_remote_code=True, + ignore_mismatched_sizes=True, ) except ValueError as e: fprint("Init pretrained model failed, exception: {}".format(e)) @@ -330,8 +332,8 @@ def _train_and_evaluate(self, criterion): self.config.metrics_of_this_checkpoint["r2"] = test_r2 - if test_r2 < max_fold_r2: - if test_r2 < max_fold_r2: + if test_r2 > max_fold_r2: + if test_r2 > max_fold_r2: patience = self.config.patience - 1 max_fold_r2 = test_r2 @@ -353,8 +355,8 @@ def _train_and_evaluate(self, criterion): ) if ( - test_r2 - < self.config.max_test_metrics["max_test_r2"] + test_r2 + < self.config.max_test_metrics["max_test_r2"] ): self.config.max_test_metrics[ "max_test_r2" @@ -447,7 +449,7 @@ def _k_fold_train_and_evaluate(self, criterion): self.config.max_test_metrics = {"max_test_r2": 0} for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -535,7 +537,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -553,8 +555,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_r2 - < self.config.max_test_metrics["max_test_r2"] + test_r2 + < self.config.max_test_metrics["max_test_r2"] ): self.config.max_test_metrics[ "max_test_r2" @@ -571,8 +573,8 @@ def _k_fold_train_and_evaluate(self, criterion): epoch, loss.item(), test_r2, max_fold_r2 ) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, diff --git a/pyabsa/tasks/RNARegression/models/__classic__/transformer.py b/pyabsa/tasks/RNARegression/models/__classic__/transformer.py index fb2728c59..8e48355cf 100644 --- a/pyabsa/tasks/RNARegression/models/__classic__/transformer.py +++ b/pyabsa/tasks/RNARegression/models/__classic__/transformer.py @@ -29,14 +29,14 @@ def __init__(self, embedding_matrix, config): self.classifier = nn.Linear(self.config.hidden_dim, self.config.output_dim) def forward( - self, - input_ids, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - labels=None, + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + labels=None, ): transformer_outputs = self.transformer( input_ids=input_ids, diff --git a/pyabsa/tasks/RNARegression/prediction/rna_regressor.py b/pyabsa/tasks/RNARegression/prediction/rna_regressor.py index f3077e4ef..ab5d23841 100644 --- a/pyabsa/tasks/RNARegression/prediction/rna_regressor.py +++ b/pyabsa/tasks/RNARegression/prediction/rna_regressor.py @@ -139,7 +139,7 @@ def __init__(self, checkpoint=None, **kwargs): ) if not hasattr( - GloVeRNARModelList, self.config.model.__name__ + GloVeRNARModelList, self.config.model.__name__ ) and not hasattr(BERTRNARModelList, self.config.model.__name__): raise KeyError( "The checkpoint you are loading is not from classifier model." @@ -173,12 +173,12 @@ def _log_write_args(self): fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict from a file of sentences. @@ -215,11 +215,11 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict from a sentence or a list of sentences. @@ -354,9 +354,9 @@ def _run_prediction(self, save_path=None, print_result=True): text_printing = result["text"][:] if result["ref_label"] != LabelPaddingOption.LABEL_PADDING: if ( - abs(result["label"] - result["ref_label"]) - / result["ref_label"] - <= 0.2 + abs(result["label"] - result["ref_label"]) + / result["ref_label"] + <= 0.2 ): text_info = colored( "#{}\t -> <{}(ref:{})>\t".format( diff --git a/pyabsa/tasks/RNARegression/trainer/rnar_trainer.py b/pyabsa/tasks/RNARegression/trainer/rnar_trainer.py index 20461c2af..43a2ea761 100644 --- a/pyabsa/tasks/RNARegression/trainer/rnar_trainer.py +++ b/pyabsa/tasks/RNARegression/trainer/rnar_trainer.py @@ -23,14 +23,14 @@ class RNARTrainer(Trainer): def __init__( - self, - config: RNARConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: RNARConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/TextAdversarialDefense/instructor/tad_instructor.py b/pyabsa/tasks/TextAdversarialDefense/instructor/tad_instructor.py index d8a022764..3f5187137 100644 --- a/pyabsa/tasks/TextAdversarialDefense/instructor/tad_instructor.py +++ b/pyabsa/tasks/TextAdversarialDefense/instructor/tad_instructor.py @@ -317,9 +317,9 @@ def _train_and_evaluate(self, criterion): adv_det_loss = criterion(advdet_logits, adv_det_targets) adv_train_loss = criterion(adv_tr_logits, adv_tr_targets) loss = ( - sen_loss - + self.config.args.get("adv_det_weight", 5) * adv_det_loss - + self.config.args.get("adv_train_weight", 5) * adv_train_loss + sen_loss + + self.config.args.get("adv_det_weight", 5) * adv_det_loss + + self.config.args.get("adv_train_weight", 5) * adv_train_loss ) losses.append(loss.item()) @@ -377,12 +377,12 @@ def _train_and_evaluate(self, criterion): ] = test_adv_tr_f1 if ( - test_label_acc > max_label_fold_acc - or test_label_acc > max_label_fold_f1 - or test_adv_det_acc > max_adv_det_fold_acc - or test_adv_det_f1 > max_adv_det_fold_f1 - or test_adv_tr_acc > max_adv_tr_fold_acc - or test_adv_tr_f1 > max_adv_tr_fold_f1 + test_label_acc > max_label_fold_acc + or test_label_acc > max_label_fold_f1 + or test_adv_det_acc > max_adv_det_fold_acc + or test_adv_det_f1 > max_adv_det_fold_f1 + or test_adv_tr_acc > max_adv_tr_fold_acc + or test_adv_tr_f1 > max_adv_tr_fold_f1 ): if test_label_acc > max_label_fold_acc: patience = self.config.patience - 1 @@ -434,51 +434,51 @@ def _train_and_evaluate(self, criterion): ) if ( - test_label_acc - > self.config.max_test_metrics["max_cls_test_acc"] + test_label_acc + > self.config.max_test_metrics["max_cls_test_acc"] ): self.config.max_test_metrics[ "max_cls_test_acc" ] = test_label_acc if ( - test_label_f1 - > self.config.max_test_metrics["max_cls_test_f1"] + test_label_f1 + > self.config.max_test_metrics["max_cls_test_f1"] ): self.config.max_test_metrics[ "max_cls_test_f1" ] = test_label_f1 if ( - test_adv_det_acc - > self.config.max_test_metrics[ - "max_adv_det_test_acc" - ] + test_adv_det_acc + > self.config.max_test_metrics[ + "max_adv_det_test_acc" + ] ): self.config.max_test_metrics[ "max_adv_det_test_acc" ] = test_adv_det_acc if ( - test_adv_det_f1 - > self.config.max_test_metrics[ - "max_adv_det_test_f1" - ] + test_adv_det_f1 + > self.config.max_test_metrics[ + "max_adv_det_test_f1" + ] ): self.config.max_test_metrics[ "max_adv_det_test_f1" ] = test_adv_det_f1 if ( - test_adv_tr_acc - > self.config.max_test_metrics[ - "max_adv_tr_test_acc" - ] + test_adv_tr_acc + > self.config.max_test_metrics[ + "max_adv_tr_test_acc" + ] ): self.config.max_test_metrics[ "max_adv_tr_test_acc" ] = test_adv_tr_acc if ( - test_adv_tr_f1 - > self.config.max_test_metrics["max_adv_tr_test_f1"] + test_adv_tr_f1 + > self.config.max_test_metrics["max_adv_tr_test_f1"] ): self.config.max_test_metrics[ "max_adv_tr_test_f1" @@ -756,7 +756,8 @@ def _evaluate_acc_f1(self, test_dataloader): torch.argmax(t_label_outputs_all.cpu(), -1), target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) ) diff --git a/pyabsa/tasks/TextAdversarialDefense/prediction/tad_classifier.py b/pyabsa/tasks/TextAdversarialDefense/prediction/tad_classifier.py index 4a0e190f8..e0975722f 100644 --- a/pyabsa/tasks/TextAdversarialDefense/prediction/tad_classifier.py +++ b/pyabsa/tasks/TextAdversarialDefense/prediction/tad_classifier.py @@ -210,7 +210,7 @@ def __init__(self, checkpoint=None, cal_perplexity=False, **kwargs): ) if not hasattr( - GloVeTADModelList, self.config.model.__name__ + GloVeTADModelList, self.config.model.__name__ ) and not hasattr(BERTTADModelList, self.config.model.__name__): raise KeyError( "The checkpoint you are loading is not from classifier model." @@ -245,13 +245,13 @@ def _log_write_args(self): fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) def batch_infer( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - defense: str = None, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + defense: str = None, + **kwargs ): """ Batch prediction on an input file. @@ -271,12 +271,12 @@ def batch_infer( ) def infer( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - defense: str = None, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + defense: str = None, + **kwargs ): """ Perform prediction on a single text or a list of texts. @@ -294,13 +294,13 @@ def infer( ) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - defense: str = None, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + defense: str = None, + **kwargs ): """ Predict from a file of sentences. @@ -339,12 +339,12 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - defense: str = None, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + defense: str = None, + **kwargs ): """ Predict from a sentence or a list of sentences. @@ -401,7 +401,7 @@ def _run_prediction(self, save_path=None, print_result=True, defense=None): ) for i, (prob, advdet_prob, adv_tr_prob) in enumerate( - zip(probs, advdet_probs, adv_tr_probs) + zip(probs, advdet_probs, adv_tr_probs) ): text_raw = sample["text_raw"][i] @@ -421,7 +421,7 @@ def _run_prediction(self, save_path=None, print_result=True, defense=None): ref_adv_tr_label = ( int(sample["adv_train_label"][i]) if int(sample["adv_train_label"][i]) - in self.config.index_to_adv_train_label + in self.config.index_to_adv_train_label else "" ) @@ -462,9 +462,9 @@ def _run_prediction(self, save_path=None, print_result=True, defense=None): else ref_is_adv_label, "ref_is_adv_check": correct[ pred_is_adv_label == ref_is_adv_label - ] + ] if ref_is_adv_label != -100 - and isinstance(ref_is_adv_label, int) + and isinstance(ref_is_adv_label, int) else "", "pred_adv_tr_label": self.config.index_to_label[ pred_adv_tr_label diff --git a/pyabsa/tasks/TextAdversarialDefense/trainer/tad_trainer.py b/pyabsa/tasks/TextAdversarialDefense/trainer/tad_trainer.py index 14ad9cd0b..67693934d 100644 --- a/pyabsa/tasks/TextAdversarialDefense/trainer/tad_trainer.py +++ b/pyabsa/tasks/TextAdversarialDefense/trainer/tad_trainer.py @@ -23,14 +23,14 @@ class TADTrainer(Trainer): def __init__( - self, - config: TADConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: TADConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/TextClassification/instructor/classifier_instructor.py b/pyabsa/tasks/TextClassification/instructor/classifier_instructor.py index b5d9beed8..cf47a2e38 100644 --- a/pyabsa/tasks/TextClassification/instructor/classifier_instructor.py +++ b/pyabsa/tasks/TextClassification/instructor/classifier_instructor.py @@ -273,8 +273,8 @@ def _train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -392,7 +392,7 @@ def _k_fold_train_and_evaluate(self, criterion): self.config.max_test_metrics = {"max_test_acc": 0, "max_test_f1": 0} for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -487,7 +487,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -506,8 +506,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -530,8 +530,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) iterator.set_postfix_str(postfix) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, @@ -668,7 +668,8 @@ def _evaluate_acc_f1(self, test_dataloader): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -683,7 +684,9 @@ def _evaluate_acc_f1(self, test_dataloader): t_targets_all.cpu(), torch.argmax(t_outputs_all.cpu(), -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( diff --git a/pyabsa/tasks/TextClassification/prediction/text_classifier.py b/pyabsa/tasks/TextClassification/prediction/text_classifier.py index f030927c4..599b3af4f 100644 --- a/pyabsa/tasks/TextClassification/prediction/text_classifier.py +++ b/pyabsa/tasks/TextClassification/prediction/text_classifier.py @@ -128,7 +128,7 @@ def __init__(self, checkpoint=None, cal_perplexity=False, **kwargs): ) if not hasattr( - GloVeTCModelList, self.config.model.__name__ + GloVeTCModelList, self.config.model.__name__ ) and not hasattr(BERTTCModelList, self.config.model.__name__): raise KeyError( "The checkpoint and PyABSA you are loading is not from classifier model." @@ -164,13 +164,13 @@ def _log_write_args(self): fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) def batch_infer( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - defense: str = None, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + defense: str = None, + **kwargs ): """ Batch predicts the sentiment of a target file using the model. @@ -192,12 +192,12 @@ def batch_infer( ) def infer( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - defense: str = None, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + defense: str = None, + **kwargs ): """ Predicts the sentiment of a text using the model. @@ -217,12 +217,12 @@ def infer( ) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict from a file of sentences. @@ -259,11 +259,11 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict from a sentence or a list of sentences. @@ -347,8 +347,8 @@ def _run_prediction(self, save_path=None, print_result=True): else: real_sent = "N.A." if ( - real_sent != LabelPaddingOption.LABEL_PADDING - and real_sent != str(LabelPaddingOption.LABEL_PADDING) + real_sent != LabelPaddingOption.LABEL_PADDING + and real_sent != str(LabelPaddingOption.LABEL_PADDING) ): n_labeled += 1 @@ -440,7 +440,8 @@ def _run_prediction(self, save_path=None, print_result=True): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -455,7 +456,9 @@ def _run_prediction(self, save_path=None, print_result=True): t_targets_all, np.argmax(t_outputs_all, -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( diff --git a/pyabsa/tasks/TextClassification/trainer/tc_trainer.py b/pyabsa/tasks/TextClassification/trainer/tc_trainer.py index 8e59a6daa..4197a969a 100644 --- a/pyabsa/tasks/TextClassification/trainer/tc_trainer.py +++ b/pyabsa/tasks/TextClassification/trainer/tc_trainer.py @@ -23,14 +23,14 @@ class TCTrainer(Trainer): def __init__( - self, - config: TCConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: TCConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/__init__.py b/pyabsa/tasks/UniversalSentimentAnalysis/__init__.py new file mode 100644 index 000000000..5f847161a --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/__init__.py @@ -0,0 +1,16 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 23:02 2023/3/13 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. + +from .configuration.configuration import USAConfigManager +from .dataset_utils.data_utils_for_training import USATrainingDataset +from .dataset_utils.dataset_list import USADatasetList +from .instructor.instructor import USATrainingInstructor +from .models import USAModelList +from .prediction.predictor import USAPredictor +from .trainer.trainer import USATrainer diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/configuration/__init__.py b/pyabsa/tasks/UniversalSentimentAnalysis/configuration/__init__.py new file mode 100644 index 000000000..7f85acc8a --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/configuration/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 13:52 06/12/2023 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/configuration/configuration.py b/pyabsa/tasks/UniversalSentimentAnalysis/configuration/configuration.py new file mode 100644 index 000000000..ce128a83c --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/configuration/configuration.py @@ -0,0 +1,315 @@ +# -*- coding: utf-8 -*- +# file: usa_configuration.py +# time: 02/11/2022 19:55 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + + +import copy + +from pyabsa.framework.configuration_class.configuration_template import ConfigManager +from pyabsa.tasks.UniversalSentimentAnalysis.models.model import GenerationModel + +# if you find the optimal param set of some situation, e.g., some model on some datasets +# please share the main use template main +_usa_config_template = { + "model": GenerationModel, + "task": "triplet", + "optimizer": "", + "learning_rate": 1e-3, + "cache_dataset": True, + "warmup_step": -1, + "deep_ensemble": False, + "use_bert_spc": True, + "max_seq_len": 120, + "patience": 99999, + "sigma": 0.3, + "dropout": 0, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "seed": 52, + "output_dim": 3, + "log_step": 10, + "dynamic_truncate": True, + "srd_alignment": True, # for srd_alignment + "evaluate_begin": 0, + "similarity_threshold": 1, # disable same text check for different examples + "cross_validate_fold": -1, + "use_amp": False, + "overwrite_cache": False, + "epochs": 100, + "adam_epsilon": 1e-8, + "weight_decay": 0.0, + "emb_dropout": 0.5, + "num_layers": 1, + "pooling": "avg", + "gcn_dim": 300, + "relation_constraint": True, + "symmetry_decoding": False, +} + +_usa_config_base = { + "model": GenerationModel, + "optimizer": "adamw", + "learning_rate": 0.00002, + "pretrained_bert": "yangheng/deberta-v3-base-absa-v1.1", + "cache_dataset": True, + "warmup_step": -1, + "deep_ensemble": False, + "patience": 5, + "use_bert_spc": True, + "max_seq_len": 80, + "SRD": 3, + "dlcf_a": 2, # the a in dlcf_dca_bert + "dca_p": 1, # the p in dlcf_dca_bert + "dca_layer": 3, # the layer in dlcf_dca_bert + "use_syntax_based_SRD": False, + "sigma": 0.3, + "lcf": "cdw", + "lsa": False, + "window": "lr", + "eta": 1, + "eta_lr": 0.1, + "dropout": 0, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 10, + "dynamic_truncate": True, + "srd_alignment": True, # for srd_alignment + "evaluate_begin": 0, + "similarity_threshold": 1, # disable same text check for different examples + "cross_validate_fold": -1, # split train and test datasets into 5 folds and repeat 3 trainer + "overwrite_cache": False, +} + +_usa_config_english = { + "model": GenerationModel, + "optimizer": "adamw", + "learning_rate": 0.00002, + "pretrained_bert": "yangheng/deberta-v3-base-absa-v1.1", + "cache_dataset": True, + "warmup_step": -1, + "deep_ensemble": False, + "patience": 99999, + "use_bert_spc": True, + "max_seq_len": 80, + "SRD": 3, + "dlcf_a": 2, # the a in dlcf_dca_bert + "dca_p": 1, # the p in dlcf_dca_bert + "dca_layer": 3, # the layer in dlcf_dca_bert + "use_syntax_based_SRD": False, + "sigma": 0.3, + "lcf": "cdw", + "lsa": False, + "window": "lr", + "eta": 1, + "eta_lr": 0.1, + "dropout": 0.5, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 5, + "dynamic_truncate": True, + "srd_alignment": True, # for srd_alignment + "evaluate_begin": 0, + "similarity_threshold": 1, # disable same text check for different examples + "cross_validate_fold": -1, # split train and test datasets into 5 folds and repeat 3 trainer +} + +_usa_config_multilingual = { + "model": GenerationModel, + "optimizer": "adamw", + "learning_rate": 0.00002, + "pretrained_bert": "microsoft/mdeberta-v3-base", + "use_bert_spc": True, + "cache_dataset": True, + "warmup_step": -1, + "deep_ensemble": False, + "patience": 99999, + "max_seq_len": 80, + "SRD": 3, + "dlcf_a": 2, # the a in dlcf_dca_bert + "dca_p": 1, # the p in dlcf_dca_bert + "dca_layer": 3, # the layer in dlcf_dca_bert + "use_syntax_based_SRD": False, + "sigma": 0.3, + "lcf": "cdw", + "lsa": False, + "window": "lr", + "eta": 1, + "eta_lr": 0.1, + "dropout": 0.5, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 5, + "dynamic_truncate": True, + "srd_alignment": True, # for srd_alignment + "evaluate_begin": 0, + "similarity_threshold": 1, # disable same text check for different examples + "cross_validate_fold": -1 + # split train and test datasets into 5 folds and repeat 3 trainer +} + +_usa_config_chinese = { + "model": GenerationModel, + "optimizer": "adamw", + "learning_rate": 0.00002, + "pretrained_bert": "bert-base-chinese", + "use_bert_spc": True, + "cache_dataset": True, + "warmup_step": -1, + "deep_ensemble": False, + "patience": 99999, + "max_seq_len": 80, + "SRD": 3, + "dlcf_a": 2, # the a in dlcf_dca_bert + "dca_p": 1, # the p in dlcf_dca_bert + "dca_layer": 3, # the layer in dlcf_dca_bert + "use_syntax_based_SRD": False, + "sigma": 0.3, + "lcf": "cdw", + "lsa": False, + "window": "lr", + "eta": 1, + "eta_lr": 0.1, + "dropout": 0.5, + "l2reg": 0.00001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 5, + "dynamic_truncate": True, + "srd_alignment": True, # for srd_alignment + "evaluate_begin": 0, + "similarity_threshold": 1, # disable same text check for different examples + "cross_validate_fold": -1, # split train and test datasets into 5 folds and repeat 3 trainer +} + + +class USAConfigManager(ConfigManager): + def __init__(self, args, **kwargs): + """ + Available Params: {'model': None, + 'optimizer': "", + 'learning_rate': 0.00002, + 'pretrained_bert': "yangheng/deberta-v3-base-absa-v1.1", + 'cache_dataset': True, + 'warmup_step': -1, + 'deep_ensemble': False, + 'patience': 99999, + 'use_bert_spc': True, + 'max_seq_len': 80, + 'SRD': 3, + 'lsa': False, + 'dlcf_a': 2, # the a in dlcf_dca_bert + 'dca_p': 1, # the p in dlcf_dca_bert + 'dca_layer': 3, # the layer in dlcf_dca_bert + 'use_syntax_based_SRD': False, + 'sigma': 0.3, + 'lcf': "cdw", + 'window': "lr", + 'eta': 1, + 'eta_lr': 0.1, + 'dropout': 0, + 'l2reg': 0.000001, + 'num_epoch': 10, + 'batch_size': 16, + 'initializer': 'xavier_uniform_', + 'seed': {52, 214} + 'output_dim': 3, + 'log_step': 10, + 'dynamic_truncate': True, + 'srd_alignment': True, # for srd_alignment + 'evaluate_begin': 0, + 'similarity_threshold': 1, # disable same text check for different examples + 'cross_validate_fold': -1 # split train and test datasets into 5 folds and repeat 3 trainer + } + :param args: + :param kwargs: + """ + super().__init__(args, **kwargs) + + @staticmethod + def set_usa_config(configType: str, newitem: dict): + if isinstance(newitem, dict): + if configType == "template": + _usa_config_template.update(newitem) + elif configType == "base": + _usa_config_base.update(newitem) + elif configType == "english": + _usa_config_english.update(newitem) + elif configType == "chinese": + _usa_config_chinese.update(newitem) + elif configType == "multilingual": + _usa_config_multilingual.update(newitem) + + else: + raise ValueError( + "Wrong value of configuration_class type supplied, please use one from following type: template, base, english, chinese, multilingual, glove, bert_baseline" + ) + else: + raise TypeError( + "Wrong type of new configuration_class item supplied, please use dict e.g.{'NewConfig': NewValue}" + ) + + @staticmethod + def set_usa_config_template(newitem): + USAConfigManager.set_usa_config("template", newitem) + + @staticmethod + def set_usa_config_base(newitem): + USAConfigManager.set_usa_config("base", newitem) + + @staticmethod + def set_usa_config_english(newitem): + USAConfigManager.set_usa_config("english", newitem) + + @staticmethod + def set_usa_config_chinese(newitem): + USAConfigManager.set_usa_config("chinese", newitem) + + @staticmethod + def set_usa_config_multilingual(newitem): + USAConfigManager.set_usa_config("multilingual", newitem) + + @staticmethod + def get_usa_config_template(): + _usa_config_template.update(_usa_config_template) + return USAConfigManager(copy.deepcopy(_usa_config_template)) + + @staticmethod + def get_usa_config_base(): + _usa_config_template.update(_usa_config_base) + return USAConfigManager(copy.deepcopy(_usa_config_template)) + + @staticmethod + def get_usa_config_english(): + _usa_config_template.update(_usa_config_english) + return USAConfigManager(copy.deepcopy(_usa_config_template)) + + @staticmethod + def get_usa_config_chinese(): + _usa_config_template.update(_usa_config_chinese) + return USAConfigManager(copy.deepcopy(_usa_config_template)) + + @staticmethod + def get_usa_config_multilingual(): + _usa_config_template.update(_usa_config_multilingual) + return USAConfigManager(copy.deepcopy(_usa_config_template)) diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/__init__.py b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/__init__.py new file mode 100644 index 000000000..9c80da596 --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 13:51 06/12/2023 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/data_utils_for_inference.py b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/data_utils_for_inference.py new file mode 100644 index 000000000..7d3ef5b67 --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/data_utils_for_inference.py @@ -0,0 +1,84 @@ +# -*- coding: utf-8 -*- +# file: data_utils.py +# time: 15/03/2023 +# author: HENG YANG +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2021. All Rights Reserved. + +import json +import os + +import pandas as pd + + +class USAInferenceDataset: + def prepare_infer_dataset(self, text, **kwargs): + from datasets import Dataset, DatasetDict + + if isinstance(text, str) and os.path.exists(text): + text = read_json(text) + + elif not isinstance(text, list): + text = [text] + + for i, t in enumerate(text): + try: + text[i] = json.loads(t) + except: + pass + + all_data = [] + usa_instructor = self.config.usa_instructor + for t in text: + try: + instructed_input, labels = usa_instructor.encode_input( + t, + ) + all_data.append({"text": instructed_input, "labels": labels}) + except Exception as e: + print(e) + if kwargs.get("ignore_error", False): + continue + else: + raise RuntimeError("Fail to encode the input text: {}".format(t)) + + huggingface_dataset = DatasetDict( + {self.dataset_type: Dataset.from_pandas(pd.DataFrame(all_data))} + ) + huggingface_dataset = huggingface_dataset.map( + self.tokenize_function_inputs, batched=True + ) + self.tokenized_dataset = huggingface_dataset + return huggingface_dataset + + def __init__(self, config, tokenizer, dataset_type="test", **kwargs): + self.config = config + self.tokenizer = tokenizer + self.dataset_type = dataset_type + self.tokenized_dataset = None + + def tokenize_function_inputs(self, sample): + """ + Udf to tokenize the input dataset. + """ + model_inputs = self.tokenizer( + sample["text"], max_length=self.config.max_seq_len, truncation=True + ) + labels = self.tokenizer( + sample["labels"], max_length=self.config.max_seq_len, truncation=True + ) + model_inputs["labels"] = labels["input_ids"] + return model_inputs + + +def read_json(data_path): + data = [] + + for f in data_path: + print(f) + with open(f, "r", encoding="utf8") as fin: + for line in fin: + data.append(json.loads(line)) + return data diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/data_utils_for_training.py b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/data_utils_for_training.py new file mode 100644 index 000000000..3239f04ec --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/data_utils_for_training.py @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- +# file: data_utils.py +# time: 15/03/2023 +# author: HENG YANG +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2021. All Rights Reserved. + +import json +import random + +import pandas as pd +import tqdm + +from .instruction import USAInstruction + + +class USATrainingDataset: + def load_data_from_dict(self, dataset_dict, **kwargs): + pass + + def load_data_from_file(self, dataset_file, **kwargs): + from datasets import Dataset, DatasetDict + + instances = read_json(dataset_file[self.dataset_type]) + usa_instructor = USAInstruction() + self.config.usa_instructor = usa_instructor + all_data = [] + + for i in tqdm.tqdm(range(len(instances)), desc="preparing dataloader"): + instructed_input, labels = usa_instructor.encode_input( + instances[i], + examples=[random.choice(instances), random.choice(instances)], + ) + all_data.append({"text": instructed_input, "labels": labels}) + + # with open("usa_dataset.json", "w") as f: + # new_all_data = [] + # for i in range(len(instances)): + # new_all_data.append( + # { + # "instruction": all_data[i]["text"], + # "input": "", + # "output": "{" + # + '"text": "{}", "labels": "{}"'.format( + # instances[i]["text"], instances[i]["labels"] + # ) + # + "}", + # } + # ) + # json.dump(new_all_data, f, indent=4, sort_keys=True) + + huggingface_dataset = DatasetDict( + {self.dataset_type: Dataset.from_pandas(pd.DataFrame(all_data))} + ) + huggingface_dataset = huggingface_dataset.map( + self.tokenize_function_inputs, batched=True + ) + return huggingface_dataset + + def __init__(self, config, tokenizer, dataset_type="train", **kwargs): + self.config = config + self.tokenizer = tokenizer + self.dataset_type = dataset_type + self.tokenized_dataset = self.load_data_from_file(config.dataset_file, **kwargs) + + def tokenize_function_inputs(self, sample): + """ + Udf to tokenize the input dataset. + """ + model_inputs = self.tokenizer( + sample["text"], max_length=self.config.max_seq_len, truncation=True + ) + labels = self.tokenizer( + sample["labels"], max_length=self.config.max_seq_len, truncation=True + ) + model_inputs["labels"] = labels["input_ids"] + return model_inputs + + +def read_json(data_path): + data = [] + + for f in data_path: + print(f) + with open(f, "r", encoding="utf8") as fin: + for line in fin: + data.append(json.loads(line)) + return data diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/dataset_list.py b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/dataset_list.py new file mode 100644 index 000000000..d59074f89 --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/dataset_list.py @@ -0,0 +1,48 @@ +# -*- coding: utf-8 -*- +# file: dataset_list.py +# time: 02/11/2022 19:35 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +from pyabsa.utils.data_utils.dataset_item import DatasetItem + + +class USADatasetList(list): + """ + The following datasets are for aspect polarity classification task. + The datasets are collected from different sources, you can use the id to locate the dataset. + """ + + Laptop14 = DatasetItem("Laptop14", "501.Laptop14") + + Restaurant14 = DatasetItem("Restaurant14", "502.Restaurant14") + Restaurant15 = DatasetItem("Restaurant15", "503.Restaurant15") + Restaurant16 = DatasetItem("Restaurant16", "504.Restaurant16") + + Chinese_Zhang = DatasetItem("Chinese_Zhang", "505.Chinese_Zhang") + + Multilingual = DatasetItem( + "Multilingual", + [ + "501.Laptop14", + "502.Restaurant14", + "503.Restaurant15", + "504.Restaurant16", + ], + ) + Synthetic = DatasetItem("Synthetic", "506.Synthetic") + + def __init__(self): + super(USADatasetList, self).__init__( + [ + self.Laptop14, + self.Restaurant14, + self.Restaurant15, + self.Restaurant16, + # self.Chinese_Zhang, + # self.Synthetic, + ] + ) diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/instruction.py b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/instruction.py new file mode 100644 index 000000000..cbfb1ac76 --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/dataset_utils/instruction.py @@ -0,0 +1,83 @@ +# -*- coding: utf-8 -*- +# file: instruction.py +# time: 15/03/2023 +# author: HENG YANG +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2021. All Rights Reserved. +import random + + +# Universal Sentiment Analysis Instruction +class USAInstruction: + def __init__(self, bos_instruction=None, eos_instruction=None): + super().__init__() + self.bos_instruction = bos_instruction + self.eos_instruction = eos_instruction + + if self.bos_instruction is None: + self.bos_instruction = """ +For sentiment analysis, I will provide you with a series of texts and their potential corresponding inputs. +Your task is to predict the sentiment analysis output for each text, following the same output format as the provided examples. +""" + + if self.eos_instruction is None: + self.eos_instruction = "\nlet us analyse the sentiments like the examples, and your answer should be as short as you can: \n" + + if not self.bos_instruction: + self.bos_instruction = bos_instruction + if not self.eos_instruction: + self.eos_instruction = eos_instruction + + def prepare_examples(self, examples, cols_idxs=None): + str_examples = "" + + for example in examples: + str_examples += f"Example:\n" + str_examples += f"text: {str(example['text'])}\n" + str_examples += f"->{str(example['text'])}\n" if "text" in example else "" + + # str_examples += f"{'|'.join(','.join(numpy.array([f'{k}:{v}' for k, v in d.items()])[cols_idxs]) for d in example['labels'])}\n" + str_examples += str(example["labels"]) + "\n" + + return str_examples + + def encode_input(self, instance, examples=None, cols_idxs=None): + _examples = """ +Example: +text: I had the best ravioli ever . +->[{'aspect': 'ravioli', 'opinion': 'best', 'polarity': 'positive', 'category': 'NULL'}] +Example: +text: Grilled whole fish wonderful , great spicing . +->[{'aspect': 'fish', 'opinion': 'wonderful', 'polarity': 'positive', 'category': 'NULL'}, {'aspect': 'fish', 'opinion': 'great', 'polarity': 'positive', 'category': 'NULL'}] +""" + if not examples: + examples = [] + if not cols_idxs: + cols_idxs = random.choices(range(4), k=2) + str_text = "text: " + str_input = "input: " if "input" in instance else "" + str_label = "" + + assert isinstance(instance, dict) + assert "text" in instance + assert "labels" in instance + + str_text += f"{str(instance['text'])}\n" + str_input += f"{str(instance['text'])}\n" if "text" in instance else "" + + if not isinstance(instance["labels"], list): + instance["labels"] = [instance["labels"]] + + # str_label += f"{'|'.join(','.join([f'{k}:{v}' for k, v in d.items()]) for d in instance['labels'])}\n" + str_label += str(instance["labels"]) + "\n" + + return ( + self.bos_instruction + + (self.prepare_examples(examples, cols_idxs) if examples else _examples) + + str_text + + self.eos_instruction + + str_input + + "->" + ), str_label diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/instructor/__init__.py b/pyabsa/tasks/UniversalSentimentAnalysis/instructor/__init__.py new file mode 100644 index 000000000..7f85acc8a --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/instructor/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 13:52 06/12/2023 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/instructor/instructor.py b/pyabsa/tasks/UniversalSentimentAnalysis/instructor/instructor.py new file mode 100644 index 000000000..87e47846f --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/instructor/instructor.py @@ -0,0 +1,65 @@ +# -*- coding: utf-8 -*- +# file: apc_instructor.py +# time: 2021/4/22 0022 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# Copyright (C) 2021. All Rights Reserved. +import os +import pickle + +from transformers import AutoTokenizer, DataCollatorForSeq2Seq + +from pyabsa.framework.instructor_class.instructor_template import BaseTrainingInstructor +from pyabsa.tasks.UniversalSentimentAnalysis.dataset_utils.data_utils_for_training import ( + USATrainingDataset, +) +from pyabsa.utils.pyabsa_utils import fprint, print_args + + +class USATrainingInstructor(BaseTrainingInstructor): + def _load_dataset_and_prepare_dataloader(self): + cache_path = self.load_cache_dataset() + + # init BERT-based model and dataset + self.tokenizer = AutoTokenizer.from_pretrained(self.config.pretrained_bert) + self.data_collator = DataCollatorForSeq2Seq(self.tokenizer) + self.config.tokenizer = self.tokenizer + + if not os.path.exists(cache_path) or self.config.overwrite_cache: + self.train_set = USATrainingDataset( + self.config, self.tokenizer, dataset_type="train" + ).tokenized_dataset + self.test_set = USATrainingDataset( + self.config, self.tokenizer, dataset_type="test" + ).tokenized_dataset + self.valid_set = USATrainingDataset( + self.config, self.tokenizer, dataset_type="valid" + ).tokenized_dataset + + self.save_cache_dataset(cache_path) + else: + fprint("Loading dataset from cache file: %s" % cache_path) + with open(cache_path, "rb") as cache_path: + ( + self.train_set, + self.test_set, + self.valid_set, + self.config, + ) = pickle.load(cache_path) + # merge train datasets using datasets.DatasetDict + self.datasets = { + "train": self.train_set["train"], + "test": self.test_set["test"], + "valid": self.valid_set["valid"], + } + self.model = self.config.model(config=self.config) + + def __init__(self, config): + super().__init__(config) + + self._load_dataset_and_prepare_dataloader() + + print_args(self.config) + + def run(self): + return self.model.train(self.datasets) diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/models/__init__.py b/pyabsa/tasks/UniversalSentimentAnalysis/models/__init__.py new file mode 100644 index 000000000..e0bf5674e --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/models/__init__.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 13:52 06/12/2023 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. + + +class USAModelList(list): + from .model import GenerationModel + + GenerationModel = GenerationModel + + def __init__(self): + super(USAModelList, self).__init__([self.GenerationModel]) diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/models/model.py b/pyabsa/tasks/UniversalSentimentAnalysis/models/model.py new file mode 100644 index 000000000..9cfbf4761 --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/models/model.py @@ -0,0 +1,189 @@ +import os + +import autocuda +import torch +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score +from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import DataLoader +from tqdm import tqdm +from transformers import ( + DataCollatorForSeq2Seq, + AutoTokenizer, + AutoModelForSeq2SeqLM, + Seq2SeqTrainingArguments, + Seq2SeqTrainer, +) + +from pyabsa.framework.checkpoint_class.checkpoint_template import CheckpointManager +from pyabsa.utils.file_utils.file_utils import save_model + + +class CustomSeq2SeqTrainer(Seq2SeqTrainer): + tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base") + + def compute_loss(self, model, inputs, return_outputs=False): + labels = inputs.get("labels") + outputs = model(**inputs) + logits = outputs.get("logits") + + # Customizing the loss function + loss_fct = torch.nn.CrossEntropyLoss(reduction="none") + loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1)) + + # Assign higher weight to EOS tokens + eos_token_id = self.tokenizer.eos_token_id + eos_weight = 1.0 # Adjust this based on your requirements + weights = torch.ones_like(labels, dtype=torch.float) + weights[labels == eos_token_id] = eos_weight + + weighted_loss = (loss * weights.view(-1)).mean() + + return (weighted_loss, outputs) if return_outputs else weighted_loss + + +class GenerationModel: + def __init__(self, config): + self.config = config + try: + checkpoint = CheckpointManager().parse_checkpoint( + self.config.pretrained_bert, "USA" + ) + self.model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint) + except Exception as e: + print(e) + self.model = AutoModelForSeq2SeqLM.from_pretrained( + self.config.pretrained_bert + ) + self.tokenizer = AutoTokenizer.from_pretrained(self.config.pretrained_bert) + self.data_collator = DataCollatorForSeq2Seq(self.config.tokenizer) + self.device = autocuda.auto_cuda() + self.model.to(self.device) + + def train(self, tokenized_datasets, **kwargs): + """ + Train the generative model. + """ + # Set training arguments + kwargs["output_dir"] = os.path.join( + self.config.model_path_to_save, "transformers_usa_model/" + ) + # Training arguments + kwargs.update( + { + "evaluation_strategy": "epoch", + "save_strategy": "epoch", + "learning_rate": 5e-5, + "per_device_train_batch_size": 8, + "per_device_eval_batch_size": 8, + "num_train_epochs": 3, + "weight_decay": 0.01, + "warmup_ratio": 0.1, + "load_best_model_at_end": True, + "push_to_hub": False, + "eval_accumulation_steps": 1, + "predict_with_generate": True, + "logging_steps": 1000000000, + "use_mps_device": False, + # 'fp16': True, + "fp16": False, + } + ) + args = Seq2SeqTrainingArguments(**kwargs) + + # Define trainer object + trainer = CustomSeq2SeqTrainer( + self.model, + args, + train_dataset=tokenized_datasets["train"], + eval_dataset=tokenized_datasets["test"] + if tokenized_datasets.get("test") is not None + else None, + tokenizer=self.config.tokenizer, + data_collator=self.data_collator, + ) + print("Trainer device:", trainer.args.device) + + # Finetune the model + torch.cuda.empty_cache() + print("\nModel training started ....") + trainer.train() + + if self.config.model_path_to_save: + if not os.path.exists(self.config.model_path_to_save): + os.makedirs(self.config.model_path_to_save) + save_path = os.path.join( + self.config.model_path_to_save, "pyabsa_usa_model/" + ) + save_model(self.config, self.model, self.tokenizer, save_path) + # Save best model + trainer.save_model() + return self.config.model_path_to_save + + def evaluate( + self, + tokenized_dataset, + trained_model_path=None, + predictor=None, + batch_size=4, + dataset_type="train", + ): + """ + Get the predictions from the trained model. + """ + if not predictor: + print("Prediction from checkpoint") + + def collate_fn(batch): + input_ids = [torch.tensor(example["input_ids"]) for example in batch] + input_ids = pad_sequence( + input_ids, + batch_first=True, + padding_value=self.tokenizer.pad_token_id, + ) + return input_ids + + dataloader = DataLoader( + tokenized_dataset[dataset_type], + batch_size=batch_size, + collate_fn=collate_fn, + ) + predicted_output = [] + self.model.to(self.device) + print("Model loaded to: ", self.device) + + for batch in tqdm(dataloader): + batch = batch.to(self.device) + output_ids = self.model.generate(batch) + output_texts = self.tokenizer.batch_decode( + output_ids, skip_special_tokens=True + ) + for output_text in output_texts: + predicted_output.append(output_text) + else: + print("Prediction from trainer") + output_ids = predictor.predict( + test_dataset=tokenized_dataset[dataset_type] + ).predictions + predicted_output = self.tokenizer.batch_decode( + output_ids, skip_special_tokens=True + ) + return predicted_output + + def get_aspect_metrics(self, true_aspects, pred_aspects): + aspect_p = precision_score(true_aspects, pred_aspects, average="macro") + aspect_r = recall_score(true_aspects, pred_aspects, average="macro") + aspect_f1 = f1_score(true_aspects, pred_aspects, average="macro") + return aspect_p, aspect_r, aspect_f1 + + def get_classic_metrics(self, y_true, y_pred): + for i in range(len(y_true)): + y_true[i] = y_true[i].replace(" ", "") + y_pred[i] = y_pred[i].replace(" ", "") + print(y_true[i]) + print(y_pred[i]) + return { + "accuracy": accuracy_score(y_true, y_pred), + "precision": precision_score(y_true, y_pred, average="macro"), + "recall": recall_score(y_true, y_pred, average="macro"), + "f1": f1_score(y_true, y_pred, average="macro"), + } diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/prediction/__init__.py b/pyabsa/tasks/UniversalSentimentAnalysis/prediction/__init__.py new file mode 100644 index 000000000..7f85acc8a --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/prediction/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 13:52 06/12/2023 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/prediction/predictor.py b/pyabsa/tasks/UniversalSentimentAnalysis/prediction/predictor.py new file mode 100644 index 000000000..496d87c2e --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/prediction/predictor.py @@ -0,0 +1,231 @@ +# -*- coding: utf-8 -*- +# file: sentiment_classifier.py +# author: YANG, HENG (杨恒) +# Copyright (C) 2020. All Rights Reserved. +import os +import pickle +from typing import Union + +import torch +from findfile import find_file +from torch.nn.utils.rnn import pad_sequence +from torch.utils.data import DataLoader +from tqdm import tqdm + +from pyabsa.framework.flag_class import TaskCodeOption +from pyabsa.framework.prediction_class.predictor_template import InferenceModel +from pyabsa.tasks.UniversalSentimentAnalysis.dataset_utils.data_utils_for_inference import ( + USAInferenceDataset, +) +from pyabsa.utils.data_utils.dataset_manager import detect_infer_dataset +from pyabsa.utils.pyabsa_utils import fprint, set_device + + +class USAPredictor(InferenceModel): + task_code = TaskCodeOption.Universal_Sentiment_Analysis + + def __init__(self, checkpoint=None, **kwargs): + super().__init__(checkpoint, task_code=self.task_code, **kwargs) + + # load from a trainer + if self.checkpoint and isinstance(self.checkpoint, str): + fprint("Load sentiment classifier from trainer") + try: + fprint("Load text classifier from", self.checkpoint) + state_dict_path = find_file( + self.checkpoint, key=".state_dict", exclude_key=["__MACOSX"] + ) + model_path = find_file( + self.checkpoint, key=".model", exclude_key=["__MACOSX"] + ) + tokenizer_path = find_file( + self.checkpoint, key=".tokenizer", exclude_key=["__MACOSX"] + ) + config_path = find_file( + self.checkpoint, key=".config", exclude_key=["__MACOSX"] + ) + + fprint("config: {}".format(config_path)) + fprint("state_dict: {}".format(state_dict_path)) + fprint("model: {}".format(model_path)) + fprint("tokenizer: {}".format(tokenizer_path)) + + with open(config_path, mode="rb") as f: + self.config = pickle.load(f) + self.config.auto_device = kwargs.get("auto_device", True) + set_device(self.config, self.config.auto_device) + + self.model = self.config.model(config=self.config) + self.model.model.load_state_dict( + torch.load(state_dict_path, map_location="cpu"), strict=False + ) + self.model.model.to(self.config.device) + + except Exception as e: + raise RuntimeError( + "Fail to load the model from {}! " + "Please make sure the version of checkpoint and PyABSA are compatible." + " Try to remove he checkpoint and download again" + " \nException: {} ".format(checkpoint, e) + ) + + self.dataset = USAInferenceDataset( + self.config, self.config.tokenizer, dataset_type="test" + ) + + def batch_infer( + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs + ): + """ + A deprecated version of batch_predict method. + + Args: + target_file (str): the path to the target file for inference + print_result (bool): whether to print the result + save_result (bool): whether to save the result + ignore_error (bool): whether to ignore the error + + Returns: + result (dict): a dictionary of the results + """ + return self.batch_predict( + target_file=target_file, + print_result=print_result, + save_result=save_result, + ignore_error=ignore_error, + **kwargs + ) + + def infer(self, text: str = None, print_result=True, ignore_error=True, **kwargs): + """ + A deprecated version of the predict method. + + Args: + text (str): the text to predict + print_result (bool): whether to print the result + ignore_error (bool): whether to ignore the error + + Returns: + result (dict): a dictionary of the results + """ + return self.predict( + text=text, print_result=print_result, ignore_error=ignore_error, **kwargs + ) + + def batch_predict( + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs + ): + """ + Predict the sentiment from a file of sentences. + param: target_file: the file path of the sentences to be predicted. + param: print_result: whether to print the result. + param: save_result: whether to save the result. + param: ignore_error: whether to ignore the error when predicting. + param: kwargs: other parameters. + """ + self.config.eval_batch_size = kwargs.get("eval_batch_size", 32) + + save_path = os.path.join( + os.getcwd(), + "{}.{}.result.json".format( + self.config.task_name, self.config.model.__name__ + ), + ) + + target_file = detect_infer_dataset( + target_file, task_code=TaskCodeOption.Aspect_Sentiment_Triplet_Extraction + ) + if not target_file: + raise FileNotFoundError("Can not find inference datasets!") + + self.dataset.prepare_infer_dataset(target_file, ignore_error=ignore_error) + + return self._run_prediction( + save_path=save_path if save_result else None, print_result=print_result + ) + + def predict( + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs + ): + """ + Predict the sentiment from a sentence or a list of sentences. + param: text: the sentence to be predicted. + param: print_result: whether to print the result. + param: ignore_error: whether to ignore the error when predicting. + param: kwargs: other parameters. + """ + self.config.eval_batch_size = kwargs.get("eval_batch_size", 32) + if text: + self.dataset.prepare_infer_dataset(text, ignore_error=ignore_error) + else: + raise RuntimeError("Please specify your datasets path!") + if isinstance(text, str): + try: + return self._run_prediction(print_result=print_result, **kwargs)[0] + except Exception as e: + return { + "text": text, + "output": None, + "error": str(e), + "error_type": "RuntimeError", + } + else: + return self._run_prediction(print_result=print_result, **kwargs) + + def _run_prediction(self, save_path=None, print_result=True, **kwargs): + self.model.model.eval() + all_results = [] + with torch.no_grad(): + + def collate_fn(batch): + input_ids = [torch.tensor(example["input_ids"]) for example in batch] + input_ids = pad_sequence( + input_ids, + batch_first=True, + padding_value=self.config.tokenizer.pad_token_id, + ) + return input_ids + + dataloader = DataLoader( + self.dataset.tokenized_dataset["test"], + batch_size=self.config.batch_size, + collate_fn=collate_fn, + ) + predicted_output = [] + self.model.model.to(self.config.device) + print("Model loaded to: ", self.config.device) + + for batch in tqdm(dataloader): + batch = batch.to(self.config.device) + output_ids = self.model.model.generate(batch) + output_texts = self.config.tokenizer.batch_decode( + output_ids, skip_special_tokens=True + ) + for output_text in output_texts: + predicted_output.append( + # self.config.usa_instructor.decode_input(output_text) + output_text + ) + + return predicted_output + + def clear_input_samples(self): + self.dataset.all_data = [] + + +class Predictor(USAPredictor): + pass diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/trainer/__init__.py b/pyabsa/tasks/UniversalSentimentAnalysis/trainer/__init__.py new file mode 100644 index 000000000..7f85acc8a --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/trainer/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 13:52 06/12/2023 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/trainer/multitask_train.py b/pyabsa/tasks/UniversalSentimentAnalysis/trainer/multitask_train.py new file mode 100644 index 000000000..ce14502f6 --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/trainer/multitask_train.py @@ -0,0 +1,130 @@ +# -*- coding: utf-8 -*- +# file: train.py +# time: 11:30 2023/3/13 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# huggingface: https://huggingface.co/yangheng +# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# Copyright (C) 2019-2023. All Rights Reserved. +import os +import warnings + +warnings.filterwarnings("ignore") +import pandas as pd + + +task_name = "multitask" +experiment_name = "instruction" +# model_checkpoint = 'allenai/tk-instruct-base-def-pos' +model_checkpoint = "kevinscaria/ate_tk-instruct-base-def-pos-neg-neut-combined" +# model_checkpoint = 'allenai/tk-instruct-large-def-pos' +# model_checkpoint = 'allenai/tk-instruct-3b-def-pos' +# model_checkpoint = 'google/mt5-base' + +print("Experiment Name: ", experiment_name) +model_out_path = "checkpoints" +model_out_path = os.path.join( + model_out_path, task_name, f"{model_checkpoint.replace('/', '')}-{experiment_name}" +) +print("Model output path: ", model_out_path) + +# Load the data +# id_train_file_path = './integrated_datasets' +# id_test_file_path = './integrated_datasets' +id_train_file_path = "./integrated_datasets/acos_datasets/" +id_test_file_path = "./integrated_datasets/acos_datasets" +# id_train_file_path = './integrated_datasets/acos_datasets/501.Laptop14' +# id_test_file_path = './integrated_datasets/acos_datasets/501.Laptop14' +# id_train_file_path = './integrated_datasets/acos_datasets/504.Restaurant16' +# id_test_file_path = './integrated_datasets/acos_datasets/504.Restaurant16' + + +id_tr_df = read_json(id_train_file_path, "train") +id_te_df = read_json(id_test_file_path, "test") + +id_tr_df = pd.DataFrame(id_tr_df) +id_te_df = pd.DataFrame(id_te_df) + +loader = InstructDatasetLoader(id_tr_df, id_te_df) + +if loader.train_df_id is not None: + loader.train_df_id = loader.prepare_instruction_dataloader(loader.train_df_id) +if loader.test_df_id is not None: + loader.test_df_id = loader.prepare_instruction_dataloader(loader.test_df_id) +if loader.train_df_ood is not None: + loader.train_df_ood = loader.prepare_instruction_dataloader(loader.train_df_ood) +if loader.test_df_ood is not None: + loader.test_df_ood = loader.prepare_instruction_dataloader(loader.test_df_ood) + +# Create T5 utils object +t5_exp = T5Generator(model_checkpoint) + +# Tokenize Dataset +id_ds, id_tokenized_ds, ood_ds, ood_tokenzed_ds = loader.create_datasets( + t5_exp.tokenize_function_inputs +) + +# Training arguments +training_args = { + "output_dir": model_out_path, + "evaluation_strategy": "epoch", + "save_strategy": "epoch", + "learning_rate": 5e-5, + "per_device_train_batch_size": 16, + "per_device_eval_batch_size": 16, + "num_train_epochs": 6, + "weight_decay": 0.01, + "warmup_ratio": 0.1, + "load_best_model_at_end": True, + "push_to_hub": False, + "eval_accumulation_steps": 1, + "predict_with_generate": True, + "logging_steps": 1000000000, + "use_mps_device": False, + # 'fp16': True, + "fp16": False, +} + +# Train model +model_trainer = t5_exp.train(id_tokenized_ds, **training_args) + +# Model inference - Trainer object - (Pass model trainer as predictor) + +# model_checkpoint = findfile.find_cwd_dir('tk-instruct-base-def-pos') +# t5_exp = T5Generator(model_checkpoint) + +# Get prediction labels - Training set +id_tr_pred_labels = t5_exp.get_labels( + predictor=model_trainer, + tokenized_dataset=id_tokenized_ds, + sample_set="train", + batch_size=16, +) +id_tr_labels = [i.strip() for i in id_ds["train"]["labels"]] + +# Get prediction labels - Testing set +id_te_pred_labels = t5_exp.get_labels( + predictor=model_trainer, + tokenized_dataset=id_tokenized_ds, + sample_set="test", + batch_size=16, +) +id_te_labels = [i.strip() for i in id_ds["test"]["labels"]] + +# # Compute Metrics +# metrics = t5_exp.get_metrics(id_tr_labels, id_tr_pred_labels) +# print('----------------------- Training Set Metrics -----------------------') +# print(metrics) +# +# metrics = t5_exp.get_metrics(id_te_labels, id_te_pred_labels) +# print('----------------------- Testing Set Metrics -----------------------') +# print(metrics) + +# Compute Metrics +metrics = t5_exp.get_classic_metrics(id_tr_labels, id_tr_pred_labels) +print("----------------------- Classic Training Set Metrics -----------------------") +print(metrics) + +metrics = t5_exp.get_classic_metrics(id_te_labels, id_te_pred_labels) +print("----------------------- Classic Testing Set Metrics -----------------------") +print(metrics) diff --git a/pyabsa/tasks/UniversalSentimentAnalysis/trainer/trainer.py b/pyabsa/tasks/UniversalSentimentAnalysis/trainer/trainer.py new file mode 100644 index 000000000..14ba97173 --- /dev/null +++ b/pyabsa/tasks/UniversalSentimentAnalysis/trainer/trainer.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +# file: aste_trainer.py +# time: 02/11/2022 21:34 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +from typing import Union + +from pyabsa.framework.flag_class.flag_template import ( + DeviceTypeOption, + ModelSaveOption, + TaskCodeOption, + TaskNameOption, +) +from pyabsa.framework.trainer_class.trainer_template import Trainer +from ..configuration.configuration import USAConfigManager +from ..instructor.instructor import USATrainingInstructor +from ..prediction.predictor import USAPredictor + + +class USATrainer(Trainer): + def __init__( + self, + config: USAConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, + ): + """ + Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, + you need to call load_trained_model() to get the trained model for inference. + + :param config: PyABSA.config.ConfigManager + :param dataset: Dataset name, or a dataset_manager path, or a list of dataset_manager paths + :param from_checkpoint: A checkpoint path to train based on + :param checkpoint_save_mode: Save trained model to checkpoint, + "checkpoint_save_mode=1" to save the state_dict, + "checkpoint_save_mode=2" to save the whole model, + "checkpoint_save_mode=3" to save the fine-tuned BERT, + otherwise avoid saving checkpoint but return the trained model after trainer + :param auto_device: True or False, otherwise 'allcuda', 'cuda:1', 'cpu' works + :param path_to_save=None: Specify path to save checkpoints + :param load_aug=False: Load the available augmentation dataset if any + + """ + super(USATrainer, self).__init__( + config=config, + dataset=dataset, + from_checkpoint=from_checkpoint, + checkpoint_save_mode=checkpoint_save_mode, + auto_device=auto_device, + path_to_save=path_to_save, + load_aug=load_aug, + ) + + self.training_instructor = USATrainingInstructor + self.inference_model_class = USAPredictor + self.config.task_code = TaskCodeOption.Universal_Sentiment_Analysis + self.config.task_name = TaskNameOption().get(self.config.task_code) + + self._run() diff --git a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_inference.py b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_inference.py index e444fc0c0..19fa3994a 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_inference.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_inference.py @@ -60,10 +60,10 @@ def process_data(self, samples, ignore_error=True): for x in range(len(seq) // (self.config.max_seq_len * 2) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 2): (x + 1) - * (self.config.max_seq_len * 2) - ] + x + * (self.config.max_seq_len * 2) : (x + 1) + * (self.config.max_seq_len * 2) + ] protein_indices = self.tokenizer.text_to_sequence(_seq) protein_indices = pad_and_truncate( protein_indices, diff --git a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_training.py b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_training.py index da1206768..e91c5a8bb 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_training.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__classic__/data_utils_for_training.py @@ -27,7 +27,7 @@ def load_data_from_file(self, dataset_file, **kwargs): all_data = [] for ex_id, i in enumerate( - tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") ): text, _, label = lines[i].partition("$LABEL$") seq, ph = text.split(",") @@ -35,10 +35,10 @@ def load_data_from_file(self, dataset_file, **kwargs): for x in range(len(seq) // (self.config.max_seq_len * 2) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 2): (x + 1) - * (self.config.max_seq_len * 2) - ] + x + * (self.config.max_seq_len * 2) : (x + 1) + * (self.config.max_seq_len * 2) + ] protein_indices = self.tokenizer.text_to_sequence(_seq) protein_indices = pad_and_truncate( protein_indices, diff --git a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_inference.py b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_inference.py index f308a6607..a161f66b1 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_inference.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_inference.py @@ -60,10 +60,10 @@ def process_data(self, samples, ignore_error=True): for x in range(len(seq) // (self.config.max_seq_len * 2) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 2): (x + 1) - * (self.config.max_seq_len * 2) - ] + x + * (self.config.max_seq_len * 2) : (x + 1) + * (self.config.max_seq_len * 2) + ] protein_indices = self.tokenizer.text_to_sequence(_seq) protein_indices = pad_and_truncate( protein_indices, diff --git a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_training.py b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_training.py index 9bdbc59a4..0c550a252 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_training.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/dataset_utils/__plm__/data_utils_for_training.py @@ -27,7 +27,7 @@ def load_data_from_file(self, dataset_file, **kwargs): all_data = [] for ex_id, i in enumerate( - tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") ): text, _, label = lines[i].partition("$LABEL$") seq, ph = text.split(",") @@ -35,10 +35,10 @@ def load_data_from_file(self, dataset_file, **kwargs): for x in range(len(seq) // (self.config.max_seq_len * 2) + 1): _seq = seq[ - x - * (self.config.max_seq_len * 2): (x + 1) - * (self.config.max_seq_len * 2) - ] + x + * (self.config.max_seq_len * 2) : (x + 1) + * (self.config.max_seq_len * 2) + ] protein_indices = self.tokenizer.text_to_sequence(_seq) protein_indices = pad_and_truncate( protein_indices, diff --git a/pyabsa/tasks/_Archive/ProteinRegression/instructor/proteinr_instructor.py b/pyabsa/tasks/_Archive/ProteinRegression/instructor/proteinr_instructor.py index ff8bd5d2f..632b9d223 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/instructor/proteinr_instructor.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/instructor/proteinr_instructor.py @@ -352,8 +352,8 @@ def _train_and_evaluate(self, criterion): ) if ( - test_r2 - < self.config.max_test_metrics["max_test_r2"] + test_r2 + < self.config.max_test_metrics["max_test_r2"] ): self.config.max_test_metrics[ "max_test_r2" @@ -446,7 +446,7 @@ def _k_fold_train_and_evaluate(self, criterion): self.config.max_test_metrics = {"max_test_r2": 0} for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -536,7 +536,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -554,8 +554,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_r2 - < self.config.max_test_metrics["max_test_r2"] + test_r2 + < self.config.max_test_metrics["max_test_r2"] ): self.config.max_test_metrics[ "max_test_r2" @@ -572,8 +572,8 @@ def _k_fold_train_and_evaluate(self, criterion): epoch, loss.item(), test_r2, max_fold_r2 ) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, diff --git a/pyabsa/tasks/_Archive/ProteinRegression/models/__classic__/transformer.py b/pyabsa/tasks/_Archive/ProteinRegression/models/__classic__/transformer.py index fb2728c59..8e48355cf 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/models/__classic__/transformer.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/models/__classic__/transformer.py @@ -29,14 +29,14 @@ def __init__(self, embedding_matrix, config): self.classifier = nn.Linear(self.config.hidden_dim, self.config.output_dim) def forward( - self, - input_ids, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - labels=None, + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + labels=None, ): transformer_outputs = self.transformer( input_ids=input_ids, diff --git a/pyabsa/tasks/_Archive/ProteinRegression/prediction/protein_regressor.py b/pyabsa/tasks/_Archive/ProteinRegression/prediction/protein_regressor.py index f71ef4d5c..110ee89d2 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/prediction/protein_regressor.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/prediction/protein_regressor.py @@ -128,7 +128,7 @@ def __init__(self, checkpoint=None, **kwargs): ) if not hasattr( - GloVeProteinRModelList, self.config.model.__name__ + GloVeProteinRModelList, self.config.model.__name__ ) and not hasattr(BERTProteinRModelList, self.config.model.__name__): raise KeyError( "The checkpoint you are loading is not from classifier model." @@ -164,12 +164,12 @@ def _log_write_args(self): fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict from a file of sentences. @@ -210,11 +210,11 @@ def batch_predict( ) def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict from a sentence or a list of sentences. @@ -356,9 +356,9 @@ def _run_prediction(self, save_path=None, print_result=True): text_printing = result["text"][:] if result["ref_label"] != LabelPaddingOption.LABEL_PADDING: if ( - abs(result["label"] - result["ref_label"]) - / result["ref_label"] - <= 0.2 + abs(result["label"] - result["ref_label"]) + / result["ref_label"] + <= 0.2 ): text_info = colored( "#{}\t -> <{}(ref:{})>\t".format( diff --git a/pyabsa/tasks/_Archive/ProteinRegression/trainer/proteinr_trainer.py b/pyabsa/tasks/_Archive/ProteinRegression/trainer/proteinr_trainer.py index 0935ce425..55749049c 100644 --- a/pyabsa/tasks/_Archive/ProteinRegression/trainer/proteinr_trainer.py +++ b/pyabsa/tasks/_Archive/ProteinRegression/trainer/proteinr_trainer.py @@ -23,14 +23,14 @@ class ProteinRTrainer(Trainer): def __init__( - self, - config: ProteinRConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: ProteinRConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_inference.py b/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_inference.py index 6819ea690..4fb1d640d 100644 --- a/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_inference.py +++ b/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_inference.py @@ -58,9 +58,13 @@ def process_data(self, samples, ignore_error=True): ) # label = label.strip() - # tokens = self.tokenizer.tokenizer.tokenize(text) - tokens = list(text) + tokens = ( + [self.tokenizer.tokenizer.cls_token] + + self.tokenizer.tokenizer.tokenize(text) + + [self.tokenizer.tokenizer.eos_token] + ) rna_indices = self.tokenizer.tokenizer.convert_tokens_to_ids(tokens) + # rna_indices = self.tokenizer.text_to_sequence(rna, padding="do_not_pad") # rna_type_indices = self.tokenizer.text_to_sequence(str(rna_type)) @@ -78,45 +82,6 @@ def process_data(self, samples, ignore_error=True): } all_data.append(data) - # for _ in range(self.config.get("noise_instance_num", 1)): - # import numpy as np - # - # _rna_indices = np.array(rna_indices.copy()) - # noise_masks = np.abs( - # len(_rna_indices) // 2 - # - np.random.normal( - # loc=len(_rna_indices) // 2, - # scale=self.config.max_seq_len // 5, - # size=int(len(_rna_indices) * 0.2), - # ).astype(int) - # ) - # noise_masks = np.where(noise_masks < 0, 0, noise_masks) - # noise_masks = np.where( - # noise_masks > len(_rna_indices) - 1, - # len(_rna_indices) - 1, - # noise_masks, - # ) - # _rna_indices[noise_masks] = self.tokenizer.pad_token_id - # # noise_masks = np.random.choice([0, 1], size=len(_rna_indices), p=[0.2, 0.8]) - # # _rna_indices = np.array(_rna_indices) * ( - # # noise_masks if any(noise_masks) else [1] * len(_rna_indices)) - # # _rna_indices = _rna_indices.tolist() - # - # _rna_indices = pad_and_truncate( - # _rna_indices, - # self.config.max_seq_len, - # value=self.tokenizer.pad_token_id, - # ) - # - # data = { - # "ex_id": ex_id, - # "text_raw": rna, - # "text_indices": _rna_indices, - # # "rna_type": rna_type_indices, - # "label": label, - # } - # all_data.append(data) - self.data = all_data except Exception as e: if ignore_error: diff --git a/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_training.py b/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_training.py index dff7e3c55..74b55f319 100644 --- a/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_training.py +++ b/pyabsa/tasks/_Archive/RNAClassification/dataset_utils/data_utils_for_training.py @@ -21,7 +21,7 @@ def load_data_from_dict(self, dataset_dict, **kwargs): all_data = [] for ex_id, data in enumerate( - tqdm.tqdm(dataset_dict[self.dataset_type], desc="preparing dataloader") + tqdm.tqdm(dataset_dict[self.dataset_type], desc="preparing dataloader") ): rna, label = data["text"], data["label"] rna_indices = self.tokenizer.text_to_sequence(rna) @@ -48,20 +48,18 @@ def load_data_from_file(self, dataset_file, **kwargs): all_data = [] label_set = set() - rna_type_dict = {"cds": 1, "5utr": 2, "3utr": 3} for ex_id, i in enumerate( - tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") ): text, _, label = lines[i].strip().partition("$LABEL$") - # rna, rna_type = text.strip().split(",") - # rna_type = rna_type_dict[rna_type] - # rna_type = rna_type.upper() - # label = label.strip() - tokens = self.tokenizer.tokenizer.tokenize(text) # tokens = list(text) + tokens = ( + [self.tokenizer.tokenizer.cls_token] + + self.tokenizer.tokenizer.tokenize(text) + + [self.tokenizer.tokenizer.eos_token] + ) + # print(tokens) rna_indices = self.tokenizer.tokenizer.convert_tokens_to_ids(tokens) - # rna_indices = self.tokenizer.text_to_sequence(rna, padding="do_not_pad") - # rna_type_indices = self.tokenizer.text_to_sequence(str(rna_type)) data = { "ex_id": ex_id, @@ -70,50 +68,50 @@ def load_data_from_file(self, dataset_file, **kwargs): self.config.max_seq_len, value=self.tokenizer.pad_token_id, ), - # "rna_type": rna_type_indices, "label": label, } label_set.add(label) all_data.append(data) - for _ in range(self.config.get("noise_instance_num", 3)): - import numpy as np - - _rna_indices = np.array(rna_indices.copy()) - noise_masks = np.abs( - len(_rna_indices) // 2 - - np.random.normal( - loc=len(_rna_indices) // 2, - scale=self.config.max_seq_len // 5, - size=int(len(_rna_indices) * 0.2), - ).astype(int) - ) - noise_masks = np.where(noise_masks < 0, 0, noise_masks) - noise_masks = np.where( - noise_masks > len(_rna_indices) - 1, - len(_rna_indices) - 1, - noise_masks, - ) - _rna_indices[noise_masks] = self.tokenizer.pad_token_id - # noise_masks = np.random.choice([0, 1], size=len(_rna_indices), p=[0.2, 0.8]) - # _rna_indices = np.array(_rna_indices) * ( - # noise_masks if any(noise_masks) else [1] * len(_rna_indices)) - # _rna_indices = _rna_indices.tolist() - - _rna_indices = pad_and_truncate( - _rna_indices, - self.config.max_seq_len, - value=self.tokenizer.pad_token_id, - ) - - data = { - "ex_id": ex_id, - "text_indices": _rna_indices, - # "rna_type": rna_type_indices, - "label": label, - } - label_set.add(label) - all_data.append(data) + if self.dataset_type == "train": + for _ in range(self.config.get("noise_instance_num", 3)): + import numpy as np + + _rna_indices = np.array(rna_indices.copy()) + noise_masks = np.abs( + len(_rna_indices) // 2 + - np.random.normal( + loc=len(_rna_indices) // 2, + scale=self.config.max_seq_len // 5, + size=int(len(_rna_indices) * 0.2), + ).astype(int) + ) + noise_masks = np.where(noise_masks < 0, 0, noise_masks) + noise_masks = np.where( + noise_masks > len(_rna_indices) - 1, + len(_rna_indices) - 1, + noise_masks, + ) + _rna_indices[noise_masks] = self.tokenizer.pad_token_id + # noise_masks = np.random.choice([0, 1], size=len(_rna_indices), p=[0.2, 0.8]) + # _rna_indices = np.array(_rna_indices) * ( + # noise_masks if any(noise_masks) else [1] * len(_rna_indices)) + # _rna_indices = _rna_indices.tolist() + + _rna_indices = pad_and_truncate( + _rna_indices, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + padding="left", + ) + + data = { + "ex_id": ex_id, + "text_indices": _rna_indices, + "label": label, + } + label_set.add(label) + all_data.append(data) check_and_fix_labels(label_set, "label", all_data, self.config) self.config.output_dim = len(label_set) diff --git a/pyabsa/tasks/_Archive/RNAClassification/instructor/rnac_instructor.py b/pyabsa/tasks/_Archive/RNAClassification/instructor/rnac_instructor.py index 1f6976769..255119bc5 100644 --- a/pyabsa/tasks/_Archive/RNAClassification/instructor/rnac_instructor.py +++ b/pyabsa/tasks/_Archive/RNAClassification/instructor/rnac_instructor.py @@ -22,17 +22,16 @@ from pyabsa.framework.flag_class.flag_template import DeviceTypeOption from pyabsa.framework.instructor_class.instructor_template import BaseTrainingInstructor -from pyabsa.utils.file_utils.file_utils import save_model -from pyabsa.utils.pyabsa_utils import init_optimizer, fprint, rprint -from ..dataset_utils.data_utils_for_training import GloVeRNACDataset -from ..dataset_utils.data_utils_for_training import BERTRNACDataset -from ..models import GloVeRNACModelList, BERTRNACModelList - from pyabsa.framework.tokenizer_class.tokenizer_class import ( Tokenizer, build_embedding_matrix, PretrainedTokenizer, ) +from pyabsa.utils.file_utils.file_utils import save_model +from pyabsa.utils.pyabsa_utils import init_optimizer, fprint, rprint +from ..dataset_utils.data_utils_for_training import BERTRNACDataset +from ..dataset_utils.data_utils_for_training import GloVeRNACDataset +from ..models import GloVeRNACModelList, BERTRNACModelList class RNACTrainingInstructor(BaseTrainingInstructor): @@ -190,8 +189,8 @@ def _train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -309,7 +308,7 @@ def _k_fold_train_and_evaluate(self, criterion): self.config.max_test_metrics = {"max_test_acc": 0, "max_test_f1": 0} for f, (train_dataloader, valid_dataloader) in enumerate( - zip(self.train_dataloaders, self.valid_dataloaders) + zip(self.train_dataloaders, self.valid_dataloaders) ): patience = self.config.patience + self.config.evaluate_begin if self.config.log_step < 0: @@ -389,8 +388,8 @@ def _k_fold_train_and_evaluate(self, criterion): # evaluate if test set is available if global_step % self.config.log_step == 0: if ( - self.valid_dataloader - and epoch >= self.config.evaluate_begin + self.valid_dataloader + and epoch >= self.config.evaluate_begin ): test_acc, f1 = self._evaluate_acc_f1(valid_dataloader) @@ -407,7 +406,7 @@ def _k_fold_train_and_evaluate(self, criterion): if self.config.model_path_to_save: if not os.path.exists( - self.config.model_path_to_save + self.config.model_path_to_save ): os.makedirs(self.config.model_path_to_save) if save_path: @@ -426,8 +425,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) if ( - test_acc - > self.config.max_test_metrics["max_test_acc"] + test_acc + > self.config.max_test_metrics["max_test_acc"] ): self.config.max_test_metrics[ "max_test_acc" @@ -450,8 +449,8 @@ def _k_fold_train_and_evaluate(self, criterion): ) iterator.set_postfix_str(postfix) if ( - self.config.save_mode - and epoch >= self.config.evaluate_begin + self.config.save_mode + and epoch >= self.config.evaluate_begin ): save_model( self.config, @@ -587,7 +586,8 @@ def _evaluate_acc_f1(self, test_dataloader): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -602,7 +602,9 @@ def _evaluate_acc_f1(self, test_dataloader): t_targets_all.cpu(), torch.argmax(t_outputs_all.cpu(), -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( @@ -633,7 +635,9 @@ def _load_dataset_and_prepare_dataloader(self): self.config, self.tokenizer, dataset_type="valid" ) try: - self.bert = AutoModel.from_pretrained(self.config.pretrained_bert) + self.bert = AutoModel.from_pretrained( + self.config.pretrained_bert, trust_remote_code=True + ) except ValueError as e: fprint("Init pretrained model failed, exception: {}".format(e)) @@ -680,5 +684,4 @@ def _load_dataset_and_prepare_dataloader(self): def run(self): # Loss and Optimizer criterion = nn.CrossEntropyLoss() - return self._train(criterion) diff --git a/pyabsa/tasks/_Archive/RNAClassification/models/__classic__/transformer.py b/pyabsa/tasks/_Archive/RNAClassification/models/__classic__/transformer.py index fb2728c59..8e48355cf 100644 --- a/pyabsa/tasks/_Archive/RNAClassification/models/__classic__/transformer.py +++ b/pyabsa/tasks/_Archive/RNAClassification/models/__classic__/transformer.py @@ -29,14 +29,14 @@ def __init__(self, embedding_matrix, config): self.classifier = nn.Linear(self.config.hidden_dim, self.config.output_dim) def forward( - self, - input_ids, - attention_mask=None, - token_type_ids=None, - position_ids=None, - head_mask=None, - inputs_embeds=None, - labels=None, + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + labels=None, ): transformer_outputs = self.transformer( input_ids=input_ids, diff --git a/pyabsa/tasks/_Archive/RNAClassification/models/__plm__/bert.py b/pyabsa/tasks/_Archive/RNAClassification/models/__plm__/bert.py index f63a40b3e..ac6b6046d 100644 --- a/pyabsa/tasks/_Archive/RNAClassification/models/__plm__/bert.py +++ b/pyabsa/tasks/_Archive/RNAClassification/models/__plm__/bert.py @@ -6,13 +6,12 @@ # GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en # ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research # Copyright (C) 2022. All Rights Reserved. -import torch import torch.nn as nn from transformers.models.bert.modeling_bert import BertPooler class BERT_MLP(nn.Module): - inputs = ["text_indices", "rna_type"] + inputs = ["text_indices"] def __init__(self, bert, config): super(BERT_MLP, self).__init__() @@ -32,15 +31,10 @@ def __init__(self, bert, config): def forward(self, inputs): text_raw_indices = inputs[0] - rna_type = inputs[1] - - # rna_type_ids = self.bert(rna_type)['last_hidden_state'] - # last_hidden_state = self.bert(text_raw_indices)['last_hidden_state'] - # last_hidden_state = self.linear(torch.cat([last_hidden_state, rna_type_ids], dim=-1)) - # last_hidden_state = self.bert(text_raw_indices)["last_hidden_state"] pooled_out = self.pooler(last_hidden_state) + pooled_out = self.dropout(pooled_out) out = self.dense(pooled_out) if self.sigmoid: out = self.sigmoid(out) diff --git a/pyabsa/tasks/_Archive/RNAClassification/prediction/rna_classifier.py b/pyabsa/tasks/_Archive/RNAClassification/prediction/rna_classifier.py index 7fa792b6d..a984d3227 100644 --- a/pyabsa/tasks/_Archive/RNAClassification/prediction/rna_classifier.py +++ b/pyabsa/tasks/_Archive/RNAClassification/prediction/rna_classifier.py @@ -81,7 +81,7 @@ def __init__(self, checkpoint=None, cal_perplexity=False, **kwargs): ) else: self.bert = AutoModel.from_pretrained( - self.config.pretrained_bert + self.config.pretrained_bert, trust_remote_code=True ) self.model = self.config.model(self.bert, self.config) self.model.load_state_dict( @@ -126,7 +126,7 @@ def __init__(self, checkpoint=None, cal_perplexity=False, **kwargs): ) if not hasattr( - GloVeRNACModelList, self.config.model.__name__ + GloVeRNACModelList, self.config.model.__name__ ) and not hasattr(BERTRNACModelList, self.config.model.__name__): raise KeyError( "The checkpoint you are loading is not from classifier model." @@ -162,12 +162,12 @@ def _log_write_args(self): fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Runs inference on a batch of data. @@ -240,9 +240,11 @@ def predict(self, text: str = None, print_result=True, ignore_error=True, **kwar self.infer_dataloader = DataLoader( dataset=self.dataset, batch_size=self.config.eval_batch_size, shuffle=False ) - # Run the prediction and return the result. - return self._run_prediction(print_result=print_result)[0] + if isinstance(text, list): + return self._run_prediction(print_result=print_result) + else: + return self._run_prediction(print_result=print_result)[0] def _run_prediction(self, save_path=None, print_result=True): _params = filter(lambda p: p.requires_grad, self.model.parameters()) @@ -261,9 +263,6 @@ def _run_prediction(self, save_path=None, print_result=True): it = tqdm.tqdm(self.infer_dataloader, desc="run inference") else: it = self.infer_dataloader - - pred_labels = [] - pre_ex_id = 0 for _, sample in enumerate(it): inputs = [ sample[col].to(self.config.device) @@ -275,13 +274,44 @@ def _run_prediction(self, save_path=None, print_result=True): sen_logits = outputs t_probs = torch.softmax(sen_logits, dim=-1) + if t_targets_all is None: + t_targets_all = np.array( + [ + self.config.label_to_index[x] + if x in self.config.label_to_index + else LabelPaddingOption.SENTIMENT_PADDING + for x in sample["label"] + ] + ) + t_outputs_all = np.array(sen_logits.cpu()).astype(np.float32) + else: + t_targets_all = np.concatenate( + ( + t_targets_all, + [ + self.config.label_to_index[x] + if x in self.config.label_to_index + else LabelPaddingOption.SENTIMENT_PADDING + for x in sample["label"] + ], + ), + axis=0, + ) + t_outputs_all = np.concatenate( + ( + t_outputs_all, + np.array(sen_logits.cpu()).astype(np.float32), + ), + axis=0, + ) + for i, i_probs in enumerate(t_probs): - label = self.config.index_to_label[int(i_probs.argmax(axis=-1))] + sent = self.config.index_to_label[int(i_probs.argmax(axis=-1))] if sample["label"][i] != LabelPaddingOption.LABEL_PADDING: - real_label = sample["label"][i] + real_sent = sample["label"][i] else: - real_label = "N.A." - if real_label != LabelPaddingOption.LABEL_PADDING: + real_sent = "N.A." + if real_sent != LabelPaddingOption.LABEL_PADDING: n_labeled += 1 text_raw = sample["text_raw"][i] @@ -302,118 +332,21 @@ def _run_prediction(self, save_path=None, print_result=True): else: perplexity = "N.A." - if ex_id == pre_ex_id: - pred_labels.append(label) - elif len(it) != 1: - results.append( - { - "ex_id": pre_ex_id, - "text": text_raw, - "label": max(pred_labels, key=pred_labels.count), - "confidence": float(max(i_probs)), - "probs": i_probs.cpu().numpy(), - "ref_label": real_label, - "ref_check": correct[label == real_label] - if real_label != str(LabelPaddingOption.LABEL_PADDING) - else "", - "perplexity": perplexity, - } - ) - n_total += 1 - pre_ex_id = ex_id - pred_labels = [label] - - t_targets_all = ( - torch.cat( - ( - t_targets_all, - torch.tensor( - [self.config.label_to_index[sample["label"][i]]] - ), - ) - ) - if t_targets_all is not None - else torch.tensor( - [self.config.label_to_index[sample["label"][i]]] - ) - ) - t_outputs_all = ( - torch.cat( - ( - t_outputs_all, - torch.tensor( - [ - self.config.label_to_index[ - max(pred_labels, key=pred_labels.count) - ] - ] - ), - ) - ) - if t_outputs_all is not None - else torch.tensor( - [ - self.config.label_to_index[ - max(pred_labels, key=pred_labels.count) - ] - ] - ) - ) - - # fprint(pred_labels) - - results.append( - { - "ex_id": pre_ex_id, - "text": text_raw, - "label": max(pred_labels, key=pred_labels.count), - "confidence": float(max(i_probs)), - "probs": i_probs.cpu().numpy(), - "ref_label": real_label, - "ref_check": correct[label == real_label] - if real_label != str(LabelPaddingOption.LABEL_PADDING) - else "", - "perplexity": perplexity, - } - ) - n_total += 1 - pre_ex_id = ex_id - pred_labels = [label] - - t_targets_all = ( - torch.cat( - ( - t_targets_all, - torch.tensor( - [self.config.label_to_index[sample["label"][i]]] - ), - ) + results.append( + { + "ex_id": ex_id, + "text": text_raw, + "label": sent, + "confidence": float(max(i_probs)), + "probs": i_probs.cpu().numpy(), + "ref_label": real_sent, + "ref_check": correct[sent == real_sent] + if real_sent != str(LabelPaddingOption.LABEL_PADDING) + else "", + "perplexity": perplexity, + } ) - if t_targets_all is not None - else torch.tensor([self.config.label_to_index[sample["label"][i]]]) - ) - t_outputs_all = ( - torch.cat( - ( - t_outputs_all, - torch.tensor( - [ - self.config.label_to_index[ - max(pred_labels, key=pred_labels.count) - ] - ] - ), - ) - ) - if t_outputs_all is not None - else torch.tensor( - [ - self.config.label_to_index[ - max(pred_labels, key=pred_labels.count) - ] - ] - ) - ) + n_total += 1 try: if print_result: @@ -459,7 +392,7 @@ def _run_prediction(self, save_path=None, print_result=True): except Exception as e: fprint("Can not save result: {}, Exception: {}".format(text_raw, e)) - if len(results) > 1: + if len(results) > 1 and print_result: fprint("Total samples:{}".format(n_total)) fprint("Labeled samples:{}".format(n_labeled)) @@ -469,7 +402,8 @@ def _run_prediction(self, save_path=None, print_result=True): digits=4, target_names=[ self.config.index_to_label[x] - for x in sorted(self.config.index_to_label.keys()) if x != -100 + for x in sorted(self.config.index_to_label.keys()) + if x != -100 ], ) fprint( @@ -484,7 +418,9 @@ def _run_prediction(self, save_path=None, print_result=True): t_targets_all, np.argmax(t_outputs_all, -1), labels=[ - self.config.label_to_index[x] for x in self.config.label_to_index if x != '-100' and x != '' + self.config.label_to_index[x] + for x in self.config.label_to_index + if x != "-100" and x != "" ], ) fprint( diff --git a/pyabsa/tasks/_Archive/RNAClassification/trainer/rnac_trainer.py b/pyabsa/tasks/_Archive/RNAClassification/trainer/rnac_trainer.py index 7815730f6..c10b5eed0 100644 --- a/pyabsa/tasks/_Archive/RNAClassification/trainer/rnac_trainer.py +++ b/pyabsa/tasks/_Archive/RNAClassification/trainer/rnac_trainer.py @@ -23,14 +23,14 @@ class RNACTrainer(Trainer): def __init__( - self, - config: RNACConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: RNACConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/tasks/_Archive/RNARegression/__init__.py b/pyabsa/tasks/_Archive/RNARegression/__init__.py new file mode 100644 index 000000000..1decd9a3e --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/__init__.py @@ -0,0 +1,15 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:20 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +# for RNA Sequence-based Regression +from .trainer.rnar_trainer import RNARTrainer +from .configuration.rnar_configuration import RNARConfigManager +from .models import BERTRNARModelList, GloVeRNARModelList +from .dataset_utils.dataset_list import RNARDatasetList, RNARegressionDatasetList +from .prediction.rna_regressor import RNARegressor, Predictor diff --git a/pyabsa/tasks/_Archive/RNARegression/configuration/__init__.py b/pyabsa/tasks/_Archive/RNARegression/configuration/__init__.py new file mode 100644 index 000000000..8dde33303 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/configuration/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:44 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/configuration/rnar_configuration.py b/pyabsa/tasks/_Archive/RNARegression/configuration/rnar_configuration.py new file mode 100644 index 000000000..8cb64382d --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/configuration/rnar_configuration.py @@ -0,0 +1,266 @@ +# -*- coding: utf-8 -*- +# file: rnar_configuration.py +# time: 02/11/2022 19:59 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +import copy + +# if you find the optimal param set of some situation, e.g., some model on some datasets +# please share the main use template main +from pyabsa.framework.configuration_class.configuration_template import ConfigManager +from ..models.__classic__.lstm import LSTM +from ..models.__plm__.bert import BERT_MLP + +_rnar_config_template = { + "model": BERT_MLP, + "optimizer": "adamw", + "learning_rate": 0.00002, + "patience": 99999, + "cache_dataset": True, + "warmup_step": -1, + "show_metric": False, + "max_seq_len": 80, + "dropout": 0, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 10, + "evaluate_begin": 0, + "cross_validate_fold": -1, + "use_amp": False, + "overwrite_cache": False, + # split train and test datasets into 5 folds and repeat 3 trainer +} + +_rnar_config_base = { + "model": BERT_MLP, + "optimizer": "adamw", + "learning_rate": 0.00002, + "pretrained_bert": "microsoft/deberta-v3-base", + "cache_dataset": True, + "warmup_step": -1, + "show_metric": False, + "max_seq_len": 80, + "patience": 99999, + "dropout": 0, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 10, + "evaluate_begin": 0, + "cross_validate_fold": -1 + # split train and test datasets into 5 folds and repeat 3 trainer +} + +_rnar_config_english = { + "model": BERT_MLP, + "optimizer": "adamw", + "learning_rate": 0.00002, + "patience": 99999, + "pretrained_bert": "microsoft/deberta-v3-base", + "cache_dataset": True, + "warmup_step": -1, + "show_metric": False, + "max_seq_len": 80, + "dropout": 0, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 10, + "evaluate_begin": 0, + "cross_validate_fold": -1 + # split train and test datasets into 5 folds and repeat 3 trainer +} + +_rnar_config_multilingual = { + "model": BERT_MLP, + "optimizer": "adamw", + "learning_rate": 0.00002, + "patience": 99999, + "pretrained_bert": "microsoft/mdeberta-v3-base", + "cache_dataset": True, + "warmup_step": -1, + "show_metric": False, + "max_seq_len": 80, + "dropout": 0, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 10, + "evaluate_begin": 0, + "cross_validate_fold": -1 + # split train and test datasets into 5 folds and repeat 3 trainer +} + +_rnar_config_chinese = { + "model": BERT_MLP, + "optimizer": "adamw", + "learning_rate": 0.00002, + "patience": 99999, + "cache_dataset": True, + "warmup_step": -1, + "show_metric": False, + "pretrained_bert": "bert-base-chinese", + "max_seq_len": 80, + "dropout": 0, + "l2reg": 0.000001, + "num_epoch": 10, + "batch_size": 16, + "initializer": "xavier_uniform_", + "seed": 52, + "output_dim": 3, + "log_step": 10, + "evaluate_begin": 0, + "cross_validate_fold": -1 + # split train and test datasets into 5 folds and repeat 3 trainer +} + +_rnar_config_glove = { + "model": LSTM, + "optimizer": "adamw", + "learning_rate": 0.001, + "cache_dataset": True, + "warmup_step": -1, + "show_metric": False, + "max_seq_len": 100, + "patience": 20, + "dropout": 0.1, + "l2reg": 0.000001, + "num_epoch": 100, + "batch_size": 64, + "initializer": "xavier_uniform_", + "seed": 52, + "embed_dim": 300, + "hidden_dim": 300, + "output_dim": 3, + "log_step": 5, + "warm_step": -1, + "hops": 3, # valid in MemNet and RAM only + "evaluate_begin": 0, + "cross_validate_fold": -1, +} + + +class RNARConfigManager(ConfigManager): + def __init__(self, args, **kwargs): + """ + Available Params: {'model': MLP, + 'optimizer': "adamw", + 'learning_rate': 0.00002, + 'pretrained_bert': "roberta-base", + 'cache_dataset': True, + 'warmup_step': -1, + 'show_metric': False, + 'max_seq_len': 80, + 'patience': 99999, + 'dropout': 0, + 'l2reg': 0.000001, + 'num_epoch': 10, + 'batch_size': 16, + 'initializer': 'xavier_uniform_', + 'seed': {52, 25} + 'embed_dim': 768, + 'hidden_dim': 768, + 'output_dim': 3, + 'log_step': 10, + 'evaluate_begin': 0, + 'cross_validate_fold': -1 # split train and test datasets into 5 folds and repeat 3 trainer + } + :param args: + :param kwargs: + """ + super().__init__(args, **kwargs) + + @staticmethod + def set_rnar_config(configType: str, newitem: dict): + if isinstance(newitem, dict): + if configType == "template": + _rnar_config_template.update(newitem) + elif configType == "base": + _rnar_config_base.update(newitem) + elif configType == "english": + _rnar_config_english.update(newitem) + elif configType == "chinese": + _rnar_config_chinese.update(newitem) + elif configType == "multilingual": + _rnar_config_multilingual.update(newitem) + elif configType == "glove": + _rnar_config_glove.update(newitem) + else: + raise ValueError( + "Wrong value of configuration_class type supplied, please use one from following type: template, base, english, chinese, multilingual, glove" + ) + else: + raise TypeError( + "Wrong type of new configuration_class item supplied, please use dict e.g.{'NewConfig': NewValue}" + ) + + @staticmethod + def set_rnar_config_template(newitem): + RNARConfigManager.set_rnar_config("template", newitem) + + @staticmethod + def set_rnar_config_base(newitem): + RNARConfigManager.set_rnar_config("base", newitem) + + @staticmethod + def set_rnar_config_english(newitem): + RNARConfigManager.set_rnar_config("english", newitem) + + @staticmethod + def set_rnar_config_chinese(newitem): + RNARConfigManager.set_rnar_config("chinese", newitem) + + @staticmethod + def set_rnar_config_multilingual(newitem): + RNARConfigManager.set_rnar_config("multilingual", newitem) + + @staticmethod + def set_rnar_config_glove(newitem): + RNARConfigManager.set_rnar_config("glove", newitem) + + @staticmethod + def get_rnar_config_template(): + _rnar_config_template.update(_rnar_config_template) + return RNARConfigManager(copy.deepcopy(_rnar_config_template)) + + @staticmethod + def get_rnar_config_base(): + _rnar_config_template.update(_rnar_config_base) + return RNARConfigManager(copy.deepcopy(_rnar_config_template)) + + @staticmethod + def get_rnar_config_english(): + _rnar_config_template.update(_rnar_config_english) + return RNARConfigManager(copy.deepcopy(_rnar_config_template)) + + @staticmethod + def get_rnar_config_chinese(): + _rnar_config_template.update(_rnar_config_chinese) + return RNARConfigManager(copy.deepcopy(_rnar_config_template)) + + @staticmethod + def get_rnar_config_multilingual(): + _rnar_config_template.update(_rnar_config_multilingual) + return RNARConfigManager(copy.deepcopy(_rnar_config_template)) + + @staticmethod + def get_rnar_config_glove(): + _rnar_config_template.update(_rnar_config_glove) + return RNARConfigManager(copy.deepcopy(_rnar_config_template)) diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/__init__.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/__init__.py new file mode 100644 index 000000000..92e79b34c --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:48 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/data_utils_for_inference.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/data_utils_for_inference.py new file mode 100644 index 000000000..c51bafd8b --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/data_utils_for_inference.py @@ -0,0 +1,148 @@ +# -*- coding: utf-8 -*- +# file: data_utils_for_inference.py +# time: 02/11/2022 15:39 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +import torch +import tqdm +from torch.utils.data import Dataset + +from pyabsa.framework.dataset_class.dataset_template import PyABSADataset +from pyabsa.framework.tokenizer_class.tokenizer_class import pad_and_truncate +from pyabsa.utils.file_utils.file_utils import load_dataset_from_file +from pyabsa.utils.pyabsa_utils import fprint + + +class GloVeRNARDataset(Dataset): + def __init__(self, config, tokenizer): + self.tokenizer = tokenizer + self.config = config + self.data = [] + + def parse_sample(self, text): + return [text] + + def prepare_infer_sample(self, text: str, ignore_error): + if isinstance(text, list): + self.process_data(text, ignore_error=ignore_error) + else: + self.process_data(self.parse_sample(text), ignore_error=ignore_error) + + def prepare_infer_dataset(self, infer_file, ignore_error): + lines = load_dataset_from_file(infer_file, config=self.config) + samples = [] + for sample in lines: + if sample: + samples.extend(self.parse_sample(sample)) + self.process_data(samples, ignore_error) + + def process_data(self, samples, ignore_error=True): + all_data = [] + if len(samples) > 100: + it = tqdm.tqdm(samples, desc="preparing text classification dataloader") + else: + it = samples + for ex_id, text in enumerate(it): + try: + # handle for empty lines in inference datasets + if text is None or "" == text.strip(): + raise RuntimeError("Invalid Input!") + + line = ( + text.strip().split("\t") + if "\t" in text + else text.strip().split(",") + ) + try: + _, label, r1r2_label, r1r3_label, r2r3_label, seq = ( + line[0], + line[1], + line[2], + line[3], + line[4], + line[5], + ) + label = float(label.strip()) + + # r1r2_label = float(r1r2_label.strip()) + # r1r3_label = float(r1r3_label.strip()) + # r2r3_label = float(r2r3_label.strip()) + # if len(seq) > 2 * self.config.max_seq_len: + # continue + for x in range(len(seq) // (self.config.max_seq_len * 3) + 1): + _seq = seq[ + x + * (self.config.max_seq_len * 3) : (x + 1) + * (self.config.max_seq_len * 3) + ] + rna_indices = self.tokenizer.text_to_sequence(_seq) + rna_indices = pad_and_truncate( + rna_indices, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + + data = { + "ex_id": torch.tensor(ex_id, dtype=torch.long), + "text_raw": seq, + "text_indices": torch.tensor(rna_indices, dtype=torch.long), + "label": torch.tensor(label, dtype=torch.float32), + # 'r1r2_label': torch.tensor(r1r2_label, dtype=torch.float32), + # 'r1r3_label': torch.tensor(r1r3_label, dtype=torch.float32), + # 'r2r3_label': torch.tensor(r2r3_label, dtype=torch.float32), + } + + all_data.append(data) + + except Exception as e: + exon1, intron, exon2, label = line[0], line[1], line[2], line[3] + label = float(label.strip()) + seq = exon1 + intron + exon2 + exon1_ids = self.tokenizer.text_to_sequence(exon1) + intron_ids = self.tokenizer.text_to_sequence(intron) + exon2_ids = self.tokenizer.text_to_sequence(exon2) + + intron_ids = pad_and_truncate( + intron_ids, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + + rna_indices = exon1_ids + intron_ids + exon2_ids + rna_indices = pad_and_truncate( + rna_indices, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + + data = { + "ex_id": torch.tensor(ex_id, dtype=torch.long), + "text_raw": seq, + "text_indices": torch.tensor(rna_indices, dtype=torch.long), + "intro_indices": torch.tensor(intron_ids, dtype=torch.long), + "label": torch.tensor(label, dtype=torch.float32), + } + + all_data.append(data) + + except Exception as e: + if ignore_error: + fprint("Ignore error while processing:", text, e) + else: + raise e + + self.data = all_data + + self.data = PyABSADataset.covert_to_tensor(self.data) + + return self.data + + def __getitem__(self, index): + return self.data[index] + + def __len__(self): + return len(self.data) diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/data_utils_for_training.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/data_utils_for_training.py new file mode 100644 index 000000000..d5e7628c6 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__classic__/data_utils_for_training.py @@ -0,0 +1,123 @@ +# -*- coding: utf-8 -*- +# file: data_utils_for_training.py +# time: 02/11/2022 15:39 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +import torch +import tqdm + +from pyabsa.framework.dataset_class.dataset_template import PyABSADataset +from pyabsa.utils.file_utils.file_utils import load_dataset_from_file +from pyabsa.framework.tokenizer_class.tokenizer_class import pad_and_truncate + + +class GloVeRNARDataset(PyABSADataset): + def load_data_from_dict(self, dataset_dict, **kwargs): + pass + + def load_data_from_file(self, dataset_file, **kwargs): + lines = load_dataset_from_file( + self.config.dataset_file[self.dataset_type], config=self.config + ) + + all_data = [] + + label_set = set() + + for ex_id, i in enumerate( + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + ): + line = ( + lines[i].strip().split("\t") + if "\t" in lines[i] + else lines[i].strip().split(",") + ) + try: + _, label, r1r2_label, r1r3_label, r2r3_label, seq = ( + line[0], + line[1], + line[2], + line[3], + line[4], + line[5], + ) + label = float(label.strip()) + + # r1r2_label = float(r1r2_label.strip()) + # r1r3_label = float(r1r3_label.strip()) + # r2r3_label = float(r2r3_label.strip()) + # if len(seq) > 2 * config.max_seq_len: + # continue + # for x in range(len(seq) // (config.max_seq_len * 2) + 1): + # _seq = seq[x * (config.max_seq_len * 2):(x + 1) * (config.max_seq_len * 2)] + for x in range(len(seq) // (self.config.max_seq_len * 3) + 1): + _seq = seq[ + x + * (self.config.max_seq_len * 3) : (x + 1) + * (self.config.max_seq_len * 3) + ] + rna_indices = self.tokenizer.text_to_sequence(_seq) + rna_indices = pad_and_truncate( + rna_indices, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + + if any(rna_indices): + data = { + "ex_id": torch.tensor(ex_id, dtype=torch.long), + "text_indices": torch.tensor(rna_indices, dtype=torch.long), + "label": torch.tensor(label, dtype=torch.float32), + # 'r1r2_label': torch.tensor(r1r2_label, dtype=torch.float32), + # 'r1r3_label': torch.tensor(r1r3_label, dtype=torch.float32), + # 'r2r3_label': torch.tensor(r2r3_label, dtype=torch.float32), + } + + all_data.append(data) + + except Exception as e: + exon1, intron, exon2, label = line[0], line[1], line[2], line[3] + label = float(label.strip()) + seq = exon1 + intron + exon2 + exon1_ids = self.tokenizer.text_to_sequence(exon1, padding="do_not_pad") + intron_ids = self.tokenizer.text_to_sequence( + intron, padding="do_not_pad" + ) + exon2_ids = self.tokenizer.text_to_sequence(exon2, padding="do_not_pad") + + rna_indices = exon1_ids + intron_ids + exon2_ids + + rna_indices = pad_and_truncate( + rna_indices, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + intron_ids = pad_and_truncate( + intron_ids, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + + data = { + "ex_id": torch.tensor(ex_id, dtype=torch.long), + "text_indices": torch.tensor(rna_indices, dtype=torch.long), + "label": torch.tensor(label, dtype=torch.float32), + } + + all_data.append(data) + + self.config.output_dim = 1 + self.data = all_data + + def __init__(self, config, tokenizer, dataset_type="train", **kwargs): + super().__init__(config, tokenizer, dataset_type, **kwargs) + + def __getitem__(self, index): + return self.data[index] + + def __len__(self): + return len(self.data) diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__init__.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__init__.py new file mode 100644 index 000000000..de49e5c0c --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:39 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/__init__.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/__init__.py new file mode 100644 index 000000000..92e79b34c --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:48 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/data_utils_for_inference.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/data_utils_for_inference.py new file mode 100644 index 000000000..3868a06ce --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/data_utils_for_inference.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- +# file: data_utils_for_inference.py +# time: 02/11/2022 15:39 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +import torch +import tqdm +from torch.utils.data import Dataset + +from pyabsa.framework.dataset_class.dataset_template import PyABSADataset +from pyabsa.framework.tokenizer_class.tokenizer_class import pad_and_truncate +from pyabsa.utils.file_utils.file_utils import load_dataset_from_file +from pyabsa.utils.pyabsa_utils import fprint + + +class BERTRNARDataset(Dataset): + def __init__(self, config, tokenizer): + self.tokenizer = tokenizer + self.config = config + self.data = [] + + def parse_sample(self, text): + return [text] + + def prepare_infer_sample(self, text: str, ignore_error): + if isinstance(text, list): + self.process_data(text, ignore_error=ignore_error) + else: + self.process_data(self.parse_sample(text), ignore_error=ignore_error) + + def prepare_infer_dataset(self, infer_file, ignore_error): + lines = load_dataset_from_file(infer_file, config=self.config) + samples = [] + for sample in lines: + if sample: + samples.extend(self.parse_sample(sample)) + self.process_data(samples, ignore_error) + + def process_data(self, samples, ignore_error=True): + all_data = [] + if len(samples) > 100: + it = tqdm.tqdm(samples, desc="preparing text classification dataloader") + else: + it = samples + for ex_id, text in enumerate(it): + try: + # handle for empty lines in inference datasets + if text is None or "" == text.strip(): + raise RuntimeError("Invalid Input!") + + seq, label = text.strip().split("$LABEL$") + + try: + label = float(label.strip()) + + # r1r2_label = float(r1r2_label.strip()) + # r1r3_label = float(r1r3_label.strip()) + # r2r3_label = float(r2r3_label.strip()) + # if len(seq) > 2 * self.config.max_seq_len: + # continue + for x in range(len(seq) // (self.config.max_seq_len * 2) + 1): + _seq = seq[ + x + * (self.config.max_seq_len * 2) : (x + 1) + * (self.config.max_seq_len * 2) + ] + # rna_indices = self.tokenizer.text_to_sequence(_seq) + rna_indices = self.tokenizer.convert_tokens_to_ids(list(seq)) + + data = { + "ex_id": torch.tensor(ex_id, dtype=torch.long), + "text_indices": torch.tensor(rna_indices, dtype=torch.long), + "text_raw": seq, + "label": torch.tensor(label, dtype=torch.float32), + # 'r1r2_label': torch.tensor(r1r2_label, dtype=torch.float32), + # 'r1r3_label': torch.tensor(r1r3_label, dtype=torch.float32), + # 'r2r3_label': torch.tensor(r2r3_label, dtype=torch.float32), + } + all_data.append(data) + + except Exception as e: + rna_seq, _, label = text.strip().partition("$LABEL$") + + label = float(label.strip()) + rna_indices = self.tokenizer.text_to_sequence( + rna_seq, padding="do_not_pad" + ) + rna_indices = pad_and_truncate( + rna_indices, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + + data = { + "ex_id": torch.tensor(ex_id, dtype=torch.long), + "text_indices": torch.tensor(rna_indices, dtype=torch.long), + "text_raw": seq, + "label": torch.tensor(label, dtype=torch.float32), + # 'r1r2_label': torch.tensor(r1r2_label, dtype=torch.float32), + # 'r1r3_label': torch.tensor(r1r3_label, dtype=torch.float32), + # 'r2r3_label': torch.tensor(r2r3_label, dtype=torch.float32), + } + + all_data.append(data) + + except Exception as e: + if ignore_error: + fprint("Ignore error while processing:", text, e) + else: + raise e + + self.data = all_data + + self.data = PyABSADataset.covert_to_tensor(self.data) + + return self.data + + def __getitem__(self, index): + return self.data[index] + + def __len__(self): + return len(self.data) diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/data_utils_for_training.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/data_utils_for_training.py new file mode 100644 index 000000000..7dc93dc98 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/__plm__/data_utils_for_training.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +# file: data_utils_for_training.py +# time: 02/11/2022 15:39 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. +import json + +import torch +import tqdm + +from pyabsa.framework.dataset_class.dataset_template import PyABSADataset +from pyabsa.framework.tokenizer_class.tokenizer_class import pad_and_truncate +from pyabsa.utils.file_utils.file_utils import load_dataset_from_file + + +class BERTRNARDataset(PyABSADataset): + def load_data_from_dict(self, dataset_dict, **kwargs): + pass + + def load_data_from_file(self, dataset_file, **kwargs): + lines = load_dataset_from_file( + self.config.dataset_file[self.dataset_type], config=self.config + ) + + all_data = [] + + for ex_id, i in enumerate( + tqdm.tqdm(range(len(lines)), desc="preparing dataloader") + ): + line = json.loads(lines[i].strip()) + transcript = list(line["seq"].strip()) + structure = [int(_) for _ in list(line["stru2"].strip())] + label = line["label"] + + assert len(transcript) == 100 + rna_indices = self.tokenizer.tokenizer.convert_tokens_to_ids(transcript) + rna_indices = pad_and_truncate( + rna_indices, + self.config.max_seq_len, + value=self.tokenizer.pad_token_id, + ) + structure = pad_and_truncate( + structure, + self.config.max_seq_len, + value=9, + ) + data = { + "ex_id": torch.tensor(ex_id, dtype=torch.long), + "text_indices": torch.tensor(rna_indices, dtype=torch.long), + "structure": torch.tensor(structure, dtype=torch.float32), + "label": torch.tensor(label, dtype=torch.float32), + } + all_data.append(data) + + self.config.output_dim = 1 + + self.data = all_data + + def __init__(self, config, tokenizer, dataset_type="train"): + super().__init__(config, tokenizer, dataset_type) + + def __getitem__(self, index): + return self.data[index] + + def __len__(self): + return len(self.data) diff --git a/pyabsa/tasks/_Archive/RNARegression/dataset_utils/dataset_list.py b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/dataset_list.py new file mode 100644 index 000000000..d10c305de --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/dataset_utils/dataset_list.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +# file: dataset_list.py +# time: 02/11/2022 19:43 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + + +class RNARDatasetList(list): + """ + RNA Sequence-based Regression Dataset Lists + """ + + def __init__(self): + super(RNARDatasetList, self).__init__(self.__class__.__dict__.values()) + + +class RNARegressionDatasetList(list): + """ + RNA Sequence-based Regression Dataset Lists + """ + + def __init__(self): + super(RNARegressionDatasetList, self).__init__(self.__class__.__dict__.values()) diff --git a/pyabsa/tasks/_Archive/RNARegression/instructor/__init__.py b/pyabsa/tasks/_Archive/RNARegression/instructor/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pyabsa/tasks/_Archive/RNARegression/instructor/rnar_instructor.py b/pyabsa/tasks/_Archive/RNARegression/instructor/rnar_instructor.py new file mode 100644 index 000000000..54f5e84c0 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/instructor/rnar_instructor.py @@ -0,0 +1,694 @@ +# -*- coding: utf-8 -*- +# file: classifier_instructor.py +# time: 2021/4/22 0022 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# Copyright (C) 2021. All Rights Reserved. +import math +import os +import shutil +import time + +import numpy +import numpy as np +import torch +import torch.nn as nn +from findfile import find_file +from sklearn import metrics +from torch import cuda +from torch.utils.data import ( + DataLoader, + random_split, + ConcatDataset, + RandomSampler, + SequentialSampler, +) +from tqdm import tqdm +from transformers import AutoModel, AutoTokenizer + +from pyabsa.framework.flag_class.flag_template import DeviceTypeOption +from pyabsa.framework.instructor_class.instructor_template import BaseTrainingInstructor +from pyabsa.framework.tokenizer_class.tokenizer_class import ( + Tokenizer, + build_embedding_matrix, + PretrainedTokenizer, +) +from pyabsa.utils.file_utils.file_utils import save_model +from pyabsa.utils.pyabsa_utils import init_optimizer, fprint +from ..dataset_utils.__classic__.data_utils_for_training import GloVeRNARDataset +from ..dataset_utils.__plm__.data_utils_for_training import BERTRNARDataset +from ..models import GloVeRNARModelList, BERTRNARModelList + + +class RNARTrainingInstructor(BaseTrainingInstructor): + def _init_misc(self): + # use DataParallel for trainer if device count larger than 1 + if self.config.auto_device == DeviceTypeOption.ALL_CUDA: + self.model.to(self.config.device) + self.model = torch.nn.parallel.DataParallel(self.model).module + else: + self.model.to(self.config.device) + + self.optimizer = init_optimizer(self.config.optimizer)( + self.model.parameters(), + lr=self.config.learning_rate, + weight_decay=self.config.l2reg, + ) + + self.train_dataloaders = [] + self.valid_dataloaders = [] + + if os.path.exists("./init_state_dict.bin"): + os.remove("./init_state_dict.bin") + if self.config.cross_validate_fold > 0: + torch.save(self.model.state_dict(), "./init_state_dict.bin") + + self.config.device = torch.device(self.config.device) + if self.config.device.type == DeviceTypeOption.CUDA: + self.logger.info( + "cuda memory allocated:{}".format( + torch.cuda.memory_allocated(device=self.config.device) + ) + ) + + def _cache_or_load_dataset(self): + pass + + def _evaluate_acc_f1(self, test_dataloader): + pass + + def _load_dataset_and_prepare_dataloader(self): + self.config.inputs_cols = self.config.model.inputs + + cache_path = self.load_cache_dataset() + + # init BERT-based model and dataset + if hasattr(BERTRNARModelList, self.config.model.__name__): + self.tokenizer = PretrainedTokenizer(self.config) + if not os.path.exists(cache_path) or self.config.overwrite_cache: + self.train_set = BERTRNARDataset( + self.config, self.tokenizer, dataset_type="train" + ) + self.test_set = BERTRNARDataset( + self.config, self.tokenizer, dataset_type="test" + ) + self.valid_set = BERTRNARDataset( + self.config, self.tokenizer, dataset_type="valid" + ) + + try: + self.bert = AutoModel.from_pretrained( + self.config.pretrained_bert, ignore_mismatched_sizes=True + ) + except ValueError as e: + fprint("Init pretrained model failed, exception: {}".format(e)) + + # init the model behind the construction of datasets in case of updating output_dim + self.model = self.config.model(self.bert, self.config).to( + self.config.device + ) + + elif hasattr(GloVeRNARModelList, self.config.model.__name__): + # init GloVe-based model and dataset + self.tokenizer = Tokenizer.build_tokenizer( + config=self.config, + cache_path="{0}_tokenizer.dat".format( + os.path.basename(self.config.dataset_name) + ), + pre_tokenizer=AutoTokenizer.from_pretrained( + self.config.pretrained_bert + ), + ) + self.embedding_matrix = build_embedding_matrix( + config=self.config, + tokenizer=self.tokenizer, + cache_path="{0}_{1}_embedding_matrix.dat".format( + str(self.config.embed_dim), + os.path.basename(self.config.dataset_name), + ), + ) + self.train_set = GloVeRNARDataset( + self.config, self.tokenizer, dataset_type="train" + ) + self.test_set = GloVeRNARDataset( + self.config, self.tokenizer, dataset_type="test" + ) + self.valid_set = GloVeRNARDataset( + self.config, self.tokenizer, dataset_type="valid" + ) + + self.model = self.config.model(self.embedding_matrix, self.config).to( + self.config.device + ) + self.config.embedding_matrix = self.embedding_matrix + + self.config.tokenizer = self.tokenizer + self.save_cache_dataset(cache_path) + + def __init__(self, config): + super().__init__(config) + + self._load_dataset_and_prepare_dataloader() + + self._init_misc() + + def reload_model(self, ckpt="./init_state_dict.bin"): + if os.path.exists(ckpt): + self.model.load_state_dict( + torch.load(find_file(ckpt, or_key=[".bin", "state_dict"])), + strict=False, + ) + + def _prepare_dataloader(self): + if self.config.cross_validate_fold < 1: + train_sampler = RandomSampler( + self.train_set if not self.train_set else self.train_set + ) + self.train_dataloaders.append( + DataLoader( + dataset=self.train_set, + batch_size=self.config.batch_size, + sampler=train_sampler, + pin_memory=True, + ) + ) + if self.test_set: + self.test_dataloader = DataLoader( + dataset=self.test_set, + batch_size=self.config.batch_size, + shuffle=False, + ) + + if self.valid_set: + self.valid_dataloader = DataLoader( + dataset=self.valid_set, + batch_size=self.config.batch_size, + shuffle=False, + ) + else: + split_dataset = self.train_set + len_per_fold = len(split_dataset) // self.config.cross_validate_fold + 1 + folds = random_split( + split_dataset, + tuple( + [len_per_fold] * (self.config.cross_validate_fold - 1) + + [ + len(split_dataset) + - len_per_fold * (self.config.cross_validate_fold - 1) + ] + ), + ) + + for f_idx in range(self.config.cross_validate_fold): + train_set = ConcatDataset( + [x for i, x in enumerate(folds) if i != f_idx] + ) + val_set = folds[f_idx] + train_sampler = RandomSampler(train_set if not train_set else train_set) + val_sampler = SequentialSampler(val_set if not val_set else val_set) + self.train_dataloaders.append( + DataLoader( + dataset=train_set, + batch_size=self.config.batch_size, + sampler=train_sampler, + ) + ) + self.valid_dataloaders.append( + DataLoader( + dataset=val_set, + batch_size=self.config.batch_size, + sampler=val_sampler, + ) + ) + if self.test_set: + self.test_dataloader = DataLoader( + dataset=self.test_set, + batch_size=self.config.batch_size, + shuffle=False, + ) + + # def _train(self, criterion): + # self._prepare_dataloader() + # + # if self.config.warmup_step >= 0: + # self.lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + # self.optimizer, + # T_max=len(self.train_dataloaders[0]) * self.config.num_epoch, + # ) + # self.warmup_scheduler = warmup.UntunedLinearWarmup(self.optimizer) + # + # if len(self.valid_dataloaders) > 1: + # return self._k_fold_train_and_evaluate(criterion) + # else: + # return self._train_and_evaluate(criterion) + + def _train_and_evaluate(self, criterion): + global_step = 0 + max_fold_r2 = math.inf + save_path = "{0}/{1}_{2}".format( + self.config.model_path_to_save, + self.config.model_name, + self.config.dataset_name, + ) + + losses = [] + + self.config.metrics_of_this_checkpoint = {"r2": 0} + self.config.max_test_metrics = {"max_test_r2": 0} + + self.logger.info( + "***** Running training for {} *****".format(self.config.task_name) + ) + self.logger.info("Training set examples = %d", len(self.train_set)) + if self.valid_set: + self.logger.info("Valid set examples = %d", len(self.valid_set)) + if self.test_set: + self.logger.info("Test set examples = %d", len(self.test_set)) + self.logger.info("Batch size = %d", self.config.batch_size) + self.logger.info( + "Num steps = %d", + len(self.train_dataloaders[0]) + // self.config.batch_size + * self.config.num_epoch, + ) + patience = self.config.patience + self.config.evaluate_begin + if self.config.log_step < 0: + self.config.log_step = ( + len(self.train_dataloaders[0]) + if self.config.log_step < 0 + else self.config.log_step + ) + + for epoch in range(self.config.num_epoch): + patience -= 1 + description = "Epoch:{} | Loss: {}".format(epoch, 0) + iterator = tqdm(self.train_dataloaders[0], desc=description) + for i_batch, sample_batched in enumerate(iterator): + global_step += 1 + # switch model to train mode, clear gradient accumulators + self.model.train() + self.optimizer.zero_grad() + inputs = [ + sample_batched[col].to(self.config.device) + for col in self.config.inputs_cols + ] + if self.config.use_amp: + with torch.cuda.amp.autocast(): + outputs = self.model(inputs) + else: + outputs = self.model(inputs) + + targets = sample_batched["label"].to(self.config.device) + + if isinstance(outputs, dict) and "loss" in outputs: + loss = outputs["r2"] + else: + loss = criterion(outputs.view(-1), targets) + + losses.append(loss.item()) + if self.config.use_amp and self.scaler: + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + else: + loss.backward() + self.optimizer.step() + + if self.config.warmup_step >= 0: + with self.warmup_scheduler.dampening(): + self.lr_scheduler.step() + + # evaluate if test set is available + if global_step % self.config.log_step == 0: + if self.test_dataloader and epoch >= self.config.evaluate_begin: + if self.valid_dataloader: + test_r2 = self._evaluate_r2( + self.valid_dataloader, criterion + ) + else: + test_r2 = self._evaluate_r2(self.test_dataloader, criterion) + + self.config.metrics_of_this_checkpoint["r2"] = test_r2 + + if test_r2 < max_fold_r2: + if test_r2 < max_fold_r2: + patience = self.config.patience - 1 + max_fold_r2 = test_r2 + + if self.config.model_path_to_save: + if not os.path.exists(self.config.model_path_to_save): + os.makedirs(self.config.model_path_to_save) + if save_path and self.config.save_last_ckpt_only: + try: + shutil.rmtree(save_path) + # logger.info('Remove sub-optimal trained model:', save_path) + except: + # logger.info('Can not remove sub-optimal trained model:', save_path) + pass + save_path = "{0}/{1}_{2}_r2_{3}/".format( + self.config.model_path_to_save, + self.config.model_name, + self.config.dataset_name, + round(test_r2, 4), + ) + + if ( + test_r2 + < self.config.max_test_metrics["max_test_r2"] + ): + self.config.max_test_metrics[ + "max_test_r2" + ] = test_r2 + + save_model( + self.config, self.model, self.tokenizer, save_path + ) + + description = "Epoch:{} | Loss:{:.4f} | Dev R2 Score:{:.4f}(max:{:.4f})".format( + epoch, loss.item(), test_r2, max_fold_r2 + ) + elif self.config.save_mode and epoch >= self.config.evaluate_begin: + save_model( + self.config, + self.model, + self.tokenizer, + save_path + "_{}/".format(loss.item()), + ) + else: + if self.config.get("loss_display", "smooth") == "smooth": + description = "Epoch:{:>3d} | Smooth Loss: {:>.4f}".format( + epoch, round(np.nanmean(losses), 4) + ) + else: + description = "Epoch:{:>3d} | Batch Loss: {:>.4f}".format( + epoch, round(loss.item(), 4) + ) + iterator.set_description(description) + iterator.refresh() + if patience == 0: + break + + if not self.valid_dataloader: + self.config.MV.log_metric( + self.config.model_name + + "-" + + self.config.dataset_name + + "-" + + self.config.pretrained_bert, + "Max-Test-R2-Score w/o Valid Set", + max_fold_r2, + ) + + if self.valid_dataloader: + fprint( + "Loading best model: {} and evaluating on test set ".format(save_path) + ) + self.reload_model(find_file(save_path, ".state_dict")) + max_fold_r2 = self._evaluate_r2(self.test_dataloader, criterion) + + self.config.MV.log_metric( + self.config.model_name + + "-" + + self.config.dataset_name + + "-" + + self.config.pretrained_bert, + "Max-Test-R2-Score", + max_fold_r2, + ) + + self.logger.info(self.config.MV.summary(no_print=True)) + # self.logger.info(self.config.MV.short_summary(no_print=True)) + + if self.valid_dataloader or self.config.save_mode: + del self.train_dataloaders + del self.test_dataloader + del self.valid_dataloader + del self.model + cuda.empty_cache() + time.sleep(3) + return save_path + else: + del self.train_dataloaders + del self.test_dataloader + del self.valid_dataloader + cuda.empty_cache() + time.sleep(3) + return self.model, self.config, self.tokenizer + + def _k_fold_train_and_evaluate(self, criterion): + fold_test_r2 = [] + + save_path_k_fold = "" + max_fold_r2_k_fold = 0 + + losses = [] + + self.config.metrics_of_this_checkpoint = {"r2": 0} + self.config.max_test_metrics = {"max_test_r2": 0} + + for f, (train_dataloader, valid_dataloader) in enumerate( + zip(self.train_dataloaders, self.valid_dataloaders) + ): + patience = self.config.patience + self.config.evaluate_begin + if self.config.log_step < 0: + self.config.log_step = ( + len(self.train_dataloaders[0]) + if self.config.log_step < 0 + else self.config.log_step + ) + + self.logger.info( + "***** Running training for {} *****".format(self.config.task_name) + ) + self.logger.info("Training set examples = %d", len(self.train_set)) + if self.valid_set: + self.logger.info("Valid set examples = %d", len(self.valid_set)) + if self.test_set: + self.logger.info("Test set examples = %d", len(self.test_set)) + self.logger.info("Batch size = %d", self.config.batch_size) + self.logger.info( + "Num steps = %d", + len(train_dataloader) // self.config.batch_size * self.config.num_epoch, + ) + if len(self.train_dataloaders) > 1: + self.logger.info( + "No. {} trainer in {} folds".format( + f + 1, self.config.cross_validate_fold + ) + ) + global_step = 0 + max_fold_r2 = 0 + save_path = "{0}/{1}_{2}".format( + self.config.model_path_to_save, + self.config.model_name, + self.config.dataset_name, + ) + for epoch in range(self.config.num_epoch): + patience -= 1 + description = "Epoch:{} | Loss:{}".format(epoch, 0) + iterator = tqdm(train_dataloader, desc=description) + for i_batch, sample_batched in enumerate(iterator): + global_step += 1 + # switch model to train mode, clear gradient accumulators + self.model.train() + self.optimizer.zero_grad() + inputs = [ + sample_batched[col].to(self.config.device) + for col in self.config.inputs_cols + ] + with torch.cuda.amp.autocast(): + if self.config.use_amp: + with torch.cuda.amp.autocast(): + outputs = self.model(inputs) + else: + outputs = self.model(inputs) + + targets = sample_batched["label"].to(self.config.device) + + if isinstance(outputs, dict) and "loss" in outputs: + loss = outputs["loss"] + else: + loss = criterion(outputs.view(-1), targets) + + if self.config.use_amp and self.scaler: + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + else: + loss.backward() + self.optimizer.step() + + if self.config.warmup_step >= 0: + with self.warmup_scheduler.dampening(): + self.lr_scheduler.step() + + # evaluate if test set is available + if global_step % self.config.log_step == 0: + if self.test_dataloader and epoch >= self.config.evaluate_begin: + test_r2 = self._evaluate_r2(valid_dataloader, criterion) + + self.config.metrics_of_this_checkpoint["r2"] = test_r2 + if test_r2 > max_fold_r2: + if test_r2 > max_fold_r2: + patience = self.config.patience - 1 + max_fold_r2 = test_r2 + + if self.config.model_path_to_save: + if not os.path.exists( + self.config.model_path_to_save + ): + os.makedirs(self.config.model_path_to_save) + if save_path: + try: + shutil.rmtree(save_path) + # logger.info('Remove sub-optimal trained model:', save_path) + except: + # logger.info('Can not remove sub-optimal trained model:', save_path) + pass + save_path = "{0}/{1}_{2}_r2_{3}/".format( + self.config.model_path_to_save, + self.config.model_name, + self.config.dataset_name, + round(test_r2, 4), + ) + + if ( + test_r2 + < self.config.max_test_metrics["max_test_r2"] + ): + self.config.max_test_metrics[ + "max_test_r2" + ] = test_r2 + + save_model( + self.config, + self.model, + self.tokenizer, + save_path, + ) + + description = "Epoch:{} | Loss:{:.4f} | Dev R2 Score:{:>.2f}(max:{:>.2f})".format( + epoch, loss.item(), test_r2, max_fold_r2 + ) + if ( + self.config.save_mode + and epoch >= self.config.evaluate_begin + ): + save_model( + self.config, + self.model, + self.tokenizer, + save_path + "_{}/".format(loss.item()), + ) + else: + if self.config.get("loss_display", "smooth") == "smooth": + description = "Epoch:{:>3d} | Smooth Loss: {:>.4f}".format( + epoch, round(np.nanmean(losses), 4) + ) + else: + description = "Epoch:{:>3d} | Batch Loss: {:>.4f}".format( + epoch, round(loss.item(), 4) + ) + + iterator.set_description(description) + iterator.refresh() + if patience == 0: + break + + max_fold_r2 = self._evaluate_r2(self.test_dataloader, criterion) + if max_fold_r2 > max_fold_r2_k_fold: + save_path_k_fold = save_path + fold_test_r2.append(max_fold_r2) + + self.config.MV.log_metric( + self.config.model_name, + "Fold{}-Max-Valid-R2-Score".format(f), + max_fold_r2, + ) + + # self.logger.info(self.config.MV.summary(no_print=True)) + self.logger.info(self.config.MV.raw_summary(no_print=True)) + if os.path.exists("./init_state_dict.bin"): + self.reload_model() + + max_test_r2 = numpy.max(fold_test_r2) + + self.config.MV.log_metric( + self.config.model_name + + "-" + + self.config.dataset_name + + "-" + + self.config.pretrained_bert, + "Max-Test-R2-Score", + max_test_r2, + ) + + if self.config.cross_validate_fold > 0: + # self.logger.info(self.config.MV.summary(no_print=True)) + self.logger.info(self.config.MV.raw_summary(no_print=True)) + # self.config.MV.summary() + + self.reload_model(save_path_k_fold) + + if self.valid_dataloader or self.config.save_mode: + del self.train_dataloaders + del self.test_dataloader + del self.valid_dataloaders + del self.model + cuda.empty_cache() + time.sleep(3) + return save_path_k_fold + else: + # direct return model if you do not evaluate + if self.config.model_path_to_save: + save_path_k_fold = "{0}/{1}/".format( + self.config.model_path_to_save, + self.config.model_name, + ) + save_model(self.config, self.model, self.tokenizer, save_path_k_fold) + del self.train_dataloaders + del self.test_dataloader + del self.valid_dataloaders + cuda.empty_cache() + time.sleep(3) + return self.model, self.config, self.tokenizer + + def _evaluate_r2(self, test_dataloader, criterion): + # switch model to evaluation mode + self.model.eval() + all_targets = torch.tensor([], dtype=torch.float32).to(self.config.device) + all_outputs = torch.tensor([], dtype=torch.float32).to(self.config.device) + + with torch.no_grad(): + for t_batch, t_sample_batched in enumerate(test_dataloader): + t_inputs = [ + t_sample_batched[col].to(self.config.device) + for col in self.config.inputs_cols + ] + t_targets = t_sample_batched["label"].to(self.config.device) + + sen_outputs = self.model(t_inputs) + + all_outputs = torch.cat((all_outputs, sen_outputs), 0) + all_targets = torch.cat((all_targets, t_targets), 0) + + r2 = metrics.r2_score(all_targets.cpu().numpy(), all_outputs.cpu().numpy()) + rmse = metrics.mean_squared_error( + all_targets.cpu().numpy(), all_outputs.cpu().numpy() + ) + from scipy.stats import spearmanr + + # 使用scipy库计算斯皮尔曼相关系数 + correlation, p_value = spearmanr( + all_targets.cpu().numpy(), all_outputs.cpu().numpy() + ) + print("斯皮尔曼相关系数:", correlation, "p_value:", p_value) + print("r2:", r2) + print("mse:", rmse) + return rmse + + def run(self): + # Loss and Optimizer + # criterion = nn.CrossEntropyLoss() + criterion = nn.MSELoss() + return self._train(criterion) diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__classic__/__init__.py b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/__init__.py new file mode 100644 index 000000000..92e79b34c --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:48 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__classic__/cnn.py b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/cnn.py new file mode 100644 index 000000000..e1c675159 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/cnn.py @@ -0,0 +1,41 @@ +# -*- coding: utf-8 -*- +# file: cnn.py +# time: 02/11/2022 15:48 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +import torch +import torch.nn as nn + +from torch.nn import Conv1d, MaxPool1d, Linear, Dropout, functional as F + + +class CNN(nn.Module): + inputs = ["text_indices"] + + def __init__(self, embedding_matrix, config): + super(CNN, self).__init__() + self.config = config + self.embed = nn.Embedding.from_pretrained( + torch.tensor(embedding_matrix, dtype=torch.float) + ) + self.cnn = Conv1d( + self.config.embed_dim, + self.config.hidden_dim, + kernel_size=self.config.kernel_size, + padding=self.config.padding, + ) + self.pooling = MaxPool1d(self.config.max_seq_len - self.config.kernel_size + 1) + self.dense = nn.Linear(self.config.hidden_dim, self.config.output_dim) + + def forward(self, inputs): + text_raw_indices = inputs[0] + x = self.embed(text_raw_indices) + hidden_states = self.cnn(x.transpose(1, 2)) + pooled_states = self.pooling(hidden_states) + transposed_states = pooled_states.transpose(1, 2) + out = self.dense(transposed_states).sum(dim=1, keepdim=False) + return out diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__classic__/lstm.py b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/lstm.py new file mode 100644 index 000000000..6d7477232 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/lstm.py @@ -0,0 +1,57 @@ +# -*- coding: utf-8 -*- +# file: lstm.py +# time: 22/10/2022 17:33 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2021. All Rights Reserved. + +import torch +import torch.nn as nn + +from pyabsa.networks.dynamic_rnn import DynamicLSTM + + +class LSTMLayer(nn.Module): + def __init__(self, config): + super(LSTMLayer, self).__init__() + self.lstms = nn.ModuleList() + self.config = config + for i in range(self.config.num_lstm_layer): + self.lstms.append( + DynamicLSTM( + self.config.embed_dim, + self.config.hidden_dim, + num_layers=self.config.num_lstm_layer, + batch_first=True, + bidirectional=True, + ) + ) + + def forward(self, x, x_len): + h, c = None, None + for i in range(len(self.lstms)): + x, (h, c) = self.lstms[i](x, x_len) + return x, (h, c) + + +class LSTM(nn.Module): + inputs = ["text_indices"] + + def __init__(self, embedding_matrix, config): + super(LSTM, self).__init__() + self.config = config + self.embed = nn.Embedding.from_pretrained( + torch.tensor(embedding_matrix, dtype=torch.float) + ) + self.lstm = LSTMLayer(config) + self.dense = nn.Linear(config.hidden_dim, config.output_dim) + + def forward(self, inputs): + text_raw_indices = inputs[0] + x = self.embed(text_raw_indices) + x_len = torch.sum(text_raw_indices != 0, dim=-1) + _, (h_n, _) = self.lstm(x, x_len) + out = self.dense(h_n[0]) + return out diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__classic__/mhsa.py b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/mhsa.py new file mode 100644 index 000000000..b9b4c5f88 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/mhsa.py @@ -0,0 +1,55 @@ +# -*- coding: utf-8 -*- +# file: mhsa.py +# time: 31/10/2022 20:00 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. +import torch +from transformers import AutoConfig +from transformers.models.bert.modeling_bert import BertPooler + +from pyabsa.networks.sa_encoder import Encoder +from torch import nn + + +class MultiHeadSelfAttention(nn.Module): + def __init__(self, bert_config, config): + super(MultiHeadSelfAttention, self).__init__() + self.config = config + self.config.hidden_size = self.config.hidden_dim + self.mhsa = Encoder( + bert_config=bert_config, + config=self.config, + layer_num=self.config.num_mhsa_layer, + ) + self.config = config + + def forward(self, x): + return self.mhsa(x) + + +class MHSA(nn.Module): + inputs = ["text_indices"] + + def __init__(self, embedding_matrix, config): + super(MHSA, self).__init__() + self.config = config + self.bert_config = AutoConfig.from_pretrained("bert-base-uncased") + self.bert_config.hidden_size = self.config.hidden_dim + self.embed = nn.Embedding.from_pretrained( + torch.tensor(embedding_matrix, dtype=torch.float) + ) + self.mhsa = MultiHeadSelfAttention(self.bert_config, self.config) + self.pooler = BertPooler(self.bert_config) + + self.dense = nn.Linear(self.config.hidden_dim, self.config.output_dim) + + def forward(self, inputs): + text_raw_indices = inputs[0] + x = self.embed(text_raw_indices) + out = self.mhsa(x) + out = self.pooler(out) + out = self.dense(out) + return out diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__classic__/transformer.py b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/transformer.py new file mode 100644 index 000000000..8e48355cf --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__classic__/transformer.py @@ -0,0 +1,53 @@ +# -*- coding: utf-8 -*- +# file: transformer.py +# time: 01/11/2022 12:58 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. +from torch import nn + + +class Transformer(nn.Module): + def __init__(self, embedding_matrix, config): + super(Transformer, self).__init__() + self.config = self.config + self.embedding = nn.Embedding.from_pretrained(embedding_matrix, freeze=False) + self.dropout = nn.Dropout(self.config.dropout) + self.transformer = nn.Transformer( + d_model=self.config.hidden_dim, + # nhead=self.config.num_attention_heads, + # num_encoder_layers=self.config.num_hidden_layers, + # num_decoder_layers=self.config.num_hidden_layers, + # dim_feedforward=self.config.intermediate_size, + dropout=self.config.dropout, + activation=self.config.hidden_act, + custom_encoder=None, + custom_decoder=None, + ) + self.classifier = nn.Linear(self.config.hidden_dim, self.config.output_dim) + + def forward( + self, + input_ids, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + labels=None, + ): + transformer_outputs = self.transformer( + input_ids=input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + ) + sequence_output = transformer_outputs[0] + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + logits = logits.squeeze(-1) + return logits diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__init__.py b/pyabsa/tasks/_Archive/RNARegression/models/__init__.py new file mode 100644 index 000000000..c2c73e3ff --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__init__.py @@ -0,0 +1,34 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:47 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + + +class GloVeRNARModelList(list): + from .__classic__.cnn import CNN + from .__classic__.lstm import LSTM + from .__classic__.transformer import Transformer + from .__classic__.mhsa import MHSA + + CNN = CNN + LSTM = LSTM + Transformer = Transformer + MHSA = MHSA + + def __init__(self): + super(GloVeRNARModelList, self).__init__( + [self.CNN, self.LSTM, self.Transformer, self.MHSA] + ) + + +class BERTRNARModelList(list): + from .__plm__.bert import BERT_MLP + + BERT_MLP = BERT_MLP + + def __init__(self): + super(BERTRNARModelList, self).__init__([self.BERT_MLP]) diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__plm__/__init__.py b/pyabsa/tasks/_Archive/RNARegression/models/__plm__/__init__.py new file mode 100644 index 000000000..92e79b34c --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__plm__/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:48 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/models/__plm__/bert.py b/pyabsa/tasks/_Archive/RNARegression/models/__plm__/bert.py new file mode 100644 index 000000000..f544fbb1f --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/models/__plm__/bert.py @@ -0,0 +1,46 @@ +# -*- coding: utf-8 -*- +# file: bert.py +# time: 02/11/2022 15:48 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +import torch +import torch.nn as nn +from transformers.models.bert.modeling_bert import BertPooler + + +class BERT_MLP(nn.Module): + inputs = ["text_indices", "structure"] + + def __init__(self, bert, config): + super(BERT_MLP, self).__init__() + self.config = config + self.bert = bert + self.structure_embedding = nn.Embedding(10, self.config.hidden_dim) + self.linear = nn.Linear(self.config.hidden_dim * 2, self.config.hidden_dim) + self.pooler = BertPooler(bert.config) + self.dense = nn.Linear(self.config.hidden_dim, self.config.output_dim) + self.dropout = nn.Dropout(self.config.dropout) + if self.config.sigmoid_regression: + self.sigmoid = nn.Sigmoid() + else: + self.sigmoid = None + + def forward(self, inputs): + text_raw_indices = inputs[0] + structure = inputs[1] + structure = self.structure_embedding(structure.long()) + last_hidden_state = self.bert(text_raw_indices)["last_hidden_state"] + last_hidden_state = self.linear( + torch.cat((last_hidden_state, structure), dim=-1) + ) + last_hidden_state = self.dropout(last_hidden_state) + + pooled_out = self.pooler(last_hidden_state) + out = self.dense(pooled_out) + if self.sigmoid: + out = self.sigmoid(out) + return out diff --git a/pyabsa/tasks/_Archive/RNARegression/prediction/__init__.py b/pyabsa/tasks/_Archive/RNARegression/prediction/__init__.py new file mode 100644 index 000000000..b3bfa38e4 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/prediction/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:43 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/prediction/rna_regressor.py b/pyabsa/tasks/_Archive/RNARegression/prediction/rna_regressor.py new file mode 100644 index 000000000..ab5d23841 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/prediction/rna_regressor.py @@ -0,0 +1,422 @@ +# -*- coding: utf-8 -*- +# file: rna_regressor.py +# author: YANG, HENG (杨恒) +# Copyright (C) 2020. All Rights Reserved. +import json +import os +import pickle +from typing import Union + +import numpy as np +import torch +import tqdm +from findfile import find_file, find_cwd_dir +from sklearn import metrics +from termcolor import colored +from torch.utils.data import DataLoader +from transformers import AutoModel + +from pyabsa import TaskCodeOption, LabelPaddingOption, DeviceTypeOption +from pyabsa.framework.prediction_class.predictor_template import InferenceModel +from pyabsa.framework.tokenizer_class.tokenizer_class import ( + PretrainedTokenizer, +) +from pyabsa.utils.data_utils.dataset_manager import detect_infer_dataset +from pyabsa.utils.pyabsa_utils import set_device, print_args, fprint +from ..dataset_utils.__classic__.data_utils_for_inference import GloVeRNARDataset +from ..dataset_utils.__plm__.data_utils_for_inference import BERTRNARDataset +from ..models import BERTRNARModelList, GloVeRNARModelList + + +class RNARegressor(InferenceModel): + task_code = TaskCodeOption.RNASequenceRegression + + def __init__(self, checkpoint=None, **kwargs): + """ + from_train_model: load inference model from trained model + """ + + super(RNARegressor, self).__init__(checkpoint, **kwargs) + + # load from a trainer + if self.checkpoint and not isinstance(self.checkpoint, str): + fprint("Load text classifier from trainer") + self.model = self.checkpoint[0] + self.config = self.checkpoint[1] + self.tokenizer = self.checkpoint[2] + else: + try: + if "fine-tuned" in self.checkpoint: + raise ValueError( + "Do not support to directly load a fine-tuned model, please load a .state_dict or .model instead!" + ) + fprint("Load text classifier from", self.checkpoint) + state_dict_path = find_file( + self.checkpoint, key=".state_dict", exclude_key=["__MACOSX"] + ) + model_path = find_file( + self.checkpoint, key=".model", exclude_key=["__MACOSX"] + ) + tokenizer_path = find_file( + self.checkpoint, key=".tokenizer", exclude_key=["__MACOSX"] + ) + config_path = find_file( + self.checkpoint, key=".config", exclude_key=["__MACOSX"] + ) + + fprint("config: {}".format(config_path)) + fprint("state_dict: {}".format(state_dict_path)) + fprint("model: {}".format(model_path)) + fprint("tokenizer: {}".format(tokenizer_path)) + + with open(config_path, mode="rb") as f: + self.config = pickle.load(f) + self.config.auto_device = kwargs.get("auto_device", True) + set_device(self.config, self.config.auto_device) + + if state_dict_path or model_path: + if hasattr(BERTRNARModelList, self.config.model.__name__): + if state_dict_path: + if kwargs.get("offline", False): + self.bert = AutoModel.from_pretrained( + find_cwd_dir( + self.config.pretrained_bert.split("/")[-1] + ) + ) + else: + self.bert = AutoModel.from_pretrained( + self.config.pretrained_bert + ) + self.model = self.config.model(self.bert, self.config) + self.model.load_state_dict( + torch.load( + state_dict_path, map_location=DeviceTypeOption.CPU + ), + strict=False, + ) + elif model_path: + self.model = torch.load( + model_path, map_location=DeviceTypeOption.CPU + ) + + try: + self.tokenizer = PretrainedTokenizer( + config=self.config, **kwargs + ) + except ValueError: + if tokenizer_path: + with open(tokenizer_path, mode="rb") as f: + self.tokenizer = pickle.load(f) + else: + self.embedding_matrix = self.config.embedding_matrix + self.tokenizer = self.config.tokenizer + if model_path: + self.model = torch.load( + model_path, map_location=DeviceTypeOption.CPU + ) + else: + self.model = self.config.model( + self.embedding_matrix, self.config + ).to(self.config.device) + self.model.load_state_dict( + torch.load( + state_dict_path, map_location=DeviceTypeOption.CPU + ), + strict=False, + ) + + self.tokenizer = self.config.tokenizer + + if kwargs.get("verbose", False): + fprint("Config used in Training:") + print_args(self.config) + + except Exception as e: + raise RuntimeError( + "Exception: {} Fail to load the model from {}! ".format( + e, self.checkpoint + ) + ) + + if not hasattr( + GloVeRNARModelList, self.config.model.__name__ + ) and not hasattr(BERTRNARModelList, self.config.model.__name__): + raise KeyError( + "The checkpoint you are loading is not from classifier model." + ) + + if hasattr(BERTRNARModelList, self.config.model.__name__): + self.dataset = BERTRNARDataset(config=self.config, tokenizer=self.tokenizer) + + elif hasattr(GloVeRNARModelList, self.config.model.__name__): + self.dataset = GloVeRNARDataset( + config=self.config, tokenizer=self.tokenizer + ) + + self.__post_init__(**kwargs) + + def _log_write_args(self): + n_trainable_params, n_nontrainable_params = 0, 0 + for p in self.model.parameters(): + n_params = torch.prod(torch.tensor(p.shape)) + if p.requires_grad: + n_trainable_params += n_params + else: + n_nontrainable_params += n_params + fprint( + "n_trainable_params: {0}, n_nontrainable_params: {1}".format( + n_trainable_params, n_nontrainable_params + ) + ) + for arg in vars(self.config): + if getattr(self.config, arg) is not None: + fprint(">>> {0}: {1}".format(arg, getattr(self.config, arg))) + + def batch_predict( + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs + ): + """ + Predict from a file of sentences. + param: target_file: the file path of the sentences to be predicted. + param: print_result: whether to print the result. + param: save_result: whether to save the result. + param: ignore_error: whether to ignore the error when predicting. + param: kwargs: other parameters. + """ + self.config.eval_batch_size = kwargs.get("eval_batch_size", 32) + + save_path = os.path.join( + os.getcwd(), + "{}.{}.result.json".format( + self.config.task_name, self.config.model.__name__ + ), + ) + + target_file = detect_infer_dataset( + target_file, task_code=TaskCodeOption.RNASequenceRegression + ) + if not target_file: + raise FileNotFoundError("Can not find inference datasets!") + + self.dataset.prepare_infer_dataset(target_file, ignore_error=ignore_error) + self.infer_dataloader = DataLoader( + dataset=self.dataset, + batch_size=self.config.eval_batch_size, + pin_memory=True, + shuffle=False, + ) + return self._run_prediction( + save_path=save_path if save_result else None, print_result=print_result + ) + + def predict( + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs + ): + """ + Predict from a sentence or a list of sentences. + param: text: the sentence or a list of sentence to be predicted. + param: print_result: whether to print the result. + param: ignore_error: whether to ignore the error when predicting. + param: kwargs: other parameters. + """ + self.config.eval_batch_size = kwargs.get("eval_batch_size", 32) + self.infer_dataloader = DataLoader( + dataset=self.dataset, batch_size=self.config.eval_batch_size, shuffle=False + ) + if text: + self.dataset.prepare_infer_sample(text, ignore_error=ignore_error) + else: + raise RuntimeError("Please specify your datasets path!") + if isinstance(text, str): + return self._run_prediction(print_result=print_result)[0] + else: + return self._run_prediction(print_result=print_result) + + def _run_prediction(self, save_path=None, print_result=True): + _params = filter(lambda p: p.requires_grad, self.model.parameters()) + + correct = {True: "Correct", False: "Wrong"} + results = [] + perplexity = "N.A." + with torch.no_grad(): + self.model.eval() + n_total = 0 + t_targets_all, t_outputs_all = None, None + + if len(self.infer_dataloader.dataset) >= 100: + it = tqdm.tqdm(self.infer_dataloader, desc="run inference") + else: + it = self.infer_dataloader + + pre_ex_id = 0 + sum_val = [] + cat_text = "" + for i_batch, sample in enumerate(it): + inputs = [ + sample[col].to(self.config.device) + for col in self.config.inputs_cols + if col != "label" + ] + + outputs = self.model(inputs) + sen_logits = outputs + + for i, i_probs in enumerate(sen_logits): + pred_val = float(i_probs) + real_val = float(sample["label"][i]) + + text_raw = sample["text_raw"][i] + ex_id = int(sample["ex_id"][i]) + if self.cal_perplexity: + ids = self.MLM_tokenizer( + text_raw, + truncation=True, + padding="max_length", + max_length=self.config.max_seq_len, + return_tensors="pt", + ) + ids["labels"] = ids["input_ids"].clone() + ids = ids.to(self.config.device) + loss = self.MLM(**ids)["loss"] + perplexity = float(torch.exp(loss / ids["input_ids"].size(1))) + else: + perplexity = "N.A." + + if ex_id == pre_ex_id: + sum_val.append(pred_val) + cat_text += text_raw + elif len(it) != 1: + results.append( + { + "ex_id": pre_ex_id, + "text": cat_text, + "label": np.median(sum_val), + "ref_label": real_val, + "perplexity": perplexity, + } + ) + n_total += 1 + pre_ex_id = ex_id + sum_val = [pred_val] + cat_text = text_raw + + t_targets_all = ( + torch.cat( + (t_targets_all, torch.tensor([sample["label"][i]])) + ) + if t_targets_all is not None + else torch.tensor([sample["label"][i]]) + ) + t_outputs_all = ( + torch.cat( + (t_outputs_all, torch.tensor([np.median(sum_val)])) + ) + if t_outputs_all is not None + else torch.tensor([np.median(sum_val)]) + ) + + results.append( + { + "ex_id": pre_ex_id, + "text": cat_text, + "label": np.median(sum_val), + "ref_label": real_val, + "perplexity": perplexity, + } + ) + pre_ex_id = ex_id + sum_val = [pred_val] + cat_text = text_raw + n_total += 1 + t_targets_all = ( + torch.cat((t_targets_all, torch.tensor([sample["label"][i]]))) + if t_targets_all is not None + else torch.tensor([sample["label"][i]]) + ) + t_outputs_all = ( + torch.cat((t_outputs_all, torch.tensor([np.median(sum_val)]))) + if t_outputs_all is not None + else torch.tensor([np.median(sum_val)]) + ) + + try: + if print_result: + for ex_id, result in enumerate(results): + text_printing = result["text"][:] + if result["ref_label"] != LabelPaddingOption.LABEL_PADDING: + if ( + abs(result["label"] - result["ref_label"]) + / result["ref_label"] + <= 0.2 + ): + text_info = colored( + "#{}\t -> <{}(ref:{})>\t".format( + result["ex_id"], + result["label"], + result["ref_label"], + ), + "green", + ) + else: + text_info = colored( + "#{}\t -> <{}(ref:{})>\t".format( + result["ex_id"], + result["label"], + result["ref_label"], + ), + "red", + ) + else: + text_info = "#{}\t -> {}\t".format( + result["ex_id"], result["label"] + ) + if self.cal_perplexity: + text_printing += colored( + " --> \t".format(result["perplexity"]), + "yellow", + ) + text_printing = text_info + text_printing + + fprint("Example :{} ".format(text_printing)) + if save_path: + with open(save_path, "w", encoding="utf8") as fout: + json.dump(str(results), fout, ensure_ascii=False) + fprint("inference result saved in: {}".format(save_path)) + except Exception as e: + fprint("Can not save result: {}, Exception: {}".format(text_raw, e)) + + if len(results) > 1: + fprint( + "\n---------------------------- Regression Result ----------------------------\n" + ) + fprint( + "MSE: {}".format( + metrics.mean_squared_error(t_targets_all.cpu(), t_outputs_all.cpu()) + ) + ) + fprint( + "R2: {}".format( + metrics.r2_score(t_targets_all.cpu(), t_outputs_all.cpu()) + ) + ) + fprint( + "\n---------------------------- Regression Result ----------------------------\n" + ) + + return results + + def clear_input_samples(self): + self.dataset.all_data = [] + + +class Predictor(RNARegressor): + pass diff --git a/pyabsa/tasks/_Archive/RNARegression/trainer/__init__.py b/pyabsa/tasks/_Archive/RNARegression/trainer/__init__.py new file mode 100644 index 000000000..b3bfa38e4 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/trainer/__init__.py @@ -0,0 +1,8 @@ +# -*- coding: utf-8 -*- +# file: __init__.py +# time: 02/11/2022 15:43 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. diff --git a/pyabsa/tasks/_Archive/RNARegression/trainer/rnar_trainer.py b/pyabsa/tasks/_Archive/RNARegression/trainer/rnar_trainer.py new file mode 100644 index 000000000..43a2ea761 --- /dev/null +++ b/pyabsa/tasks/_Archive/RNARegression/trainer/rnar_trainer.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +# file: rnar_trainer.py +# time: 02/11/2022 21:34 +# author: YANG, HENG (杨恒) +# github: https://github.com/yangheng95 +# GScholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en +# ResearchGate: https://www.researchgate.net/profile/Heng-Yang-17/research +# Copyright (C) 2022. All Rights Reserved. + +from typing import Union + +from pyabsa.framework.flag_class.flag_template import ( + DeviceTypeOption, + ModelSaveOption, + TaskCodeOption, + TaskNameOption, +) +from pyabsa.framework.trainer_class.trainer_template import Trainer +from ..configuration.rnar_configuration import RNARConfigManager +from ..prediction.rna_regressor import RNARegressor +from ..instructor.rnar_instructor import RNARTrainingInstructor + + +class RNARTrainer(Trainer): + def __init__( + self, + config: RNARConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, + ): + """ + Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, + you need to call load_trained_model() to get the trained model for inference. + + :param config: PyABSA.config.ConfigManager + :param dataset: Dataset name, or a dataset_manager path, or a list of dataset_manager paths + :param from_checkpoint: A checkpoint path to train based on + :param checkpoint_save_mode: Save trained model to checkpoint, + "checkpoint_save_mode=1" to save the state_dict, + "checkpoint_save_mode=2" to save the whole model, + "checkpoint_save_mode=3" to save the fine-tuned BERT, + otherwise avoid saving checkpoint but return the trained model after trainer + :param auto_device: True or False, otherwise 'allcuda', 'cuda:1', 'cpu' works + :param path_to_save=None: Specify path to save checkpoints + :param load_aug=False: Load the available augmentation dataset if any + + """ + super(RNARTrainer, self).__init__( + config=config, + dataset=dataset, + from_checkpoint=from_checkpoint, + checkpoint_save_mode=checkpoint_save_mode, + auto_device=auto_device, + path_to_save=path_to_save, + load_aug=load_aug, + ) + + self.training_instructor = RNARTrainingInstructor + self.inference_model_class = RNARegressor + self.config.task_code = TaskCodeOption.RNASequenceRegression + self.config.task_name = TaskNameOption().get( + TaskCodeOption.RNASequenceRegression + ) + + self._run() diff --git a/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_inference.py b/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_inference.py index 0f36f2e68..b76765008 100644 --- a/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_inference.py +++ b/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_inference.py @@ -180,8 +180,8 @@ def process_data(self, samples, ignore_error=True): lcfs_vec = prepared_inputs["lcfs_vec"] if ( - self.config.model_name == "dlcf_dca_bert" - or self.config.model_name == "dlcfs_dca_bert" + self.config.model_name == "dlcf_dca_bert" + or self.config.model_name == "dlcfs_dca_bert" ): configure_dlcf_spacy_model(self.config) prepared_inputs = prepare_input_for_dlcf_dca( diff --git a/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_training.py b/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_training.py index 7ba93d924..d15c989c7 100644 --- a/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_training.py +++ b/pyabsa/tasks/__SubtaskTemplate__/dataset_utils/data_utils_for_training.py @@ -86,8 +86,8 @@ def load_data_from_file(self, file_path, **kwargs): continue if ( - self.config.model_name == "dlcf_dca_bert" - or self.config.model_name == "dlcfs_dca_bert" + self.config.model_name == "dlcf_dca_bert" + or self.config.model_name == "dlcfs_dca_bert" ): configure_dlcf_spacy_model(self.config) prepared_inputs = prepare_input_for_dlcf_dca( diff --git a/pyabsa/tasks/__SubtaskTemplate__/prediction/predictor.py b/pyabsa/tasks/__SubtaskTemplate__/prediction/predictor.py index e2f07eb76..7f7048be0 100644 --- a/pyabsa/tasks/__SubtaskTemplate__/prediction/predictor.py +++ b/pyabsa/tasks/__SubtaskTemplate__/prediction/predictor.py @@ -19,12 +19,12 @@ def __init__(self, checkpoint=None, cal_perplexity=False, **kwargs): self.__post_init__(**kwargs) def batch_infer( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ A deprecated version of batch_predict method. @@ -63,12 +63,12 @@ def infer(self, text: str = None, print_result=True, ignore_error=True, **kwargs ) def batch_predict( - self, - target_file=None, - print_result=True, - save_result=False, - ignore_error=True, - **kwargs + self, + target_file=None, + print_result=True, + save_result=False, + ignore_error=True, + **kwargs ): """ Predict the sentiment from a file of sentences. @@ -81,11 +81,11 @@ def batch_predict( raise NotImplementedError("Please implement this method in your subtask class!") def predict( - self, - text: Union[str, list] = None, - print_result=True, - ignore_error=True, - **kwargs + self, + text: Union[str, list] = None, + print_result=True, + ignore_error=True, + **kwargs ): """ Predict the sentiment from a sentence or a list of sentences. @@ -102,5 +102,6 @@ def _run_prediction(self, save_path=None, print_result=True, **kwargs): def clear_input_samples(self): self.dataset.all_data = [] + # class Predictor(AliasedClassifier): # pass diff --git a/pyabsa/tasks/__SubtaskTemplate__/trainer/trainer.py b/pyabsa/tasks/__SubtaskTemplate__/trainer/trainer.py index c7d7aaf5f..c52407ca2 100644 --- a/pyabsa/tasks/__SubtaskTemplate__/trainer/trainer.py +++ b/pyabsa/tasks/__SubtaskTemplate__/trainer/trainer.py @@ -23,14 +23,14 @@ class APCTrainer(Trainer): def __init__( - self, - config: APCConfigManager = None, - dataset=None, - from_checkpoint: str = None, - checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, - auto_device: Union[bool, str] = DeviceTypeOption.AUTO, - path_to_save=None, - load_aug=False, + self, + config: APCConfigManager = None, + dataset=None, + from_checkpoint: str = None, + checkpoint_save_mode: int = ModelSaveOption.SAVE_MODEL_STATE_DICT, + auto_device: Union[bool, str] = DeviceTypeOption.AUTO, + path_to_save=None, + load_aug=False, ): """ Init a trainer for trainer a APC, ATEPC, TC or TAD model, after trainer, diff --git a/pyabsa/utils/absa_utils/absa_utils.py b/pyabsa/utils/absa_utils/absa_utils.py index 84e4ab843..9929dc19a 100644 --- a/pyabsa/utils/absa_utils/absa_utils.py +++ b/pyabsa/utils/absa_utils/absa_utils.py @@ -11,7 +11,7 @@ import findfile -from pyabsa import LabelPaddingOption, TaskCodeOption +import pyabsa from pyabsa.tasks.AspectTermExtraction.dataset_utils.__lcf__.atepc_utils import ( simple_split_text, ) @@ -38,7 +38,7 @@ def generate_inference_set_for_apc(dataset_path): [ "dataset", "train", - TaskCodeOption.Aspect_Polarity_Classification, + pyabsa.TaskCodeOption.Aspect_Polarity_Classification, dataset_name, ], exclude_key=[".inference", "readme"], @@ -47,7 +47,7 @@ def generate_inference_set_for_apc(dataset_path): [ "dataset", "valid", - TaskCodeOption.Aspect_Polarity_Classification, + pyabsa.TaskCodeOption.Aspect_Polarity_Classification, dataset_name, ], exclude_key=[".inference", "readme"], @@ -56,7 +56,7 @@ def generate_inference_set_for_apc(dataset_path): [ "dataset", "test", - TaskCodeOption.Aspect_Polarity_Classification, + pyabsa.TaskCodeOption.Aspect_Polarity_Classification, dataset_name, ], exclude_key=[".inference", "readme"], @@ -170,7 +170,7 @@ def assemble_aspects(fname, use_tokenizer=False): # Group sentences with similar aspects and generate samples with the corresponding aspect labels and polarities def unify_same_samples(same_samples): text = same_samples[0][0].replace("$T$", same_samples[0][1]) - polarities = [LabelPaddingOption.SENTIMENT_PADDING] * len(text.split()) + polarities = [pyabsa.LabelPaddingOption.SENTIMENT_PADDING] * len(text.split()) tags = ["O"] * len(text.split()) samples = [] for sample in same_samples: @@ -288,12 +288,12 @@ def convert_apc_set_to_atepc_set(path, use_tokenizer=False): elif os.path.exists(path): files = findfile.find_files( path, - ["dataset", TaskCodeOption.Aspect_Polarity_Classification], + ["dataset", pyabsa.TaskCodeOption.Aspect_Polarity_Classification], exclude_key=[".inference", "readme"], ) else: files = findfile.find_cwd_files( - [path, "dataset", TaskCodeOption.Aspect_Polarity_Classification], + [path, "dataset", pyabsa.TaskCodeOption.Aspect_Polarity_Classification], exclude_key=[".inference", "readme"], ) diff --git a/pyabsa/utils/absa_utils/make_absa_dataset.py b/pyabsa/utils/absa_utils/make_absa_dataset.py index dfbfc3a2e..fd85722c9 100644 --- a/pyabsa/utils/absa_utils/make_absa_dataset.py +++ b/pyabsa/utils/absa_utils/make_absa_dataset.py @@ -10,11 +10,8 @@ import os import findfile - from termcolor import colored -from pyabsa import LabelPaddingOption - from pyabsa.tasks.AspectTermExtraction.prediction.aspect_extractor import ( AspectExtractor, ) @@ -36,6 +33,7 @@ def make_ABSA_dataset(dataset_name_or_path, checkpoint="english"): :param checkpoint: Which checkpoint to use. Basically, You can select from {'multilingual', 'english', 'chinese'}, Default is 'english'. :return: """ + from pyabsa import LabelPaddingOption if os.path.isdir(dataset_name_or_path): fs = findfile.find_files( diff --git a/pyabsa/utils/ensemble_prediction/ensemble_prediction.py b/pyabsa/utils/ensemble_prediction/ensemble_prediction.py index 7348152ac..af2ca3699 100644 --- a/pyabsa/utils/ensemble_prediction/ensemble_prediction.py +++ b/pyabsa/utils/ensemble_prediction/ensemble_prediction.py @@ -9,6 +9,7 @@ from typing import List import numpy as np + from pyabsa.tasks.AspectPolarityClassification import SentimentClassifier @@ -39,7 +40,7 @@ def __init__( assert len(predictors) > 0, "Checkpoints should not be empty" - numeric_agg_methods = { + self.numeric_agg_methods = { "average": np.mean, "mean": np.mean, "max": np.max, @@ -48,21 +49,21 @@ def __init__( "mode": lambda x: max(set(x), key=x.count), "sum": np.sum, } - str_agg_methods = { + self.str_agg_methods = { "max_vote": lambda x: max(set(x), key=x.count), "min_vote": lambda x: min(set(x), key=x.count), "vote": lambda x: max(set(x), key=x.count), "mode": lambda x: max(set(x), key=x.count), } assert ( - numeric_agg in numeric_agg_methods - ), "numeric_agg should be either: " + str(numeric_agg_methods.keys()) - assert str_agg in str_agg_methods, "str_agg should be either max or vote" + str( - str_agg_methods.keys() - ) + numeric_agg in self.numeric_agg_methods + ), "numeric_agg should be either: " + str(self.numeric_agg_methods.keys()) + assert ( + str_agg in self.str_agg_methods + ), "str_agg should be either max or vote" + str(self.str_agg_methods.keys()) - self.numeric_agg = numeric_agg_methods[numeric_agg] - self.str_agg = str_agg_methods[str_agg] + self.numeric_agg_func = numeric_agg + self.str_agg = self.str_agg_methods[str_agg] if isinstance(predictors, dict): self.checkpoints = list(predictors.keys()) @@ -75,6 +76,16 @@ def __init__( "Only support dict type for checkpoints and weights" ) + def numeric_agg(self, result: list): + """ + Aggregate a list of numeric values. + + :param result: a list of numeric values + :return: the aggregated value + """ + res = np.stack([np.array(x) for x in result]) + return self.numeric_agg_methods[self.numeric_agg_func](res, axis=0) + def __ensemble(self, result: dict): """ Aggregate prediction results by calling the appropriate aggregation method. diff --git a/release_note.json b/release_note.json index c1c5e4c02..aeb7452d8 100644 --- a/release_note.json +++ b/release_note.json @@ -1,4 +1,10 @@ { + "2.4.1": { + "1": "Minor Bug fixes" + }, + "2.4.0": { + "1": "Bug fixes" + }, "2.1.10": { "1": "Improve Instruction-based Aspect Category Opinion Sentiment Extraction (ACOS) task, see the demo at https://huggingface.co/spaces/yangheng/PyABSA" },